NIST Special Publication 973

Eleventh International Conference on

Computer Technology in Welding

T.A. Siewert and C. Pollock, Editors

NAT'L INST. OF STAND & jEpH

NiST

QC

.U57 nstitute of Standards and Technology

no. 973 Administration, U.S. Department of Commerce

2002 c.JL
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'At Boulder, CO 80303. "Some elements at Boulder, CO.
NIST Special Publication 973

Eleventh International Conference

on Computer Technology in Welding

T. A. Siewert

and

C. Pollock,

Editors

Materials Reliability Division

Materials Science and Engineering Laboratory

325 Broadway

Boulder, Colorado 80305

January 2002

U.S. Department of Commerce

Donald L. Evans, Secretary

Technology Administration

Phillip J. Bond, Under Secretaryfor Technology

National Institute of Standards and Technology

ArdenL. Bement, Jr., Director
Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such

identification is not intended to imply recommendation or endorsement by the

National Institute of Standards and Technology, nor is it intended to imply that the

entities, materials, or equipment are necessarily the best available for the purpose.

National Institute of Standards and Technology Special Publication 973

Natl. Inst. Stand. Technol. Spec. Publ. 973, 319 pages (January 2002)

CODEN: NSPUE2

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CONTENTS

TRACK A: SENSING

Chair: Dr. J. E. Jones, N. A. Technologies, Golden, Colorado

Session Al: Sensing and Control I: GMAW

Al-1 The Experimental Studies on the Process-Integrated Quality Assurance in

Pulsed Current GMAW ofAluminum

Dr. S. Rajasekaran, Tamilnadu, India 3

Al-2 Droplet Oscillation and Weld Pool Imaging Using Computer-Controlled

Composite Pulse Current

Dr. B. Zheng, EWI, Columbus, Ohio 21

Al-3 Optimization of PGMAW Using Online Observation and Statistical Data

S. Nordbruch, University of Bremen, Germany 31

Al-4 Through the Arc Tracking of 5G Narrow Gap Pipe Welds

T. S. Rajagopalan, CRC-Evans Automatic Welding, Houston, Texas 39

Al-5 Development of Rotating GMA Welding System and its Application to

Arc Sensors

C. H. Kim, Korean Adv. Inst, of Science and Tech., Taejeon, Korea 47

Keynote

"Lights Out" Welding: Progress towards Complete Welding Automation

T. P. Quinn, Past Chairman, AWS A9 Committee on Computerization,

NIST, Boulder, Colorado 57

Chair: Dr. D. M. Barborak, WeldWare Inc., Columbus, Ohio

Session A2: Sensing and Control II: GMAW and GTAW

A2-1 A System for Real-Time Control of Gas Metal Arc Weld Profile

Dr. D. M. Barborak, WeldWare Inc., Columbus, Ohio 71

A2-2 Weld Penetration Control During GTA Welding Using Weld Pool

Oscillation Sensing

Dr. M. J. M. Hermans, Delft University of Technology, The Netherlands 79

A2-3 Real-Time Molten Pool Area Extraction for Control of Gas Tungsten

Arc Welding

Prof R. Kovacevic, SMU, Richardson, Texas 87

Chair: W. Rippey, NIST, Gaithersburg, Maryland

Session A3: Sensing and Control III: VPPAW

A3- 1 Monitoring Stability of Keyhole Formation in VPPAW

Prof R. Kovacevic, SMU, Richardson, Texas 97

A3-2 Systems Technology for Variable Polarity Plasma Arc Welding of

Aluminum Alloys

Dr. B. Zheng, EWI, Columbus, Ohio 105

iii
Chair: Dr. A. C. Hall, Sandia National Laboratories, Albuquerque, New Mexico

Session A4: Imaging

A4- 1 Characterizing the Microstructure ofA GTA Weld hi-Process Using

High-Speed, High Magnification, Digital Imaging

Dr. A. C. Hall, Sandia National Laboratories, Albuquerque, New Mexico 117

A4-2 Vision-based Weld Defect Measurement and Depiction

C. Reichert, EWI, Columbus, Ohio 125

A4-3 Computer Micro-image Analysis in the Heat Affected Zone of HQ130

High Strength Steel

Dr. Y. Li, Shandong University, Peoples Republic of China 137

Chair: Dr. T. P. Quinn, NIST, Boulder, Colorado

Session A5: Fabrication Quality and Standards

A5-1 Virtual and Augmented Reality for Quality Improvement of Manual Welds

P. Tschimer, University Bremen, Germany 147

A5-2 A9 Mission, Current Activities, and What Should We Do Next

W. Rippey, NIST, Gaithersburg, Maryland 155

Chair: Dr. K. Matsuyama, MIT, Cambridge, Massachusetts

Session A6: Sensing and Control Systems IV

A6-1 On-Line Weld Quality Diagnostics in Friction Stir Welding

Prof R. Kovacevic, SMU, Richardson, Texas 173

A6-2 A Real-Time Monitoring and Control System for Resistance Spot Welding

Dr. K. Matsuyama, Massachusetts Institute of Technology (MIT),

Cambridge, Massachusetts 183

TRACK B: MODELING

Chair: Dr. R. D. Everhart, EWI, Columbus, Ohio

Session Bl: Modeling I: Distortion and Stress

B 1 - 1 An Integrated FEA Based Procedure for Weld Fixture Design

Dr. Z. Yang, Caterpillar Inc., Peoria, Illinois 203

B 1 -2. Welding Induced Distortions Modeling of Large Plate Structures

Dr. R. D. Everhart, EWI, Columbus, Ohio 211

Bl-3 Computational Modeling of Residual Stress in Welds

Dr. K. Koppenhoefer, EWI, Columbus, Ohio 223

Bl-4 A Study on the Prediction of Welding Distortions of the Hull in Shipbuilding

S. B. Shin, Hyundai Heavy Ind., Co., Korea 233

Bl-5 Numerical Simulation of Shear Stress Distribution in the Fillet of an AI-AI2O3

Soldering Assembly

Dr. X. Ma, National Key Laboratory of Reliability Physics of

Electronic Product, Peoples Republic of China 241

iv
Chair: Dr. M. V. Li, EWI, Columbus, Ohio

Session B2: Modeling II: Predicting Microstructure and Performance

B2-1 Advances in Aluminium Weld Simulations Applying WELDSIM

H. G. Fjser, Institute for Energy Technology, Norway 251

B2-2 A Computational Model for Heat Affected Zone of Reactor Pressure

Vessel Steel

Dr. J.-S. Kim, Korea Power Engineering Co., Korea 265

B2-3 Novel Computer-aided Method of Welding Materials Design

V. Mazurovsky, CWC, Ltd, Ariel, Israel 273

B2-4 Finite Element Analysis of Creep Strength Mis-matched Dissimilar

Welded Joints

H. Chen, Southwest Jiaotong University, Chengdu, China 281

Chair: Dr. D. F. Farson, The Ohio State University, Columbus, Ohio

Session B3: Modeling III

B3-1 Laser Weld Keyhole Dynamics: Simulation and Experiment

Dr. D. F. Farson, OSU, Columbus, Ohio 289

B3-2 Finite Element Modeling on Welding Processes and Welding Phenomena

Dr. B. Palotas, Budapest University of Technology and

Economics, Hungary 297

Chair: Dr. M. Williams, Los Alamos National Laboratory, Los Alamos, New Mexico

Session B4: Modeling IV

B4-1 Three-Dimensional Simulation of Robot Path and Heat Transfer of a TIG-Welded

Part with Complex Geometry

M. Ericsson, University of Trollhattan/Uddevalla, Sweden 309

B4-2 Dynamic Modeling of GTAW for Rapid Manufacturing

Prof R. Kovacevic, SMU, Richardson, Texas 317

B4-3 Three Dimensional Transient Heat Transfer Analysis For GTA Welding Process

M. Jou, National Taiwan Normal University, Taipei City, Taiwan 327

V

Preface

These are the proceedings of the eleventh conference in this series. The conference was held

December 5 and 6, 2001, in Columbus, Ohio, under the sponsorship of the American Welding

Society, NIST, and The Welding Institute. These proceedings include 32 presentations grouped into

sessions on: sensing and control of GMAW, sensing and control of GTAW, sensing and control of

VPPAW, imaging, fabrication quality and standards, sensing and control systems, modeling of

distortion and stress, predicting microstructure and performance, and general modeling topics. The

large number of submissions required the papers to be divided into two parallel tracks: a sensing and

control track, and a modeling track. In addition, there was a keynote talk on our progress toward total

automation. These proceedings include all the manuscripts that were submitted (including

viewgraphs). Additional copies are available fromNTIS (National Technical Information Service) or GPO (Government Printing Office) as NIST Special Publication 973 (See below).

The first U.S. workshop on computerization ofwelding data was held in 1986, under the sponsorship

ofthe American Welding Institute (AWI) and the then National Bureau of Standards (now. National

Institute of Standards and Technology, NIST). The workshop produced a list of national needs in

welding data, a list designed to guide database developers. The proceedings ofthat first workshop are available from NTIS as NIST Special Publication 742.

There had been sufficient advances in database activity by 1 988 to justify a second meeting, this time

as a joint workshop-conference under the sponsorship of AWI, NIST, and the American Welding

Society (AWS). The scope was expanded to include a conference on the latest developments and a

preconference tutorial to provide novices with a background in common cortputer applications. The

conference was attended by 61 managers, welding engineers, and computer professionals. The

proceedings of the workshop and conference are available from NTIS as NIST Special Publication

781.

By 1 990, the welding database activity had grown large enough to justify a third conference on this

topic, again sponsored by AWI, AWS, and NIST. It consisted of a series of tutorials, a keynote

presentation, and technical sessions on the topics: off-line planning, real-time welding information,

data systems and standards, and industrial applications. The conference included demonstrations of

welding software, and was followed by a meeting of the AWS Committee on Computerization of

Welding Data.

The fourth conference, held during November 1992 in Orlando, Florida, was truly an international

conference; speakers representing 10 countries presented papers on the topics of standards,

applications, quality and NDE, sensing, control, and databases. Once again, a preconference tutorial

was organized and taught by AWI personnel covering PC networks, expert systems, neural networks,

Windows and the Excel Spreadsheet, and databases. The conference also included a keynote

presentation, tabletop exhibits, and hands-on demonstrations of welding software. The AWS

Committee on Computerization of Welding Data met following the Conference. These proceedings

are available from AWS (Code: CP-1 192).

vii

J
The fifth conference, held August 1994 in Golden, Colorado, continued the trend of growth in size

and scope. It consisted of 3 1 papers (by 69 authors) on the topics ofquality control, off-line planning

and simulation, commercial software systems, control and automation, welding optimization, data

acquisition and sensors, application case studies, welder and procedure qualification systems, weld

prediction and control, and large-scale systems. This year the preconference tutorials were on the

topics of computing platforms, sensing and data acquisition, and line planning for welding

automation. These tutorials were taught by experts from ABB Robotics, AMET, CSM, Ford

Research Laboratory, and NIST. The conference was cosponsored by AWI, AWS, the Colorado

School of Mines, and NIST. The conference proceedings are available from AWS (code: CP-794).

The sixth conference was held June 9-12, 1996, in the "Heart of Europe," where Belgium, Germany,

and the Netherlands converge in the Limberg region. The Technical Program was aimed to embrace

all known facets of computing practice as applied to welded fabrication and manufacturing. Expert

advice was provided in process selection, consumable selection, and welding procedure generation

and interpretation of standards. Proceedings are available through Woodhead Publishing Ltd. (see

below).

The seventh conference was held July 8-12,1 997, in San Francisco, California, and was presented in

conjunction with the American Welding Society, the National Institute of Standards and Technology,

and The Welding Institute. Attendees were able to observe how computers can be used for more

than just databases. In addition to tutorials, the following were covered: case studies, controls and

controllers, sensing, process automation, modeling heat and fluid flow, modeling thermomechanical

effects, and modeling residual stress or mechanical effects. Also covered were the Internet and

networked systems in the welding industry. Additional copies ofthe proceedings are available from

NTIS as NIST Special Publication 923.

The eighth conference was held June 22 to 24, 1998, in Liverpool, U.K., and was sponsored by The

Welding Institute. The significant advances in technologies such as expert systems, neural networks,

and the internet, were highlighted in tutorial sessions. The 35 papers were grouped into sessions on:

sensors/vision, modeling and control, modeling of process and joint properties, modeling and

simulation, education and training, and quality control and quality assurance. Proceedings are

available through Woodhead Publishing Ltd.

The ninth conference was held September 28 to 30, 1999, in Detroit, Michigan, under the

sponsorship of the American Welding Society, NIST, and The Welding Institute. There were 57

papers grouped into sessions on resistance weld simulation, simulation of gas metal arc welding,

modeling of weld shape and distortion, modeling of solidification, general modeling, welding

documentation, sensing and control of arc quality, droplet control and process automation,

automation communication and interfaces, and documentation and database applications. The large

number of papers required the papers to be divided into two parallel tracks: a modeling track, and a

sensing and control track. In addition, there were tutorials on weld cell communication issues and on

web page design. The printed proceedings were supplemented with a CDROM disk, which included

electronic versions of most of the papers and some electronic files of the presentations. Additional

copies are available from NTIS or GPO as NIST Special Publication 949.

viii
The tenth conference was held June 6 and 7, 2000, in Copenhagen, Denmark, under the leadership of

The Welding Institute and the Force Institute. The papers were divided into sessions on sensor and

vision systems, equipment control, manufacturing and production, education and training, and

modeling and simulation. The proceedings are available in both paper and electronic forms from

Woodhead Publishing Ltd.

To order the NIST Special Publications, contact:

Superintendent of Documents or National Technical Information Service

Government Printing Office Springfield, VA 22161

Washington, DC 20402 (703) 487-4650

(202) 5 12-1 800 http://www.ntis.gov/

http://www.access.gpo.gov/

To order the 1992 and 1994 proceedings, contact:

Order Department

American Welding Society

550 N.W. LeJeune Road

Miami, Florida 33126

(800) 334-9353 (or (305) 443-9353, Ext. 280)

To order the 1996, 1998, or 2000 proceedings, contact:

Woodhead Publishing Ltd.

Abington Hall, Abington

Cambridge CBl 6AH

England

+44(0)1223 891358

Except where attributed to NIST authors, the content of individual sections of this volume has neither been

reviewed nor edited by the National Institute of Standards and Technology. NIST therefore accepts no

responsibility for comments or recommendations therein. The mention of trade names in this volume neither

constitutes nor implies any endorsement or recommendation by the National Institute of Standards and

Technology.

Acknowledgment: The editors wish to express their appreciation to those who helped run the

conference, chair the sessions, and prepare these proceedings, especially to Gladys Santana of the

American Welding Society and Vonnie Ciaranello of NIST.

ix
I

!

i

I
Session Al: Sensing and Control I: GMAW

1

THE EXPERIMENTAL STUDIES ON THE PROCESS-INTEGRATED QUALITY

ASSURANCE IN PULSED CURRENT GMA WELDING OF ALUMINUM

Dr. S. Rajasekaran

ABSTRACT

Al-Mg alloys are widely used in the production of new breed lightweight cars. The pulsed

current gas metal arc welding (GMAW-P) is widely used to weld these alloys. However, quality

of the weld mainly depends upon the parameters of the pulsed current such as peak current (Ip),

peak duration (Tp), background current (Ib), background duration (Tb), welding speed (Ws), and

wire feed speed (Wf). Selecting the most suitable GMAW-P parameter is very complex due to

interdependence of parameters.

Therefore effects of the GMAW-P parameters on different types of droplet detachments, namely

one droplet detachment during peak duration, one droplet detachment during background

duration, two droplets detachment during peak duration, and three droplets detachment during

peak duration have been studied. The best type of droplet detachment for GMAW-P has been

selected on the basis of process-integrated quality assurance. This process-integrated quality

assurance enables online monitoring of welding voltage, welding current as well as the

reproduction of welding data and complete documentation of all parameters and welding

machine adjustments. Therefore, this method was used to select the suitable pulse parameter to

welding.

The 1.2mm diameter of ER 5356 filler metal and 6mm thick AA 5083 Al-Mg alloy base metal

were used throughout welding experiments. Electrical signal especially pulsed current waveform

as time function, contains essential information about physical phenomena in arc. Therefore, the

average current per pulse cycle time was measured from the pulsed current waveforms using

AutoCAD 2000 and a self-developed computer software EL-JS. The statistical analysis was

carried out to determine uniformity of average current. Results show that one droplet detachment

during peak duration can provide stable weld compared to other types of droplet detachments.

This experimental study helped to achieve high-quality cost-effective welds with high production

rate. KEY WORDS

Pulsed Current GMAW, Pulse Parameters, Waveform, Process-Integrated Quality Assurance,

Al-Mg Alloys Welding, Droplet Detachment, Data Analysis, Computer Data, Arc Sensing.

At the time of this work, Dr. S. Rajasekaran was Research Scholar in the Department of Mechanical Engineering,

Indian Institute of Technology-Bombay, Mumbai 400076, Maharastra, India. He is now Assistant Professor in the

Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Kuniamuthur Post,

Coimbatore 641008, Tamilnadu, India.

3
INTRODUCTION

Aluminum has become increasingly important to manufacturing products. Aluminum has

replaced many more traditional steel materials. The most conspicuous product of aluminum and

its alloys is the new breed lightweight cars. The reason is its higher resistance to climatic

conditions and higher corrosion resistance over steel cars. However, the most recognized benefit

is its greater strength combined with lower weight. The pulsed current gas metal arc welding

(GMAW-P) is the most advanced and promising one among various GMA welding processes,

for the welding of aluminum and its alloys, because it can greatly improve the weld quality and

promote the automation of all-position welding. It offers the possibility of welding thin metal

with spray transfer or using thick wire diameter for low average welding current.

Peak duration —, |—Background duration

Current,

A

verage

current

current

I Spray current

level B ackground

c urren t

Peak

Time, ms

a) Pulsed current

Peak duration —

|

^

Background duration

:

voltage

B ackground

voltage

1

Time, ms

b) Pulsed voltage

Ip-Peak current (A);

T-Pulse cycle time (ms), T=Tp+Tb; Pulse Frequency, Fpu=l/T

Iav -Average current (A); Iav = (Ip Tp + Ib Tb)/(Tp + Tb)

Ij-Spray current level (A); Above It-Spray metal transfer

Below It- Globular metal transfer;

Is-Background current (A)

Vp-Peak voltage (V);

VB-Background voltage (V)

Tp-Peak duration (ms);

Tfl-Background duration (ms)

Figure 1 : Pulsed current and pulsed voltage waveform for GMAW-P

In GMAW-P, the current fluctuates between a high peak current for the pulses and a low

background current in a regular cycle (Fig. 1). The molten metal is detached either as a single

droplet or multiple droplets depending upon the pulse parameters, namely peak current (Ip), peak

duration (Tp), background current (Ib), and background duration (Tb) (Refs. 1-3). If it is a single

droplet, this detachment may occur either in peak duration or background duration, and if it is

multiple droplet detachment, the detachment may occur in peak duration (Refs. 4-5). This

difference in the timing of droplet detachment affects the arc stability and the overall quality of

the weld. There are also shortcomings in the conventional GMAW-P process. First, setting of

GMAW-P variables can be very complicated. Secondly, it has a poor self-adjust ability in

response to accidental arc length disturbance. Studies on the arc characteristics are very

important to understand the stability of arc under various pulsing conditions. Therefore, studies

are carried out by focusing efforts on solving these problems.

4
All manufacturing industries and welding engineers are considering quality control to be an

important factor. Recently, there has been a lot of pressure to achieve the welding benchmark

standards. Process-integrated quality assurance enables online monitoring of welding voltage,

welding current as well as the reproduction of welding data and complete documentation of all

parameters and welding machine adjustments.

In this research paper, a optimum GMAW-P parameter is selected on the basis of process

integrated quality assurance: For this, first, the effects of GMAW-P parameters on the various

types of droplet detachments were studied and classified on the basis of type of droplet

detachment. Secondly, the average current per pulse cycle time was measured for the various

types of droplet detachments and the values of mean, standard deviation, and coefficient of

variation were calculated. The optimum GMAW-P was selected by choosing the pulse parameter

which resulted in the least standard deviation and the coefficient of variation, to produce costeffective, spatter free, high quality welds.

OBJECTIVE

The objective of this work was to study how GMAW-P parameters influence droplet detachment,

and to determine to what extent pulsed current can be used to produce stable Al-Mg alloy welds.

EXPERIMENTAL WORK

Filler Metal and Base Metal

The welding was carried out using solid filler metal ER 5356 Al-Mg alloy having a diameter of

1.2mm and with the compatible AA 5083 Al-Mg alloy of 6.0mm thick base metal. Argon gas

having a commercial purity of 99.97% and a flow rate of 3.333x10 m^/s was used throughout

the welding as a shielding gas.

Welding Power Supply

A transistorized welding power supply was used for carrying out GMAW-P operations (Fig. 2)

in a pulsed current mode, operating with constant voltage output characteristics. In the pulsed

current mode, peak voltage, background voltage, peak duration, and cycle time can be vary over

the ranges 10 to 40V, 10 to 30V, 1 to 15ms and 3.3 to 50 ms, respectively. Direct setting of

levels at peak and background current is not possible with the welding power supply used in the

present study. Hence, levels of peak and background voltage were adjusted by trial and error to

obtain the desired peak and background current levels, which were measured using high speed,

double-channel, digital storage oscilloscope. The peak duration, wire feed speed, and cycle time

could be independently adjusted using the welding power supply control.

5
1 . Personal computer

2. Double channel digital storage oscilloscope

3. Internal color digital plotter

4. Waveform processor

5. 5 kHz low pass passive filter for current signal

6. 5 kHz low pass passive filter for voltage signal

7. Torch

8. Filler metal

9. Electric arc

10. Base metal

1 1 . Carriage

12. Filler metal feed rollers

13. Filler metal reel

14.Transistorized welding power supply

15. Control plug

16. Argon gas cylinder

Figxire 2: The schematic of the pulsed current gas metal arc welding

Low Pass Passive Filters

The waveforms, which were captured by a high speed, double channel, digital storage

oscilloscope (Gould 4072) were not clear. Moreover, carrying out any measurements on the

waveform was difficult because of the poor appearance of the waveforms. Therefore the problem

of high frequency noise and process noise in the signals of the voltage and current was discussed

with the manufacturer and eliminated from the signals using two 5 kHz low pass passive filters as shown in Fig. 2. Totally, two filters were designed and fabricated. The first one was used for

the pulsed voltage signal and the second one for the pulsed current signal. These filters permit

frequencies below 5 kHz to pass but reject all other frequencies, which are above 5 kHz. Since

the expected molten droplet frequency is well within 5 kHz, the droplet detachment phenomena

could be clearly studied from the voltage spikes on the waveforms of pulsed voltage.

Data Acquisition

The pulsed current was sensed from the power supply and the pulsed voltage was sensed

between the base metal and the contact tip. Sufficient time was allowed during welding to reach

a stable welding condition. For this, the delay time was set in the oscilloscope and then

instantaneous current and voltage were monitored and recorded simultaneously with a high

speed, double channel, digital storage oscilloscope (Gould 4072) through 5 kHz low-pass passive

filters (Fig. 2).

Droplet detachment was observed from the voltage spikes on the waveform of the pulsed

voltage. There were variations in the peak voltage values along the peak duration apart from

those owing to the intentional pulsing. The reason for these variations is that when the droplet

was transferred, the instantaneous arc voltage momentarily increased and formed voltage spikes

6
on the peak voltage levels along the peak duration. Droplet detachment time and the number of

droplet detachments were obtained from the pulsed voltage traces.

Stored waveforms of pulsed current and voltage were transferred from the oscilloscope to a

personal computer and saved for later analysis. The personal computer was equipped with the

General purpose Instrumentation Bus (GB-IB) for current and voltage data acquisition. Totally

1000 data samples were saved during each welding experiments. Analysis of the fluctuations of

these values as a function of various pulsing parameters was useful to understand arc's behavior

and this could be correlated to the various types of molten metal droplet detachments.

C.Wav= (C.W1+C.W2+C.W3 +C.Wn)/n

Standard deviation, a = V (Z[C.Wav fl n) - (EC.Wav/ n)^

Coefficient of variance, C.V = (a/C.WAv) x 100

Figure 3: Measurement of average current for each pulse cycle time, and

calculation of the standard deviation and coefficient of variation.

Pulsed Current Signal

A typical pulsed current waveform is shown in Fig. 3. Area under every pulsed current

waveform (A. ms) per cycle time was calculated using AutoCAD 2000 and a self-developed

computer package EL-JS. The average current per cycle time was calculated by dividing area of

waveform under one pulse by pulse cycle time. Statistical analysis was carried out on the basis of

mean average current, standard deviation, and coefficient of variance, to determine the suitable

pulse parameter.

The level of arc stability was assessed from the regularity of average current per cycle time. The

standard deviation and coefficient of variation of average current were taken as the best measure

of the regularity of filler metal melting, during welding operation. The pulsed current waveform

with lesser standard deviation and coefficient of variation of average current was considered to

be good from the standpoint of good arc stability.

Time, ms

7
Welding Procedure

The flat position bead-on-plate welds were prepared under mechanized conditions at a fixed

welding speed of 10 mm/s and fixed wire speed of 100 mm/s. The welding torch was positioned

normal to the base metal surface. Filler metal was connected to the direct-current-electrodepositive (DCEP) polarity. Prior to welding, the oxide film was removed from the base metal by

mechanical means. The welding torch was mounted on four-wheeled variable speed tractor (Esab

A2 Mini trac). It was moved above the base metal and welding was carried out (down hand type)

in the rolling direction of base metal. The torch-to-work-angle and contact tip-to-work distance

were maintained at 90^ and 15 mm, respectively. The most suitable parameters of the pulsed

current and the type of droplet detachment to obtain a good quality weld were selected on the

basis of uniformity of average current.

RESULTS AND DISCUSSION

The improvement of arc stability in GMAW-P through optimization of the control parameters

can extend the operational range of filler metal. This is because the level of peak current exceeds

the level of spray current for axial droplet detachment, even at low average currents. The high

peak current exerts a pinch effect on the molten droplet at the tip of the filler metal sufficient to

propel a single droplet axially through the arc to the base metal. This pinch effect gives good

control over droplet detachment during welding. The generation of excessive spatter mainly

depends on whether the critical duration is too long or too short. The background current is used

to just maintain the arc between each pulse without extinguishing.

Optimum Waveform of Pulsed Current

Experiments were conducted at fixed values of Ws = lOmm/s, Wp = lOOmm/s, Ib = 20 A, and Tb = 6 ms to establish the relationship between I? and Tp for different types of droplet detachments,

namely, one droplet detachment during peak duration, one droplet detachment during

background duration, two droplets detachment during peak duration, and three droplets

detachment during peak duration, respectively. For this, levels of Ip were kept above the spray

current level, and Ip and Tp were allowed to vary. In order to obtain the above types of droplet

detachments, pulse cycle time was allowed to vary. For this, it was not assumed in this study that

molten droplet diameter would be equal to the diameter of weld metal.

Various types of droplet detachments were observed for different GMAW-P parameters by

means of high speed, double channel, digital storage oscilloscope and classified them in terms of

Ip and Tp. Such a result is shown in Fig. 4.

Zone 'A'

In the zone 'A' a large droplet is formed and arc behavior becomes unstable accompanied by

short circuits (Fig. 5), erratic droplet detachment, and much spatter, because the electromagnetic

force applied to the molten filler metal tip is not directed to the base metal. Note that in this zone

the value of Ip is smaller than 168 A, which is 18A higher than spray current level of 150A with

1.2mm diameter ER 5356 Al-Mg filler metal used in this research work. It shows that higher

currents are necessary to realize the spray type of molten droplet detachment. The pendant

8
droplet is stationary at the end of the background period; therefore the additional current is

required to overcome the drop inertia. But in the steady D.C. GMAW process the pendant

droplet is in constant streaming motion once it is initiated.

260

ZoDC F

240

220

g 200

^ 160

— ZcociC \ >^ . >w /

Tr MiD "^^/^ * ^^s, V Zone:A

140

Sprf^CTrrrat Level (1 50 A)

Peak duration, (ms)

Zone c _r^^ droplet detachment during peak duration 1

\ One droplet detachment during background duration

|

Zone D _rrwo droplets detachment during peak duration, an3~l

\ Three droplets detachment during peak duration |

Figure 4: Classified zones of different types of droplet detachments

Zone 'B'

In the zone 'B', where Ip>168A and Tp<Tp min, the peak energy is too small to make a droplet

detachment in one pulse and the pendant droplet swells into a large globe during several pulses.

Therefore this zone is undesirable.

Zone 'C

One droplet with a diameter close to that of the filler metal detaches regularly and reliably per

every pulse cycle time either during background period (Fig.6) or during peak duration (Fig.7) in

the single droplet detachment zone 'C where Ip>168A and Tp min ^ Tp ^ Tp ideal. The

waveforms with less variations in the average current for the one droplet detachment during

background duration and for the one droplet detachment during peak duration are shown in Fig.

8 and Fig. 9, respectively.

Zone 'D'

The droplet detachment was apparently the spray type in the zone 'D'. Where Ip>168A and T? >

Tp max. In this multiple droplet detachment zone, either two droplets (Fig. 10) or three droplets

(Fig. 11) were detached during peak duration, depending upon the combination of GMAW-P

within this zone 'D'.

There was a considerable spatter during experiments with multiple droplet detachments. This is

because when the peak duration is too long, beyond the optimum value, the molten filler metal

tip is prolonged. This resulted in much spatter due to contact with the base metal, and the

subsequent explosion of droplets. The peak energy was also high because of the application of

large peak duration in multiple detachments as shown in Table 1. As can be seen from the

Table 1, one droplet detaches during background period for average peak energy of 18.5 J, and

9
one droplet detaches during peak duration for average peak energy of 21.2 J. At the same time,

average peak energy of 28.7 J was required to obtain two droplets detachment during peak, and

average peak energy of 30.7 J was required in the case of three droplets detachment during peak.

Fig. 12 shows a typical pulsed current waveform with less variation in the mean average current

for the two droplets detachment during peak duration. The pulsed current waveform with less

variation in the mean average current for the three droplets detachment during peak duration is

presented in Fig. 13.

Peak duration = 3 ms

Droplet detachment

time " 4.05 ms

Necking

Filler metal

Stages of droplet

detachment

One droplet detachment

during backgroimd duration

Droplet detachment

time = 4.05 ms

Voltage spike indicating

droplet detachment

Background

duration= 6 ms

Time, ms

Ip= 160 A; Tp= 5 ms; Ib= 20 A; Tb= 1 1 ms; Ip= 260.8 A; Tp= 3 ms; Ib= 20 A; Tb=^ 6 ms; Td = 4.05 ms; Vp=20.8 V; Vb= 13.8 V Vp= 25.32 V; Vb= 15.98 V; Iav= 108.4 A; Vav= 19.08 V;

Wf= 100 mm/s; and Ws= 10.0 mm/s

Figure 5: Typical pulsed voltage and current Figure 6: Voltage signal showing one droplet detachment

waveform with short-circuit during background duration

Zone 'E'

The droplet detachment was apparently the spray type in the zone 'E', where Ip>168A and Tp>Tp

max. But, the molten filler metal tip was prolonged and touches the base metal causing much

spatter. Moreover, as Tp increases in this zone 'E', frequency of droplet detachment decreases at a

constant feeding rate of filler metal, resulting in continual increased fluctuations of the arc

length.

Zone 'F'

In the zone 'F', arc breaks because of the excessive arc length. Therefore, this zone is highly

undesirable.

10
Droplet detacbment

time = 5.33 ms

Background

-duration = 6 ms

Time, ms

Ip= 176.5 A; Tp= 6 ms; Ib= 20 A; Tb= 6 ms; Td= 5.33 ms; Vp= 23.3 V; Vb= 16.6 V

Iav= 95.1 A; Vav= 19.6 V; Wf= 100 mm/s; and Ws= 10.0 mm/s (Weld no. EL-3)

Figure 7: Voltage signal showing one droplet detachment during peak duration

Selection of Suitable GMAW-P Parameter and Type of Droplet Detachment

Average pulse current has been one of the criteria considered for selection of the most suitable

pulse parameter combinations in order to obtain good quality weld. Also, on the basis of the

above criterion, a suitable type of droplet detachment was recommended from among the four

different types of droplet detachments, namely, one droplet detachment during peak duration,

one droplet detachment during background duration, two droplets detachment during peak

duration, and three droplets detachment during peak duration.

The standard deviation and coefficient of variation of the average current were calculated (Table

1) for four types droplet detachments, namely, one droplet detachment during peak duration

(Region- 1), one droplet detachment during background duration (Region-2), two droplets

detachment during peak duration (Region-3), and three droplets detachment during peak duration

(Region-4) and represented in Figs. 14 and 15, respectively. Different types of droplet

detachments were obtained at various pulsing conditions and grouped in Region- 1, Region-2,

Region-3, and Region-4.

Good correlation was obtained between the standard deviation of average current and peak

energy, and between the coefficient of variation of the average current and peak energy. Region1 consists of the lowest range of standard deviation among all four regions. This shows good

regularity of average current in Region- 1, during filler metal melting. Region-4 consists of the

highest range of standard deviation among all four regions. Region-2 and Region-3 are in

between Region-1 and Region-4 as shown in Figs. 14 and 15.

The lowest standard deviation and the lowest coefficient of variation for the average current were

observed for the welds EL-1, EL-8, EL-10, EL-18, which were obtained at different pulsing

conditions, namely, one droplet detachment during peak duration, one droplet detachment during

background duration, two droplets detachment during peak duration, and three droplets

11
detachment during peak duration, respectively. Among these four types of droplet detachments,

the lowest standard deviation (0.9228) and the lowest coefficient of variation (0.8361) were

found for the weld no. EL-1, which was made with one droplet detachment during peak duration.

Weld Tp Tp Vp T Tn* Deta Peak C Wav C W<!n C Wr V

No. -ch

ment

Energy

(J)

(A) (ms) (V) (ms) (ms) Type (A) (A)

EL-1 196.8 5 22.9 11.0 5.00 A 22.53 110.3718 0.9228 0.8361

EL-2 181.8 6 23.1 12.0 5.14 A 25.20 103.5011 0.9597 0.9272

EL-3 176.5 6 23.3 12.0 5.33 A 24.67 95.0075 1.2426 1.3079

EL-4 231.9 3 24.8 9.0 2.85 A 17.25 108.9990 1.1516 1.0565

EL-5 240.5 3 23.2 9.0 4.38 B 16.74 109.9400 1.2834 1.1674

EL-6 211.5 4 23.1 10.0 5.90 B 19.54 110.3000 1.3218 1.1984

EL-7 244.2 3.5 22.1 9.5 4.20 B 18.89 120.2500 1.2627 1.0499

EL-8 229.0 4 22.1 10.0 4.38 B 20.24 120.4600 L2504 1.0380

EL-9 243.6 3.5 22.0 9.5 4.20 B 18.76 122.2900 1.3006 1.0635

EL-10 217.5 4 23.1 10 3.60 C 20.10 109.6437 1.4227 1.2976

EL-11 226.0 4 23.6 10 3.72 C 21.33 112.8266 1.5072 1.3359

EL-1

2

224.0 5 23.1 11 3.54 C 25.87 118.2110 1.5921 1.3468

EL- 13 180.5 6 22.8 12 4.71 C 24.69 103.3983 1.5579 1.5067

EL- 14 187.1 8 24.9 14 3.87 C 37.27 112.7396 1.7526 1.5546

EL-1

5

185.6 7 23.0 13 3.86 D 29.88 111.7484 2.1722 1.9439

EL- 16 192.9 8 23.9 14 3.74 D 36.88 120.6306 2.0297 1.6826

EL-1

7

180.2 9 23.6 15 4.02 D 38.27 118.0505 2.0210 1.7120

EL-1

8

168.3 9 22.6 15 4.42 D 34.23 114.1309 L9066 1.6706

EL-1

9

177.1 9 23.8 15 4.51 D 37.93 115.6615 2.0776 1.7963

EL-20 172.7 10 23.6 16 4.8 D 40.76 111.3447 2.3147 2.0789

Ip

Tp

Vp

T

Td

Peak current

Peak duration

Peak voltage

Pulse cycle time

Droplet detachment time

For multiple droplet detachments,

Td= Time between the beginning

of the pulse and the first droplet

detachment

A: One droplet detachment during peak duration

B: One droplet detachment during background duration

C: Two droplets detachment during peak duration, and

D: Three droplets detachment during peak duration

Peak Energy = Ip x Tp x Vp (J)

C.Wa.v Mean value of average current

C.Ws.D Standard deviation of average current

C. Wcv Coefficient of variation of average current

Table 1 . Type of droplet detachment, and statistical analysis of average current

12
<

I

50 n

Time, ms

Ip= 229.0 A; Tp= 4 ms; Ib= 20 A; Tb= 6 ms;

Td = 4.38 ms;Vp= 22.1 V;Vb= 14.8 V;

Iav= 120.46 A; Vav= 17.4 V; Wf== lOOmm/s;

Ws= 10.0 mm/s; C.Wav = 120.46 A;

C.Ws.D =1.2504 A; and C.Wc v = 1-0380.

Figure 8: The waveform with less variation in

the average current for the one droplet

detachment during background

duration (Weld no. EL-8).

Ip= 196.8 A; Tp= 5 ms; Ib= 20 A; Tb= 6 ms;

Td= 5 ms; Vp= 22.9 V; Vb= 15.03 V;

Iav= 110. 3718 A; Vav= 18.1 V; Wf= 100 mm/s;

Ws= 10.0 mm/s; C.Wav = 110.3718 A;

C.Ws.D =0.9228 A; and C.Wcv = 0.8361

Figure 9: The waveform with less variation

in the average current for the one

droplet detachment during peak

duration (Weld no. EL-1)

The smaller the standard deviation and the smaller the coefficient of variation, the more uniform

the average current is. That means this type of droplet detachment provides a droplet from the

filler metal tip regularly during peak duration in order to produce spatter free, high quality, and

stable weld. Hence, one droplet detachment during peak duration was considered to be the best

for providing a more uniform average current than the other types of droplet detachments. Based

on this, the following parameters of the pulsed current were found to be more suitable than the

other combinations of pulsed current: I? = 196.8 A; Tp = 5.0 ms; Ib = 20 A; Tb = 6 ms; Wf =100

mm/s; Iav== 110.3718 A; and Ws= 10.0 mm/s.

Therefore, only the GMAW-P combinations within the optimum zone 'C (Fig. 4), which

provides one droplet detachment during peak duration, are suitable for carrying out good quality

weld. The above parameter combination can be programmed to the computer so that whenever

operator wants to use the wire feed rate, Wp = 100 mm/s, automatically this particular pulsed

waveform is selected, and the operator gets good quality weld. Thus process-integrated quality

assurance of the weld can be possible on the basis of more uniform average current. This above

experimental work can be suitably extended to the other wire feed rates in order to achieve stable

weld.

Furthermore, the standard deviation (1.2504) and coefficient of variation (1.0380) of the weld

no. EL-8, which was welded with one droplet detachment during background duration, was

found to be the second best in terms of the uniform average current.

13
Filler metal

Second droplet detachment

time = 5.2 ms

First droplet detachment

time = 3.8 ms

Droplet detachment

time = 5.2 ma

Stages of droplet detachment

^ ^ Second droplet detadiment

during peak duration

First droplet detachment

during peak duration

Voltage spike indicating

first droplet detachment

Voltage spike indicating second droplet detachment

Time, ms

Ip= 230.3 A; Tp= 5 ms; Ib= 20 A; Tb= 6 ms; Tdi - 3.8 ms; Td2= 5.2 ms; Vp= 23.02 V;

Vb= 15.2 V; Iav= 109.4 A; Vav= 18.3 V; Wf= lOOmm/s; and Ws= lO.Omm/s

Figure 10: Voltage signal showing two droplets detachment during peak duration.

Third droplet detachment

time = 9.4 ms

Second droplet detachment

time = 7.4 ms

First droplet detachmen

;

time =4.8 ms

^

Third droplet detachment

during peak duration

Stages of droplet detachment

Second droplet detachment

during peak duration

First droplet detachment

during peak duration

Voltage spike indicating

first droplet detachment Vohage spike indicating second droplet detachment

Voltage spike indicating

third droplet detachment Backgroimd

time = 6 ms

Time, ms

Ip= 1 72.7 A; Tp= 1 0 ms; 1b = 20 A; Tb = 6 ms; Tdi = 4.8 ms; Td2 = 7.4 ms; Td3 = 9.4 ms;

Vp= 23.6 V; Vb = 1 6.5 V; Uv = 1 1 1 .3 A; Vav = 20.2 V; Wp = 100 mm/s; and Wg = 10.0 mm/s.

Figure 1 1 : Voltage signal showing three droplets detachment during peak duration.

14
Ip= 217.5 A; Tp=4ms; Ib=20 A; TB=6ms;

Td= 5 ms; Vp= 23.1 V; Vb= 14.7 V;

Iav= 109.6437 A; Vav= 17.98 V;

Wf = 1 00 mm/s; Wg = 1 0.0 mm/s;

C.Wav = 109.6437 A; CWg o =1.4227 A;

andC.Wc.v = 1.2976.

Figure 12: The waveform with less variation

in the average current for the two

droplets detachment during peak

duration (Weld no. EL-10).

If= 168.3 A; Tp= 9 ms; Ib= 20 A; Tb= 6 ms;

Td = 4.42 ms; Vp= 22.6 V; Vb= 15.32 V;

Iav= 114.1309 A; Vav= 18.77 V;

Wf = 1 00 mm/s; Wg = 1 0.0 mm/s;

C.Wav = 114.1309 A; CWgo =1.9066 A;

andC.Wcv = 1-6706

Figure 13: The waveform with less variation

in the average current for the three

droplets detachment during peak

duration (Weld no. EL- 18).

SUMMARY

The automotive industry is interested to furthering the use of Al-Mg alloys in the production of

new breed lightweight cars. The pulsed current gas metal arc welding (GMAW-P) is widely used

to weld these alloys. However, quality of the weld mainly depends upon the parameters of the

pulsed current. Selecting the most suitable GMAW-P parameter is very complex due to

interdependence of parameters. Therefore, in this research paper, an optimum GMAW-P

parameter was selected on the basis of process-integrated quality assurance. Because, processintegrated quality assurance enables online monitoring of welding voltage, welding current as well as the reproduction of welding data and complete documentation of all parameters and

welding machine adjustments.

15
I.

3.0 -

2,5 Region 4

2,0 Region 3

r/i 1.5

^^^^

1.0 ^

Region 2

Region 1 0,5

n 1 1 1 1 1 1 1 1 1

10 14 18 22 26 30 34 38 42 46

Peak Energy (J)

Figure 14: Standard deviation of average current versus peak energy

2.5

2.0

Region 2 ^^^^^egion 4

1 1,5

1,0 ^^J^^Z Region 3

Region 1

0.5

0

1 1 1 1 1 1 1 1 1

10 14 18 22 26 30 34 38 42 46

Pccili Energy (i

)

Figure 15: Coefficient of variation of average current versus peak energy

16
For this, first, the effects of GMAW-P parameters on the various types of droplet detachments

were studied and classified in different zones on the basis of type of droplet detachment.

Secondly, the average current per pulse cycle time was measured from the each waveform for the

various types of droplet detachments and the values of mean, standard deviation, and coefficient

of variation were calculated. The optimum GMAW-P was selected by choosing the pulse

parameter which resulted in the least standard deviation and the coefficient of variation, to

produce cost-effective, spatter free, high quality welds. Among four types of droplet detachments

studied, one droplet detachment during peak duration was considered to be the best for providing

a more uniform average current than the other types of droplet detachments. Thus processintegrated quality assurance of the weld was possible on the basis of more uniform average

current.

Statistical analysis of fluctuations in average current per pulse cycle time using a personal

computer was carried out for the quantitative assessment of welding arc stability. The computer

analysis represents a powerful means of measuring the degree of arc stability and filler metal

melting uniformity. This technique facilitates research into process-integrated quality assurance

and to improve the design of GMAW-P equipment to ensure that preferred welding parameters

are used and to research the cause of variable performance of GMAW-P equipment in actual

practice. The study can also be extended to monitor the manual skill of welding machine

operators during their training or actual work. Importantly during underwater welding, the

instructor cannot find out the weld quality because of poor visibility of the prevailing or existing

condition.

CONCLUSION

1. Statistical analyses of average current from the pulsed current waveform using a

computer provide a powerful means of directly assessing the arc stability. This can be

very helpful for monitoring and improving the GMAW-P process and for optimizing the

parameters of the GMAW-P process, optimizing filler metal, welding equipment, and

power sources.

2. Every variation in the melting and detachment of filler metal can be seen in the

waveforms of pulsed voltage and pulsed current. This study helped to obtain optimum arc behavior in terms of arc stability, spatter level, and droplet detachment in order to ensure

optimum weld quality.

3. The peak duration is critical, for if it is too long or too short it results in the generations of

excessive spatter.

4. The zone 'C' is preferable among all the zones to carryout welding. In this zone, only the

one droplet detachment during peak duration was desirable because the standard

deviation and coefficient of variation of average current were found to be the lowest,

among from the four types of droplet detachments.

5. The peak energy required for one droplet detachment during background duration was the

lowest. Furthermore, one droplet detachment during peak duration, two droplets

detachment during peak duration, and three droplets detachment during peak duration

17
require higher peak energies, increasing in the order given. The highest peak energy of

40.76 J was observed in the case of three droplets detachment during peak duration.

6. The most uniform average current was observed for the welds made with one droplet

detachment during peak duration (weld no. EL-1) compared with the average currents for

one droplet detachment during background duration, two droplets detachment during

peak duration, and three droplets detachment during peak duration. Because, among from

the four types of droplet detachments, the lowest standard deviation (0.9228) and the

lowest coefficient of variation (0.8361) were found for the weld no. EL-1. Based on this,

the following parameters of the pulsed current were found to be more suitable than the

other combinations of pulsed current: Ip = 196.8 A; Tp = 5.0 ms; Ib = 20 A; Tb = 6 ms; Wp

= 100 mm/s; Iav= 110.3718 A; and Ws = 10.0 mm/s. Thus process-integrated quality

assurance of the weld was possible on the basis of more uniform average current. This

study can be very much useful for fabricators to derive the fullest benefits from GMAWP in high quality or automatic or robotic welding.

7. The value of Ip should be set above the spray current level and Tp should be so adjusted

that the filler metal tip is melted to a proper size and one droplet is detached appropriately

to the base metal during peak duration.

8. Control parameters that characterize the arc stability include average current, peak

energy, and droplet detachment time. Monitoring and analyzing these parameters can

make sure that the GMAW-P process will be carried out in a optimum droplet

detachment type, thus ensuring the best welding conditions for the optimum droplet

detachment.

LIST OF SYMBOLS

c.v Coefficient of variation Td2 Time between the beginning

C.Wa.v Mean value of average current of the pulse and the second

C.Wcv Coefficient of variation of droplet detachment

average current Td3 Time between the beginning

C.WsD Standard deviation of average current of the pulse and the third

DCEP Direct Current Electrode Positive droplet detachment

Fpu Pulse frequency (1/T) Tp Peak duration

GMAW-P Pulsed Current Gas Metal Arc Welding Td Droplet detachment time

I Welding current V Voltage

Ib Background current Vb Background voltage

Ip Peak current Vp Peak voltage

It Spray current level Vav Average arc voltage

Iav Average current Wp Wire feed speed

T Pulse cycle time Ws Welding speed

Tb Background duration o Standard deviation

Tdi Time between the beginning of the

pulse and the first droplet detachment

18
REFERENCES

1. Rajasekaran, S.; Kulkami, S.D.; Mallya, U.D.; Chaturvedi, R. C. 1998. Droplet

detachment and plate fusion characteristics in pulsed current gas metal arc welding.

Welding Journal 77 (6): 254-s to 269-s.

2. Rajasekaran, S. 1999. Weld bead characteristics in pulsed GMA welding of Al-Mg

alloys. Welding Journal 78 (12): 397-s to 407-s.

3. Rajasekaran, S. 2000. Weld surface undulation characteristics in the pulsed GMA

welding process. The Ninth International Conference on Computer Technology in

Welding. September 28-30,1999, National Institute of Standards and Technology, United

States Department of Commerce, Special Publication No. 949. May 2000, 349-360.

4. Rajasekaran, S.; Kulkami, S.D.; Mallya, U.D.; Chaturvedi, R. C. 1995. Molten droplet

detachment characteristics in steady and pulsed current GMA welding Al-Mg alloys.

Proceedings of the Sixth International Conference on Aluminum Weldments (INALCO

'95). April 3-5, 1995, Cleveland, Ohio, U.S.A. American Welding Society, Miami, Fla,

207-224.

5. Rajasekaran, S.; Kulkami, S.D.; Mallya, U.D.; Chaturvedi, R. C. 1994. Droplet

detachment and plate fusion characteristics in pulsed current gas metal arc welding.

Proceedings of the Advanced Joining Technologies for New materials II. March 2-4,

1994, The Cocoa Beach Hilton, Florida, U.S.A, American Welding Society, Miami, Fla,

172-187.

19

DROPLET OSCILLATION AND WELD POOL IMAGING

USING COMPUTER-CONTROLLED COMPOSITE PULSE CURRENT

B. Zheng

ABSTRACT

This paper presents a unique solution to real-time monitoring of both droplet detachment and

weld pool during a pulsed gas metal arc welding process: a composite pulse current consisting of

a square wave form followed by a sine wave form was designed. The instant for initiating a

constant base current adaptively started at the instant droplet detachment was sensed. During the

period of the base current, a flag signal was generated to trigger the imaging of a weld pool. The

approach makes droplet detachment and image acquisition be proceeded without outside

intervention. This provides the possibility for real time quantifiable monitoring and control for

both droplet transfer and weld pool penetration.

INTRODUCTION

The results of the previous researches showed that metal transfer and weld pool penetration both

have a large influence on the generation of the defects such as undercut, bum through,

insufficient melting, spatter, gas pore, and even weld cracks during a gas metal arc welding

(GMAW) process {Ref. 1-3). Hence, in-process monitoring and control for metal transfer and

weld pool penetration are crucial in order to minimize the cost of post-weld inspection and

repair. Currently, a droplet/arc oriented control strategy is often used even though it is inefficient

in some applications. Since maintaining a consistent weld pool has a dramatically direct impact

on weld quality, a weld pool oriented control strategy has been largely demanded.

In a GMAW process with argon-rich shielding, the different metal transfer modes of shortcircuiting, globular, and spray can be observed in sequence as the welding current is increased

(Ref. 2-5) when steel electrode wire is used. The critical welding currents at which the metal

transfer mode changes are defined as transition currents, one of which is the spray transition

current at which globular transfer becomes spray transfer (Ref. 2-3). The level of the spray

transition current mainly depends on many factors such as wire diameter and composition of

shielding gas. Quality welds can be achieved using the projected spray transfer mode of one

droplet per pulse (ODPP). To ease the flexible selection of ODPP and simplify the adjustment of

process parameters, pulsed current welding (GMAW-P) is a preferred process (Ref. 3 and 6-9).

However, most of GMAW-P processes with ODPP mode use open loop control that regulates

only the metal transfer mode and pay little attention to the weld pool (even though some

researches have been pursued on sensing and closed-loop control of droplet transfer) (Ref. 4-21).

The partial reason for this is the difficulty in measuring characteristic signals and control

compatibility of droplet detachment with weld pool penetration.

Besides through-arc sensing of a weld pool (Ref 22-28), machine vision systems have been used

to sense the weld pool and control the full penetration during a PAW, or GTAW, or GMAW

process (Ref. 29-45). These vision systems can be characterized into band-pass arc light filtering,

* Edison Welding Institute, 1250 Arthur E. Adams Drive, Columbiis, OH 43221

21
coaxial viewing, high-shutter-speed camera sjmchronizing with laser-assisted illumination, and

infrared thermography. The instantaneous decrease of the welding current has also been used to

weaken the intensity of the arc light to capture a clear weld pool image in GMAW-P (Ref 44).

However, the interference resulting from metal transfer while imaging a weld pool was not

solved because of the inaccurate open-loop control that was used to determine the instant of the

droplet detachment. Also, the camera had some limitations, such as the shutter speed and the

luminance, which could not meet the needs of imaging and controlling a GMAW weld pool.

The objective of this paper is to explore a unique solution to the compatibility of sensing and

control of a droplet detachment and imaging a weld pool during a GMAW-P process, which will

set a basis for real time control of a fully penetrated weld pool (Ref 44-46).

EXPERIMENT PROCEDURE

A PC-controlled, inverter welding power source and a wire feeder are used in this experiment. A

high frame rate digital camera with adjustable frame rate and a frame grabber is used for

monitoring the droplet with the back illuminating light from a He-Ne laser. Current and voltage

sensors are integrated with a data acquisition system. A positioning system driven by stepper

motors is also interfaced with the system. A CCD camera with high shutter speed is attached to

the front of the torch to synchronously image the weld pool.

To explore a droplet detachment method associated with the imaging of a weld pool, a controlled

signal generator was designed, the current wave form of which is shown in Fig. 1 . The period of

pulse consists of a square wave form and a sine wave form with a pre-selected frequency. The

peak current for a square pulse is experimentally set at around 160 A that is below the spray

transition current, the function of which is to initiate the droplet growth and minimize the energy

input to a workpiece. The function of a sine wave pulse following the square pulse is to activate

an externally forced oscillation of a droplet before it detaches from the tip of an elecfrode wire.

The sine wave pulse with its peak level (260 A) being higher than transition current (220-240 A,

in this case) adaptively

terminates at the

instant of the droplet

detachment monitored

by the arc voltage

sensor. The imaging of

a weld pool is initiated

during the base current

(40 A) period after a

time delay that is

experimentally set. The

frequency of the sine

wave pulse can be

varied from 0 to 500

Hz. The total cycle

frequency is less than

30 Hz due to the

limitation of the

Pulse peak current =

|pgi Sine \vave peak current = Ips2 Base current = 1^ Transition current = Ig Pulse period = tpsi + tps2 Base period =

Sine wave cycle lime = tslne

T Time

tc Delay for triggering camera = t j

Shutter open

time = tc

Vs— Synchronous pulse

Time

Vr— Camera trigger pulse

1

Time

Fig. 1 Composite Pulse Current (Ic), Synclironous Pulse (Vs),

and Trigger Pulse (Vj)

22
recording frequency by the high shutter speed camera. A droplet cannot detach during the period

of a square pulse because of the low electromagnetic pinch-off force resulted from the acting

current. The sine wave current pulse not only promotes the continuous growth of a droplet as it

oscillates but also assists in droplet detachment and further decreases the energy input to a

workpiece. At the end of the base period for maintaining an arc, a new cycle repeats the same

process.

Bead-on-plate welds were made in a flat position on 6.0-mm thick mild steel plates. The

electrode wire is 1 .2-mm diameter steel (AWS standard ER 70S) and the shielding gas is argonrich (85%AH-15%C02).

RESULTS AND DISCUSSION

According to the static force balance theory (Ref. 10-12), the forces promoting droplet

detachment in a flat welding position are the electromagnetic force resulting from the welding

current, gravity and the plasma gas drag force. The force resisting the droplet detachment is

mainly the surface tension. The other resistant forces are anode spot pressure and metallic vapor

force acting on the anode spot. When the forces promoting droplet detachment are equal to or

greater than the resisting forces, the droplet will detach from the tip of an electrode wire. The

electromagnetic force will be a promoting force for droplet detachment when the lower half of

the droplet is covered with the spread arc anode spots and the welding current is above the

transition current. Otherwise, it will become a resisting force for droplet detachment. The

magnitude of the electromagnetic force depends on the level of welding current (Ref 11-13).

The control strategies for droplet detachment may be categorized into two types: electrical and

mechanical (Ref 6-19).

Since electrically controlling the

electromagnetic force inside the droplet and arc

is reliable and fast, most investigations on

droplet detachment control focus on this

approach. In an ODPP mode, the current

during the pulse period of a cycle forms a

droplet with a certain size, while the current

during the base period of a cycle prevents the

arc from extinguishing. The melting rate of the

wire (the wire feed speed) is directly

proportional to the pulse frequency: the higher

the frequency, the higher both the average

welding current and the melting rate. The pulse

period and level also have the positive effect

on the melting rate when the pulse frequency is

constant. Therefore, the mass and heat

transferred to the workpiece may be flexibly

regulated by the adjustment of average welding

current.

Both the static force balance theory and the

pinch instability theory reveal that the ODPP approach, characterized by approximately the same

Fig. 2 Control for Droplet Detachment

with Resonance Oscillation

23
a

Fig. 3 Images of a Weld Pool in Pulsed Current Gas

Metal Arc Welding of Steels

a. viewing over a weld pool from the front of a welding torch

b. side view over a weld pool from the side of a weld torch

droplet diameter as that of the wire, can be electrically achieved only when the pulse current is

above the transition current.

The droplet may detach during

the pulse period or the base

period in a cycle, depending

upon different combinations of

the selected parameters.

During its growth, the droplet

at the tip of an electrode wire

oscillates vertically

(sometimes with a horizontal

component of oscillation) at

random frequencies (multiresonant oscillation) due to the

forces acting on it. The

random oscillation of the

droplet was observed and

analyzed mostly in a globular

transfer mode where the droplet suspends at the tip of an electrode wire for a comparatively

longer time because the electromagnetic force inside the droplet is not large enough to detach it (Ref. 11-13). This type of oscillation is not controllable and thus not a candidate for droplet

detachment control. The active control of droplet detachment was developed using a high frame

rate camera as a vision sensor to detect the vertical coordinate of the droplet along the axis of the

wire (Ref. 13-14). However, there are some issues that still need to be addressed, such as the

possibility to change the instant of the

droplet detachment through changing

the number of oscillations in a cycle

and the compatibility with the

imaging of a weld pool.

An active variation in the welding

current between a level above the

spray transition current and a level at

the base current during the pulse

period in a cycle will make the

electromagnetic force inside the

droplet change accordingly. Suppose

the surface tension, the plasma drag

force and other forces are constant,

the droplet at the tip of an electrode

wire may be forced to oscillate

synchronously due to this varying and

controllable electromagnetic force. A

frequency-controlled variation of a

current with a sine-wave form is an

ideal approach (as shown in Fig. 1).

The natural frequency of oscillation

Slice of a Image Based

on Self-thresholding

Medium Filtering with a Window of 9 Elements

I

Bordering the Middle

Part of a Weld Pool

Removal of the Middle

Part of a Weld Pool

Adaptive Operator

Edge Enhancement

Sobel Edge

Enheancement

I

Prewitt Gradient Edge

Enhancement

Binary an Image

Binary Opening and

Closing

Differential Subtraction

Scanning for Area

Worm Tracing of Edge

and Scanning for Area

Fig. 4 Procedure for Processing a Weld Pool Image

24
for a given droplet depends on its size. When this frequency equals that of a periodically varied

external composite force applied on the droplet, resonance will occur. In this case, the droplet

may detach if the magnitude of the force due to resonance is high enough. The essence of this

approach is to convert the electrical energy from a welding current into the mechanical energy of

a droplet oscillation that can be used to control the timing of droplet detachment and to image the

weld pool. During droplet oscillation, although the heat generated during the period when the

sine wave-form current is acting might be less than the heat due to a square pulse with the same

magnitude and duration, it still is high enough to continuously melts the wire.

Figure 2 shows sequential images of droplet oscillations and detachment created using the

composite pulse wave form shown in Fig. 1 . It can be seen that the arc is maintained and the

electrode wire melts from frame 1 to frame 7. From frame 8 to frame 12, the droplet finishes the

first cycle of oscillation. From frame 12 to frame 15, the droplet makes the second oscillation.

From frame 15 to frame 18,

the droplet oscillates the third

time. The droplet also makes

the fourth oscillation from

frame 18 to frame 21, and then

detaches at the instant between

frame 22 and frame 23. The

frequency of the droplet

oscillation (496 Hz,

determined by counting the

number of image frames and

making a simple calculation)

is nearly the same as that of a

sine wave pulse (500 Hz).

Compared with the droplet

oscillation, the sine wave

current has just two and a half

cycles. Therefore, in this case,

the droplet can oscillate only

twice based on the sine wave

frequency and one induced

oscillation resulting from the

reduction of the current after

the square wave pulse. The

fourth oscillation and the

detachment of the droplet

indicate that the droplet

resonance occurs because of

the effect of inertia produced

by the forced multiple

oscillations. An algorithm for

controlling the welding current

is developed based on the

composite wave form using

Fig. 5 Processed Images of a Weld Pool Using Different

Algorithms

(a) Bordering the middle part of an image

(b) Prewitt gradient edge enhancement

(c) Scanning an image differentially subtracted

(d)Worm tracing of an edge for binary image

(e)Area caculation of weld pool in an image

(f) Sum of an edge and an original image of a weld pool

25
arc voltage as a monitoring signal.

Since droplet detachment can be achieved at a controllable instant using the unique composite

pulse designed in this paper, a high shutter speed camera is externally triggered to capture the

image of a weld pool under the active projection of light from a laser source. This is done after a

droplet detaches during the period of the base current. A band pass filter that allows 330 nm N2

laser light to pass through is attached to the front of the camera. The light is guided through a

fiber optics cable and projected on the weld pool. The time delay of tx, shown in Fig. 1, can be

adjusted. The process of droplet transfer also can be simultaneously imaged on the same frame

as that of the image of the weld pool. In order to guarantee the image quality of a weld pool, tx was experimentally determined to match that instant immediately after a droplet is transferred.

Because of the decrease of the welding current, the image of a weld pool illuminated by the laser

light forms under a very low light radiation from an arc. This sequential timing approach

uniquely makes a trade-off between the droplet transfer control and the weld pool imaging. The

acquired images viewed at different camera positions are shown in Fig. 3. It can be seen that the

metal transfer process is avoided while the image of a weld pool is captured. The side view of a weld pool shows that the weld pool possesses a convex shape that is much different from that of

a concave weld pool which occurred in gas tungsten arc welding or plasma arc welding. The

boundary located at the rear part of a weld pool can not be clearly identified because of its

convex shape. The image of a weld pool from the front view satisfies the requirements needed

for image processing, even though a brighter area resulting from an arc during the base current

exists in the middle area. The features of a weld pool image acquired in this paper lead to the determination of an off-line

image processing procedure that is shown in Fig. 4. Also, scanning and worm tracing for edge

extraction and area calculation are comparatively pursued. The procedure for image processing

includes routine algorithms and core algorithms such as adaptive thresholding for image slicing,

bordering the middle part of an image, and worm tracing of an edge. The boundary for a weld

pool image by using worm tracing algorithm can be seen in Fig. 5(d). Some processed images

corresponding to Fig. 4 are shown in Fig. 5. The variation of the area calculation between

scanning and worm tracing for an edge are in the range of a 100-200 pixel difference for the

defined weld pool area (the white area in Fig. 5 (e)) that has more than 30,000 pixels.

CONCLUSIONS

The following conclusions can be drawn based on the research of this paper:

1 . In-process monitoring and control of weld quality is one of the crucial issues for gas metal

arc welding (GMAW). The unique solution presented demonstrates real-time monitoring of

both droplet detachment and the weld pool during a GMAW-P process.

2. A composite pulse current consisting of a square wave followed by a sine wave can initiate

the growth and oscillation of a droplet before its detachment from the tip of an electrode

wire. The instant for activating a constant base current can be adaptively started after the

sensed instant of droplet detachment.

3. The cyclic number of droplet oscillations depends largely on pulse parameters. Careful

selection of welding parameters, monitoring arc voltage, and appl)dng appropriate algorithms

26
to extract the information of droplet detachment will guarantee real time control for smooth

metal transfer.

4. Imaging of a weld pool can be reliably triggered by a delayed flag signal generated during

the period of a base current. In a cycle, droplet detachment and image acquisition can be

compatibly accomplished.

5. In addition to other image processing routines, worm tracing and scanning are developed for

calculating the area of a weld pool image. The procedure developed for processing a weld

pool image includes its boundary extraction and area calculation and provides the possibility

for real time quantifiable monitoring and control of the weld pool penetration state in the

future.

ACKNOWLEDGEMENT

The author expresses his great acknowledgement to the supports of Mechanical Engineering

Department of Southern Methodist University, Dallas, Texas and Edison Welding Institute,

Columbus, Ohio.

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29

OPTIMIZATION OF PGMAW USING ONLINE OBSERVATION

AND STATISTICAL DATA

S. Nordbruch A. Graser ^

ABSTRACT

In this paper a monitoring system that simplifies the finding of optimal welding parameters, the

analysis and the optimization of pulsed gas metal arc welding (PGMAW) is described.

The system allows the visual online observation of all states of the welding process, including

the droplet transfer, without an additional lighting unit. Additionally, the synchronized measurement of the welding current and welding voltage signals during image recording and the extraction of characteristic parameters of the signals is possible. Furthermore, the system allows the

visual analysis of the material transition images.

For an analysis and optimization of the process the systems computes statistical data of all collected and calculated visual and electrical data of a recording sequence.

KEYWORDS

PGMAW, droplet transfer, online observation, visual data, electrical welding data, statistical data

INTRODUCTION

The pulsed gas metal arc welding process is an important component in many industrial and

manufacturing operations. It is highly suited to a wide range of applications. Due to the complex

processes, the extreme brightness of the welding arc, the high number of different welding tasks,

etc., the finding of optimal parameters, the test of new welding parameter combinations or the

analyses by process errors is difficult.

For the solution of the problems the visual observation of the droplet transfer in combination

with the measurement of electrical welding parameters is an approach. Typically, the droplets

should be even and in uniform size and the material transition should be splashless.

The visual observation of the material transition has been used extensively. Normally digital

Charge-Coupled-Device (CCD) high-speed cameras in combination with an optical laser are used. Due to the extreme brightness these approaches are using the shadowgraphing technique,

described by Allemand et. al. (Ref. 1).

For the mentioned problems the systems are unsuitable due to a set of disadvantages. The most

important are: » The necessity of the lighting unit and the limited possibilities of observation caused by this

(shadowgraphing technique). » The acquisition and maintenance costs are very high.

Friedrich-Wilhelm-Bessel-Institut Forschungsgesellschaft m.b.H, Postfach 106364, 28063 Bremen, Germany

University Bremen, Institute of Automation (lAT), Kufsteiner Str. NWl, 28359 Bremen, Germany

31
SYSTEM

The monitoring system consists of a High-Dynamic-Range-CMOS camera (HDRC) with an external trigger input, an intelligent measuring board for the triggering of the image recording and

the synchronized and simultaneous measurement of electrical welding parameters, a signal processing unit for the automatic calculation of characteristic welding process parameters, an image

processing unit for the automatic visual analysis of the droplet transfer images and a statistical

unit. Due to the properties of the HDRC camera the system requires no additional lighting unit.

The basic set-up of the system is shown in figure 1,

Figure 1 : Experimental set-up

High-Dvnamic-Range-CMOS Camera (HDRC)

HDRC cameras use an image sensor with a brightness dynamic of approximately 10^:1. This

corresponds approximately to the intensity difference of the welding process. HDRC cameras are therefore able to observe all states of the material transition in all welding positions without an

additional lighting unit.

The principle of HDRC cameras can be described as follows. The level of intensity, i.e. the

color/gray values of an object, are essentially dependent on the exposure rate and the material

properties of the object with its reflection properties. The information of an object is essentially

dependent on the contrast. A CCD camera maps the absolute color/gray values caused by respective irradiation in the image. As opposed to the CCD camera the HDRC camera maps the contrast, which is caused by different reflections. I. e., brightness conditions (color/gray values of

the object) are independently represented by the intensity of illumination as constant number

differences. Before further processing, this high intensity dynamic is compressed logarithmically

in every cell of the image sensor just like in the human eye. Due to this compression, the information content of the image that is included in the contrast is not reduced.

A further advantage of HDRC cameras is, that at every time each sensor cell can be accessed

independently of all others. The constraint of CCD cameras that must read out complete images

does not apply for HDRC cameras. This property of HDRC cameras allows the selection of sub

areas of the image sensor instead of the complete image. Considerably higher image recording

frequencies become possible.

Principle of Image Recording and Measurement of Electrical Welding Parameters

The system uses the periodicity of the droplet transfer of the PGMAW process. For the observation of the regularity of such processes it is sufficient to take one picture per period of the same

32
process phase. In order to observe the exactly same process phase in each case (also at period

times varying), the image recording is synchronized on an electrical welding parameter and is

not carried out in fixed time intervals. The image recording starts after a trigger criterion is detected and an additional variable delay time (see figure 2).

Figure 2: Procedure of image and electrical welding parameter recording

Due to the variable trigger criterion and the variable delay time, every state of the welding process (the melting of filler wire through the arc, the detachment of the drop and finally the immersion of the drop into the welding bath) can be observed in online mode, even if this state itself

has no unambiguous trigger criterion.

With periodically recorded images, the virtual picture of a quasi-stationary process, i.e. of a stationary drop is generated. Irregularities within the welding process are immediately recognized

as different images in a series. The influence of varying welding parameters on the process is

immediately visible.

Furthermore the variable delay time between the trigger criterion and the trigger impulse for the

camera in one recording series can be continuously enlarged. In this case a virtual picture of a

continuous process, i.e. of a virtual droplet transfer through the sequence of the different consecutive drops, is recorded.

Simultaneous to the image recording the welding current and voltage are measured. The measurement starts at the trigger criterion and holds after a variable time within the period of the

process (see figure 2).

Signal Processing

The signal processing unit allows the automatic calculation of the following typical characteristic

PGMAW process parameters from the measured current and voltage signals (see figure 3). » Pulse current Ip and voltage Up

» Background current Ig and voltage Ug

» Mean value (of the period) of current /;;, and voltage Um

» Pulse tp and background tg time » Period time tges and frequencyfges

Additionally, the unit calculates specific parameters of the used welding supply (Ref 2). The

supply generates current and voltage signals that contain a so-called 'backpack' in the falling

edge of the pulse (see figure 3). The calculated special parameters are: » Backpack current Ir and voltage Ur

» Backpack time U

1 = variable trigger criterion

2 = delayed image recording = measurement of electrical

welding parameters

•-t

2

33
Figure 3: Calculated electrical welding parameters

Image Analysis

For further conclusions onto the process the droplet transfer images are analyzed visually. The

image processing unit allows the automatic detection and geometric measurement of all objects

in the images and their classification. The unit is divided into the classical image processing parts

preprocessing, segmentation, feature extraction and classification.

Due to the extreme brightness conditions and the characteristics of the HDRC camera, objects

within the arc differ only by few gray levels from the background and objects outside of the arc

clearly visible against the background (see figure 4). The images cannot be analyzed with standard image processing techniques. Pre-processing is essential. For pre-processing a procedure is

necessary, which is robust against varying image contents (e.g. arc brightness). A procedure was

developed, which enables the „recognition" of the high-contrast areas that have to be maintained

and low-contrast areas which have to be improved. Additionally pre-processing contains a compensation of the locally varying lighting influences, so that the result image of the pre-processing

only shows the contrasts with the interesting information. The segmentation is carried out regionoriented by the calculation of an optimal gray level threshold for each image. The feature extraction part detects the objects and measures their geometry (volume, size, width, height, perimeter,

among other things). With a statistical classification procedure, which characterizes the object

classes by distribution densities, the objects are assigned to the given object classes („drops",

„splashes" or „unknown") according to the criterion of the maximal probability. Figure 4 demonstrates the steps of the image analyze.

Figure 4: Image processing: a.) Original image b.) Image with contrast enhancement and shading

corrections c.) Image after threshold segmentation d.) Image with detected and classified object

34
Statistical Data

The statistical unit computes the following data of all measured and calculated visual and electrical parameters of a recording sequence. » Minimum, maximum and range » Mean and median

» Standard deviation and variance » Linear deviation dimension

» Coefficient of variation » Histogram chart » Modal value(s) of the histogram chart » Skewness and kurtosis » Regressions line (trend analysis)

The unit also performs sequential analysis, which allows the user to analysis and optimizes the

process.

Results

The efficiency of the system is illustrated by experimental data . Figure 5 presents the quasistationary process through the recording of several different drops at the same states of the process. The first three images (figure 5a, 5b and 5c) of the sequence show an even droplet transfer.

The drops have approximately the same size and shape and are nearly at the same position. Image 5d shows the consequences of irregularities. The material transition splits up into several

little drops.

Figure 5: Quasi-stationary drop with irregularities

Figure 6 illustrates an example for all measured and calculated data of a material transition. It shows an image of the droplet transfer, results of the visual analysis, synchronized and simultaneously recorded electrical welding parameters current and voltage and calculated characteristic

electrical welding process parameters. Figure 7, figure 8, table 1 and table 2 present examples for

the statistical unit. The figures and tables show the mean values of the individual periods of the

welding current and voltage of a sequence as well as its calculated statistical data.

All images were recorded during weld-surfacing of imalloyed steel with a thickness of 4 mm and a diameter of the

welding wire of 1 ,2 mm. Welding was carried out using a EWM High-Tech Inverter power supply "hitegral Inverter MIG 350 Pulse (Update 2.0)" (Ref. 2) and with a vertical position of the welding torch in tub position. The protective gas consists of 82% argon and 1 8% carbon dioxide.

35
b.

Object Drop

Width [mm] 1,41

Height [mm] 1,49

Size [mm^] 1,57

Perimeter [mm] 4,77

c.

current [ A ] voltage [ V ] 450 45

100 40

350 35

300 30

250 25

I 200 20

150 15

100 10

50 5

d.

4 b —

^—

'f.

• —

*

\ f V

1 X

'

Vr' * I

• *

ij

1—

?

h. .

•

) \—11

1

1000 2000 3000 4000 5000 6000 7000 8000 9000 time after trigger criterion [ ]

Current A Voltage V

Mean 121,7 Mean 25,8

Pulse 434,6 Pulse 40,0

Backpack 152,7 Backpack 29,5

Background 36,3 Background 20,3

Figure 6: Common representation of recorded and calculated data of one period a.) Image of the

droplet transfer b.) Result image of the visual analysis c.) Synchronized and simultaneously

measured electrical welding parameters current and voltage including the time of the image re- cording d.) Results of the calculation of characteristic process parameters

Specifications

The capability of the system is dependent on the employed components (model of the processor,

the main memory of the computer, etc.), the size of the image area and the monitoring modes

(online/offline observation and display and online/offline analysis).

On a computer with two Pentium at III 850 MHz, an image area size of 256 x 256 pixel and the

operating system Windows NT 4.0 for example

• a visual online observation and display of the droplet transfer with 160 pictures per second,

• a simultaneous and synchronized measurement of the welding current and voltage with offline display,

• offline analyses (visual, characteristic process parameters and statistical data)

• and the saving of all data in the main memory,

are possible.

36
number of recorded periods current (mean values) from 86,76 A to 1 14.76 A

Figure 7: Mean values of the individual periods of the welding current of a recorded sequence

and its calculated statistical data a.) Graph of the mean values including regressions line b.) Histogram chart c.) Statistical data (selection)

Mean 105,87 A Minimum 86,76 A

Median 105,88 A Maximiim 114,76 A

Modal 104,96 A Range 28,0 A

Variance 24,42 A Coefficient of variation 0,04 A

Standard deviation 4,94 A Linear deviation dimension 3,43 A

Table 1 : Statistical data (selection) of mean values of the individual periods of the welding current of a recorded sequence

Figure 8: Mean values of the individual periods of the welding voltage of a recorded sequence

and its calculated statistical data a.) Graph of the mean values including regressions line b.) Histogram chart c.) Statistical data (selection)

Mean 21,50 V Minimum 20,56 V

Median 21,25 V Maximum 29,13 V

Modal 20,98 V Range 8,57 V

Variance 2,22 V Linear deviation dimension 0,59 V

Standard deviation 1,49 V Coefficient of variation 0,06 V

Table 2: Statistical data (selection) of mean values of the individual periods of the welding voltage of a recorded sequence

37
SUMMARY AND CONCLUSIONS

The sensor system described in this paper allows the online visual observation of all states of the

PGMAW process, including the droplet transfer, without additional lighting. The image re- cording is synchronized to an electrical welding parameter.

Additionally, with the system the simultaneous and synchronized measurement of electrical

welding parameters and the calculation of characteristic process parameters is possible.

Furthermore, the system is featured with an image processing unit for the automatic detection,

geometry measurement and classification of the material transition. With this unit, visual 2-D

features of the material transition can be directly assigned to the electrical welding parameters.

For an analysis and optimization of the process the systems computes statistical data (extreme

values, mean values, standard deviation, etc.) of all measured and calculated visual and electrical

data of a recording sequence.

Presently, the optimization is restricted to the online observation of the images of the droplet

transfer and the offline analysis of all measured and calculated data.

The system can be used

» for the fast and economical adjustment of suitable welding parameters prior to manufacturing, » to test different combinations of parameter settings, filler metals, inert gases, etc., » and the development of welding devices.

ACKNOWLEDGMENT

This project is supported by the German Federal Ministry of Economics and Technology

(BMWi) via the "Arbeitsgemeinschaft industrieller Forschungsvereinigungen (AiF)" (No. 12583

N/1).

REFERENCES

1. AUemand, CD.; Schoeder, R.; Ries, D.E.; and Eagar T.W. 1985. A Method of Filming Metal

Transfer in Welding Arcs, Welding Journal (64) (1): 45-47.

2. EWM High-Tech Precision, Miindersbach, Germany. Welding supply type: Integral Inverter

MIG 350 Pulse (Update 2.0).

3. Cheremisinoff, N. P. 1987. Practical Statistics for Engineers and Scientists. Technomic Publishing Company, Lancaster.

38
THROUGH THE ARC TRACKING OF 5G NARROW GAP PIPE WELDS

T.S. Rajagopalan*, P.A. Tews*

ABSTRACT

A through-the-arc joint tracking system was developed for use with mechanized orbital pipeline

welding equipment designed to weld pipeline girth welds in the 50 position. Although through

the arc joint tracking has been successfully applied to lap and fillet welds in the IG and 2G

positions, and is offered by many robotic manufacturers, through the arc tracking of pipeline

girth welds in the 5G position is new. The system was successfully used to aid welders in

welding over 8200 joints of 42 inch (1.06m) diameter .417", .500" and .600"(10.6 mm, 12.7 mm,

and 15.2 mm) wall thickness grade X-70 line pipe during the summer of 2000. The mechanized

equipment utilized the solid wire pulsed gas metal arc process with Argon-C02 shield gas. The

joint design was a compound bevel narrow gap with 5-degree sidewalls. Voltage and current

waveforms were recorded along with torch position and timing signals using a high-speed

multichannel data acquisition system developed specifically for this research. Analysis of the

recorded waveforms enabled the sample timing to be optimized. The recorded waveforms show

the effect of joint depth and oscillation width on the arc voltage waveforms. Provisions were

made to vary the torch to work target value as a function of position around the pipe, but it was

found that this feature was unnecessary. Provisions were also made to vary the sensitivity and

speed of correction for each weld pass, but this too was found to be unnecessary. Optimum

sensitivity and maximum correction values were developed that worked for all fill passes. This

reduced weld repairs on this project by enabling the use of an optimum oscillation width. Too

wide an oscillation width caused excessive sidewall melting, which increased weld pool fluidity,

creating an improper weld contour in the vertical position. Too narrow an oscillation width

caused lack of sidewall fusion defects. Through the arc joint tracking eliminated these problems

by centering the torch oscillation pattern. This enabled the use of an oscillation width wide

enough to eliminate lack of sidewall fusion defects, but narrow enough to minimize dilution

KEYWORDS

Mechanized welding, Through-the-arc tracking, GMAW-P

INTRODUCTION

In mechanized welding of cross-country pipelines, guide bands are temporarily clamped around

the outside of the pipe near the joint to be welded. Devices referred to as welding bugs are temporarily clamped onto the guide bands. The welding bugs travel around the pipe on the band.

The bugs have a wire feeder mounted either on the bug or nearby. When the wire feeder is

mounted on the bug, small (10 pound or less) spools of welding electrode are carried on the bug.

When the wire feeder is mounted nearby, such as on the wall of a welding shelter, 30 pound

*CRC-Evans Automatic Welding, Houston, TX, 77086

39
spools of welding electrode are used. In this case, the wire feeder feeds the welding electrode

through a cable-hose to the welding torch on the bug. The bugs also provide a means for

oscillating the torch across the joint, either using a shuttle type oscillator or a pendulum type

oscillator. If a shuttle type oscillator is used, the center of oscillation is controlled by the shuttle.

If a pendulum oscillator is used, an additional motorized horizontal slide must be provided to

move the center of oscillation to the right or left. The pendulum oscillator itself cannot be used,

because the angle between the torch and the two joint sidewalls must be equal. Welding bugs

typically have a motorized axis for moving the torch toward or away from the surface of the pipe

to maintain the desired contact-tip-to-work distance.

In pulsed arc welding with a constant wire feed speed, the welding power supply varies the

voltage at the contact tip to maintain the desired pulsed current waveform. When the contact tip

to work distance increases, the welding power supply increases the voltage applied to the contact

tip in order to maintain the desired current waveform. Conversely, when the contact tip to work

distance decreases, the welding power supply decreases the voltage applied to the contact tip so

the welding current will not increase beyond the desired waveform. This relationship can be

used to provide a measure of both the contact tip to work distance and the center of the

oscillation pattern with respect to the sidewalls. By comparing the measured arc voltage as it sweeps across the center of the joint to a target arc voltage, the result of the comparison can be

used to move the torch upward or downward to constantly balance the measured arc voltage

against the target voltage. Similarly, the voltage measured when the torch is melting one

sidewall can be compared to the voltage measured when the torch is melting the other sidewall,

and this comparison used to maintain the center of oscillation equally between the joint

sidewalls. The pulsed arc process was used in the work reported on in this paper.

In short arc welding with a constant wire feed speed and constant potential welding power

supply, the welding current increases as the contact tip to work distance decreases. The same

principals described above can be applied to measurements of welding current to maintain a

desired contact tip to work distance and maintain the center of oscillation equally between the

joint sidewalls.

PROCEDURE

Welding was carried out on 42" (1.06m) diameter 0.563 inch wall grade X70 pipe using 1.2mm

ER70S-6 welding electrode with 85%Argon-15%C02 shielding gas. As stated above, the gas

metal arc pulsed welding process was used. The electrode was fed at a constant rate for each fill pass. Travel speed was constant for all except the last pass. A manual travel speed override of

+/- 25% was permitted for the last pass to accommodate variations in joint gap, which affect

joint fill. Remote wire feeders were used with 12 foot cable hoses and water cooled welding

torches. Welding electrode was supplied on 30 pound spools. Figure 1 shows the welding

shelters used on this project. The shelters rest on the pipe, and contain the welding bugs, wire

feeders, and control cabinet. The welding power supplies were mounted on the rear deck of the

shelter support tractor, and powered by an alternator driven by the tractor power take-off.

Each of the welding bugs had two welding torches. Each torch was independently oscillated across the joint by means of a shuttle type oscillator driven by a brushless DC servo motor and

40
ball-nut lead screw. The torches were moved towards or away from the joint by means hollow

shaft stepper motors with brass nut lead screws.

S-i-t-U

Figure 1 Typical Welding Shelter with Pipe, Bugs, and Control Cabinets

Figure 2 is a block diagram of the overall bug control system. Five host microprocessors with

peer-to-peer communications were used in the system. The host microprocessor used was an

Intel 80C152, a variety of the popular 8051 family of microprocessors, with an on board CSMACD serial communications channel. An RS-485 type twisted pair communications link operating

at 1.3 megabaud connected the host microprocessors to one another. There were two stacks of

boards in the control cabinet for controlling the wire feeders, travel motor, oscillating motors,

and vertical adjust motors for the two torches. Each stack contained a power supply board, host

CPU board, and four daughter boards. One daughter board controlled the encoder motors used

for wire feed and travel. A second daughter board controlled the stepper motors used for vertical

adjust. A third daughter board controlled the bushless DC motor used to oscillate the torch across the joint. The fourth daughter board digitized the arc voltage and arc amperage from the

associated torch. A third host microprocessor was built into the hand held pendant used to

operate the welding bug. Two more host microprocessors located on the support tractor had

daughter boards connected to them for controlling the welding power supplies and monitoring

the arc voltage and current for quality reporting.

Figure 3 shows a block diagram of the board used to control the brushless DC motor for torch

oscillation. The board contained an Atmel AT-89S8252 microprocessor, a Precision Motion

Devices MC-1231 motion control chipset, and Linear Devices LT-11581 FET drivers. The

Atmel controlled the PMD chipset, overseeing the oscillation motion, recomputing the trajectory

in response to host commands to change rate, width, torch center of oscillation, right dwell, or

41
BLOCK DIAGRAM OF P -5 00 CONTROL SYSTEM

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Figure 2 Block Diagram of Control System

left dwell. The Atmel microprocessor set bits in a status word when the oscillator reached

programmable percentages of the cross seam distance. The host microprocessor interrogated the

Atmel microprocessor 1000 times per second to detect these status word bit transitions. When

42
detected, the host scheduled the appropriate tracking task, which would normally read the analog

to digital converter data or start the analog to digital converter.

BLOCK DIAGRAM O F S H U TT LE O SC ILLA TO R

BRUSHLESSDCSERVOBOARD

BLDC MOTOR

Figure 3 Block Diagram of Oscillator Daughter Board

Figure 4 is a block diagram of the arc voltage and current measurement daughter board. Analog

values from sense wires and shunts were sampled 40,000 times per second and digitized using

Hewlett Packard HP7800 isolation amplifiers and a 12 bit Analog Devices LM12454 converter.

An Analog Devices ADSP2181 Digital Signal Processor processed the converted values. The

board sampled the data for the commanded length of time, and returned the processed values of

the sampled data when queried. One type of processing performed was excluding sampled arc voltage readings from the average when sampled arc current was below peak levels.

BLOCK DIAGRAM OF HIGH SPEED

DATA ACQUISITION BOARD

ARC VOLTS

ARC AMPS

Figure 4 Block Diagram of Data Acquisition Board

The host microprocessor compared the center samples to the target voltage, and used the error times a scaling factor to command the torch to work motor to raise or lower the torch. The

computed move limited to a programmable maximum value per sweep. The computed move

command was based solely on the proportional error, without integral or derivative terms.

Studies were carried out to determine the sampling window that would optimize the comparison

of the voltage drops that occur as the torch and welding electrode approach each sidewall of the

joint. To carry out this research, an eight-channel analog to digital converter board from

National Instruments was purchased. Isolation amplifiers were designed and built to protect the

analog to digital converter board from the welding arc voltage, and to allow arc voltage, arc

current, digital timing signals, and analog processes signals to be measured simultaneously.

43
Timing signals were provided by the Atmel microprocessor on the brushless DC servo motor

board that controlled the torch movement. Arc voltage was measured at the arc, and arc current

was measured close to the arc by means of a clamp-on Hall Effect current probe. Processed

analog values were obtained from a digital to analog converter connected to a serial output on the

ADSP2181 digital signal processor. Waveforms were stored on disk, and were printed out on a

laser printer.

Trials were also conducted to determine optimum attenuation factors and maximum move factors

for vertical and horizontal tracking. Tracking was given time to stabilize and then a step change

was made to either the cross seam or vertical adjust motor. The response time and overshoot of

the cross seam and vertical adjust axis were measured by means of linear potentiometers attached

for this purpose. The optimum values for attenuation and maximum correction per sweep were

selected and used on the pipeline welding project.

Results

Figure 5 shows a typical waveform obtained using the national instruments data acquisition

system. The top trace is the arc voltage, the second trace down is the arc current, the third trace

down is the "late indicator", the bit set by the brushless DC servo motor board Atmel chip

indicating the torch has reached a programmable percentage of its sweep. The bit goes low after

the host CPU detects it has gone high and has sent a command back to the Atmel to clear the bit.

The next trace down is the right wall indicator, another bit set by the Atmel when the torch has

reached its extreme point of oscillation in one direction. The trace below the right wall indicator

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44
is the left wall indicator. The bottom trace is the analog value of the result of the processing

done by the high speed data acquisition board. The data was taken at a rate of 1 80 oscillations

per minute. The time period between the right and left wall timing signals was 333 milliseconds.

The pulse frequency was about 135 Hz. The short segments of the bottom trace represent the

value of the processed arc voltage signal measured from the end of one sidewall to the start of

the opposite sidewall measurement. Because these represent the torch to work measurement,

they are nearly equal in value. The longer segments of the bottom trace represent the value of

the processed arc voltage measured while the torch approached the sidewall. The values are lower because the contact tip to work distance decreases as the torch approaches the side wall.

Figure 6 shows the effect of a step change to the cross seam position. The top trace is arc

voltage, the second, third and fourth traces are the late indicator, right wall, and left wall

indicators respectively. The second trace from the bottom is the processed arc voltage

measurement for the previous period. The bottom trace is oscillation motion measured by a

linear potentiometer. The center of oscillation was intentionally shifted 0.010 inches after the

tracking had stabilized. The time for the oscillation pattern to return is approximately seconds, or 18 oscillations. Figure 8 shows the result of excessive response to the difference in sidewall

measurements. The system overcorrected in an under-damped fashion.

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Discussion

The 12 bit analog to digital converter provides a resolution of arc voltage of 0.024 volts per bit.

The scale factor applied to the torch to work error was equivalent to 0.125 inch of movement per

45
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2 volt difference between measured arc voltage and target arc voltage. This scale factor was

attenuated to achieve the damped response shown in figure 6. The under damped response

shown in Figure 7 occurred when no attenuation was used.

Prior to introducing through the arc tracking on the project site, manually guided dual torch bugs

were welding 90 joints per day with a 9% repair rate. After introducing through the arc tracking,

the repair rate dropped to less than 4%. The most common weld defect that required repairs

without through the arc tracking was lack of sidewall fusion in the second and third fill passes.

The engineering critical assessment required welds with small stacked lack of sidewall fusion

defects be repaired. The cause of this defect was excessive puddle fluidity caused by excessive

weld metal dilution. The excessive weld metal dilution was in turn caused by setting oscillation

width wide in an attempt to insure sidewall fusion regardless of the torch not being centered in

the groove. Through the arc tracking allowed the use of a width that was just wide enough to

touch both sidewalls by maintaining the torch oscillation pattern centered between the sidewalls.

SUMMARY

A through the arc seam tracking system has been developed and applied to a dual torch external

welding bug used for cross country pipe line girth welding. The system was successfully used to

weld 122 miles (196 Km) of 42 inch (1.06 m) diameter grade X-70 pipe. Use of through the arc seam tracking reduced weld repair rate from the 9% to less than 4%.

46
DEVELOPMENT OF ROTATING GMA WELDING SYSTEM AND

ITS APPLICATION TO ARC SENSORS

C.-H. Kim* and S.-J. Na*

ABSTRACT

The sensitivity of rotating arc sensor is related with the dynamic wire melting due to the

insufficient self-regulation of the arc length. This paper presents the dynamic simulation of wire

melting by using the variable space network method and by modeling the heat flux from the

molten end of the wire into the electrode. A new type of arc rotation mechanism with a hallowshaft motor was devised to implement a high-speed rotating arc and used to develop the arc

sensor.

KEYWORDS

GMA welding, Arc Sensor, Dynamic Wire Melting, Rotating Arc

INTRODUCTION

Through-arc-sensing is widely used for automatic seam tracking because of its many advantages

such as the possibilities for real time control, no auxiliary parts around the welding torch, no

need for maintenance and low cost. The arc sensing method uses the electrical arc as a sensor

and is based on signal variations as a function of the CTWD (contact-tip-to-workpiece distance).

Therefore it is generally necessary to weave or rotate the welding torch to intentionally stimulate

the differences in the CTWD. With the conventional torch weaving method the upper limit of

oscillation frequency is about 4-5Hz owing to the mechanical restraint. The arc rotation method

enables a high-speed rotation of arc over several tens of Hz. In Japan and Germany, arc rotation

mechanisms that rotate the electrode nozzle by an external motor have been developed (Ref 1-2).

However, accessibility to the joint location may be limited by the external motor system attached

to the welding torch.

Self-regulation in GMAW occurs due to using a constant voltage power source. The arc length

remains approximately constant for variations in the CTWD, because the time constant of the

self-regulating process is shorter than the torch weaving rate in conventional GMA welding (Ref.

3). However, the self-regulation of the arc length is not fully performed in high-speed rotating

GMAW owing to a rapid movement of the welding torch. Consequently the rotating arc sensor

operates in a dynamic state and the sensitivity of the sensor is greater than that of the

conventional weaving arc sensor

In a static state, the electrode melting model was confirmed experimentally by Lesnewich in

1958, and was proved theoretically by Halmoy in 1979 and experimentally by many researchers.

* Dept. of Mech. Eng., Korea Advanced Institute of Science and Technology (KAIST), 373-1 Kxisung Dong,

Yusong Gu, Taejeon, Korea (South)

47
Recently, dynamic wire melting models have been developed using an energy balance approach

which uses an 'action integral' and neglecting the heat conduction along the electrode wire (Ref.

4). However, Kim et al. addressed that the Peclet number is not high enough to neglect the

thermal conduction (Ref. 5). They modeled the heat transferred to the melting tip of the solid

electrode from two sources : the heat transferred from the molten end of the wire, and the heat

directly delivered to the solid electrode by electron condensation.

In this study, a dynamic model of the GMA welding process and an arc rotation mechanism were

developed for high-speed rotating arc welding. The mathematical model includes :

(1) dynamic model that predicts the electrode melting by considering the heat conduction in

the electrode and the heat transfer by condensing the electrons directly from the welding

arc to the electrode

(2) heat transfer model that describes the heat from the molten end of the electrode wire to

its solid ends

(3) simplified weld pool model under assumption that the arc rotates very rapidly

The developed arc rotation mechanism was used to improve the weld quality and to develop an

automatic seam tracking sensor. It could improve the accessibility to the joint and adaptability to

the conventional welding torch system.

ARC ROTATION MECHANISM

A schematic diagram and a photograph of the developed arc rotating mechanism are shown in

Fig. 1. The mechanism includes a hollow-shaft motor, an eccentric tip and 3 carbon brushes. The

electrode wire is deflected circularly by an eccentric tip that is rotated by the hollow shaft of

rotating motor and galvanized through the carbon brushes. This mechanism can be installed

inside the electrode nozzle and connected directly to a conventional torch system. The weight

and size of the nozzle can be reduced considerably by using an adequately small motor.

(a) Schematic diagram (b) Photograph

Figure 1 : Devised rotation mechanism

48
The arc positions are defined in Fig. 2(a) and the images of 20 Hz rotating arc taken by a highspeed camera at various arc positions are shown in Fig. 2(b). The arc length seems to be shorter

at Cr than at Cf, because the mohen pool is formed at the position of Q.

MODEL OF GMA WELDING SYSTEM

Figure 3 shows arc heating at the electrode and the equivalent electrical circuit of conventional

GMA welding system having a power source with constant-voltage characteristics. For a

dynamic state the one-dimensional conduction equation in electrode wire is given by

l.{k{T)^) + p^{T)f +F = C^{T)p{T){w^ + ^-) (1)

and the dynamic wire melting model is given by the following expression.

_ k{T) ^

+

^= iw- dL, I dt)AHp{T) (2)

In Eq. 2, Qm can be calculated from the temperature distribution of the electrode and Waszink's

experimental results (Ref. 6). From Fig. 4, Qm/I shows a linear relation with the Peclet number

for spray and globular transfer mode. The charcteristic length of the Peclet number is S/Le ,where

S is the cross-sectional area of wire and Lg is the wire extension length. S is in proportion to the

volumetric melting rate and Lg to the Joule heating inside the electrode.

Equation 1 is a kind of one-phase Stefan problem, and for low Peclet number the conduction heat

transfer along the electrode axis is not negligible. To analyze this one-phase melting

phenomenon, the Murray-Landis method (the variable space network method) is used. For each

iteration step, the electrode is divided into the same number of equally sized space increments,

which expand or shrink in time as shown in Fig. 5 (Ref .7).

49
Power Source model

Qs, heat from side

condensation Qm, lieatfrom

molten end

Contact tube

Lp Rp

Electrode extension

Ve = Re I

Arc voltage

I Va=Vao+RaI+(Eal+Eai I)La

workpiece

(a) Wire melting model (b) Euqivalent circuit of GMA welding system

Figure 3: Mathematical modeling of GMA welding system

• 1 .2 (> wire - spray

* 1 .6 (> wire - spray

4-

2-

1-

1 .2 ()> wire - globular

1 .6 <) wire - globular

Pe (=w(S/LJ/a)

(a) Spray transfer mode

Pe{=w(S/L»

(b) Globular transfer mode

Figure 4: Heat flux entering the solid wire from the molten end

I z ^ Le*

Tm

k* step

k+lTm

(k+l)* step

Figure 5: Variable space network

50
The governing equation for the whole loop of the welding circuit can be written as

dt L^+L^ L^+L^ L,+L^

where the elecrical resistance in the electrode(i?e) is calculated as follows.

= f-^^^ (4)

The welding current signals were simulated by using eq. 1-4 and compared with experimental

ones as shown in Fig. 6. To minimize the effect of weld pool shape, the amplitudes of welding

current were measured for bead-on-plate welding with sinusoidal variation of torch height.

Figure 6: Sensitivity of arc sensor for bead-on-plate welding with torch height oscillation

In rotating arc welding, the CTWD is considerably affected by the weld pool shape. Especially

for rotating along the rear half-circle, the arc moves above the previously formed pool and

calculation of the CTWD considering the molten pool height at arc position is crucial. Analyses

of dynamic 3-D GMA weld pool are regarded as of major importance but are so sophisticated

and time-consuming that it is beyond the scope of this paper.

Under the assunption that the arc rotates very rapidly, a simplified 3-D quasi-steady model of

molten pool was adopted in this study. The previous work has demonstrated that the conduction

model, without consideration of fluid flow, can predict the shape of weld pool boundary in the

vicinity of arc with reasonable accuracy (Ref. 8). A quasi-steady conduction model was used for

calculation of the CTWD during high-speed arc rotation. Welding current waveforms were

simulated by solving the governing equations (1-4) and the simplified weld pool model. For

example, experimental and simulated results under 25 Hz rotation speed are shown in Fig. 7.

Although the experimental welding signals contain high-frequency fluctuations, their basic

waveforms can be predicted reasonably well by the simulations.

51
350

300

250 S 200 c

b

o

150

100

50

0.0

L C R C. L

40 ms

L U

—I

0.1 0.2 Time (s)

(a) Simulated results

350

300

250-

<; 200-

I

150 " 100-

50-

L C, R C, L

- raw signal

' filtered signal

40tns . L L

0.0 —I0.1 0.2 Time (s)

(b) Experimental results

Figure 7: Simulated and experimental welding current waveform for V-groove welding with

25Hz arc rotation

ROTATING ARC SENSOR

By self-regulation of the arc length, the variation of the CTWD generates the change of the

electrode extension in conventional torch weaving system. The weaving arc sensor has relatively

poor resolution and limitation of application to the aluminum welding because the resistivity of

Al electrode is much lower than that of the welding arc. But by the change of the arc length due

to the variation of the CTWD, the rotating arc sensor has an improved sensitivity as shown in Fig.

6, and can be applied to the aluminum welding.

Arc rotation

position

Figure 8: Arc sensing algorithm

When the welding torch is positioned at the center of groove, the current waveform becomes

symmetric at Cf. If the welding torch is deviated from the center of groove, the current waveform

changes to asymmetrical shape as shown in Fig. 8. The area difference (Sl-Sr) shows a linear

relationship with the offset distance as in Fig. 9 and is adopted for the error criterion. The

rotating arc sensor can track the weld seam by using the correction data proportional to the area

52
difference. Figure 10 shows examples of the automatic weld seam tracking for mild steel and Al

5052 respectively. The weld seam could be tracked with an accuracy of 0.07 mm maximum error and 0.035mm standard deviation of error in the welding of mild steel. The rotating arc sensor

showed a fairly good performance also in the aluminum welding in spite of the slightly increased

error.

1.0

0.S

(O 0.0-

-0.5H

to

-1.0-

-1.5 -3

Experimental results Linear fit

I 99.7% Confidence interval

-10 12

Offset distance(mm)

CO

0.8

0.6-1

0.4

0.2-

0.0-

-0.2

-0.4

-0.6-1

-0.8

Experimental results —Linear lit

I 99.7% Confidence interval

-2 0 2

Offset distance (mm)

(a) Mild steel (b)Al 5052

Figure 9: Correlation between offset distance and area difference

E

E> Hc

og 2 a

% 1

offset 4

<j (error) : 0.035 mm

Max error : 0.071 mm

—I

10 —I

20 —I30 40 —50 Torch position X (mm)

(a) Mild steel

o (error) : 0.040 mm

Max error : 0.088 mm

-1

30 —1—40 Torch position X (mm)

(b)Al 5052

Figure 10: Examples of automatic seam tracking by rotating arc sensor

CONCLUSION

(1) New-type arc rotation system was developed to implement a mechanically high-speed

rotating arc. By using a hollow-shaft motor the rotating mechanism could improve the

accessibility to the joint.

(2) Welding current was simulated by considering the dynamic characteristics of GMA

welding system such as dynamic wire melting, transient temperature distribution of wire

53
and internal resistance and inductance of welding power source. By time domain

simulations the welding current was predicted and used for the development of the arc

sensor.

(3) Rotating arc sensor was developed to track the weld seam with improved sensitivity,

responsiveness and accuracy. Besides the rotating arc sensor could be applied to

aluminum welding successfully.

ACKNOWLEGEMENT

The authors would like to thank the Ministry of Science and Technology, Korea, for the financial

support by a grant from the Critical Technology 21 Project.

REFERENCES

1 Sugitiani, Y.; Kobayashi, Y.; and Murayama, M. 1991. Development and application of

automatic high speed rotation arc welding. Welding International 7 (5): 577 to 583.

2 Dilthey, U.; Oester, M.; and Gollinick, J. 1996. Mechanized metal-gas-shielded arc welding

with high-speed rotary wire electrode. Welding & Cutting 48 (1 1): 866 to 875.

3 Cook, G.E.; Andersen, K.; Femadez, K.R.; Sheperd, M.E.; and Wells, A.M.Jr. 1987.

Electrical arc sensing for robot positioning control. Robotic arc welding. Eds. J.D. Lane:

181 to 216: Springer-Verlag: IFS Publication.

4 Shepard, M.E.; and Cook, G.E. 1992. A frequency-domain model of self-regulation in gasmetal arc welding. Third International Conference on Trends in Welding Research: 899 to

903: ASM International.

5 Kim, Y.-S.; McEligot, D.M.; and Eagar, T.W. 1991. Analysis of electrode heat transfer in

gas metal arc welding. Welding Journal 70 (1): 20-s to 31-s.

6 Waszink, J.H.; and Van Den Heuvel, G.J.M. 1982. Heat generation and heat flow in the

filler metal in GMA welding. Welding Journal 61 (8): 269-s to 282-s.

7 Murray, W.D.; and Landis, F. 1959. Numerical and machine solution of transient heatconduction problems involving melting or freezing. Journal of Heat Transfer 81: 106 to

112.

8 Kim, J.-W. 1991. A studv on the analvsis of weld pool convection and seam tracking bv

considering the arc length characteristics in GMA welding. Ph.D. dissertation. Taejeon,

Korea: KAIST.

54
Keynote Lecture

55

"Lights Out" Welding:

Wliere We Are and Wliere

We Need To Go

Progress towards Complete Welding

Automation

T.P. Quinn

National Institute of Standards

and Technology

Boulder, CO 80305

'^Lights out" welding

« Welding automated to the point tliat no

human intervention necessary

57
Challenges to Automation

» Reproduce the welder's hand motion ^

« Automatically track joint ^

# Adaptively fill large or uneven gaps ^

« Sense weld defects — take corrective

action

#From blueprint to assembly with no

''training"

Welder's hand motion

Articulated arm robots, crawlers, rotation

devices, ...

Faster

-^ More accurate

Untiring

58
Automatically track joint

Laser stripes, Vision systems, Touch

Sensors, Tlirougli-tlie-arc sensors

Integrated into tlie controller software can

tracic tlie joint with two-pass algorithms and

single-pass algorithms

Adaptlvely fill ^

# With the joint tracker signals, software

exists that can effectively handle large or

uneven gaps, while controlling heat input

59
Sense weld defects ¥

#Through-the-arc sensing (no feedback)

^Research-grade weld pool visualization

"i^Through-the-torch (TIG available)

"^ IR, laser stripe (etc.)

«^Weld-pool vibration

From blueprint to assembly

# Least developed area

# Solutions usually unique, proprietary

# Standards necessary

60
A-9 Welding Activity iViodei

USEOA1, Al'lHOk. Rii'i>cj WOHKIV. ki \t>i:K mil 1 i».TL\r KbV 1.0

R[,r OM*.!! MH t) NOTLS: 1 a 1 J * l- 7 U «

Key: UiM iiv)t*i1y Oattas have turttwi ilpccnnpoeiiions. biacf boifwntr/c no

lu)tl)oi doconipv^iUon. DRAFT WORKING GROUP DOCUMENT

ASS An; Wektig Uartutactiirtru) Adfvtv '^torkl

Design

Maintain

Welding

Procedure

Database

Weldment

Requirements"

Design

Weldment

61
Possible Standards

# Welding Procedure

A9.1 Early database standard

Considering withdrawal

4 Little interest in standard database interface

# Weld Overlay Standards

-V Interchange Welding Overlay between CAD

systems

Interest unknown

Manufacturing Planning

Select Weld

Process,

parameters &

procedures

Plan Motion,

Sensor

Strategies

Simulation

Weldi

PrO'

62
Possible Standards

# Annotated Weld Overlay

"4 CAD Description

-5 Interest unl<nown • Weldment Program

4 Output of an offline programmer — to

interface with welding cell

#Interest from shipyards

4' Navy sponsored document available for

standard

63
Possible Standards

# Sensor Data

-3 Database interface for quality documentation

and statistical process control

Scope could include inspection reports

-^Interest unknown

Intra-cell Network

System

Controller

64
Intra-cell network (cont.)

• Idea is to have a standard hardware and

software connection along all communications

paths

# Advantages:

"^ Easier integration for complex systems

«^PIug-and-play for simple systems

"^Network cabling simpler

»#^Not limited to a proprietary system or proprietary

components

Intra-cell network (cont.)

# Disadvantages

Proprietary systems are well established

Which network protocol to use?

# Interest

Large consumers of cells universally interested

Power source manufacturers also interested

Robot producers lukewarm

^^Integrators ??

65
status

•A9.4 based on ''ArcLink" in development

Ready for balloting

#DOJ review underway

Controversy over perception of AWS

''endorsing" one network over another

#A9.? (or ODVA) based on a DeviceNet

standard under consideration

The Pitches

# standards development can lead to

faster time-to-market

# AWS needs input from those most

affected

# More people and energy required

66
Contact Information

# A9 Chair

Bill Rippey

Telephone 301.975.3417

NIST

100 Bureau Drive, MS 8230

Gaithersburg,MD 20899-8230

william.rippey@nist.gov

67

Session A2: Sensing and Control II:

GMAW and GTAW

69

A SYSTEM FOR REAL-TIME CONTROL OF

GAS METAL ARC WELD PROFILE

D.M. Barborak*, H.W. Ludewig^ R.W. Richardson^, D.F. Farson^ and S. Yurkovich^

ABSTRACT

The objective of this research was to investigate the use of a unique, hybrid robotic Gas Metal

Arc Welding control system for robustly controlling weld shape of single-pass fillet welds in the

presence of common production perturbations and disturbances. Such a system could improve

the fatigue properties of weldments, increasing service life and decreasing design requirements.

A hybrid robotic control system was developed consisting of a 6-axis articulated arm welding

robot, laser-based machine vision system, and weld profile controller. The control architecture

consisted of integrated feedforward and feedback, multi-input/multi-output control loops that ran simultaneously to perform joint finding, joint tracking, modified fill, contact-tip-to-work

(CTWD) regulation, and weld symmetry control. Complex weld profile features such as weld

symmetry and weld profile were addressed as reference tracking and disturbance rejection

control problems.

KEYWORDS

Robotic GMAW, Fillet Weld, Weld Geometry, Weld Profile, Feedback Control, Feedforward

Control, Adaptive Fill, Joint Finding, Joint Tracking, Weld Symmetry Control.

INTRODUCTION

The surface profile of a weld deposit and presence of any discontinuities are very influential to

its fatigue performance. Robotic arc welding relies on process set points derived during the

development of an optimal welding procedure to produce a desired weld profile with minimal

discontinuities. Classified as open-loop control, this technique assumes all the process inputs

and disturbances remain fixed and produce a repeatable output in terms of weld quality. Any

error, change in plant dynamics, or disturbance to the process is likely to cause deterioration in

weld quality which may require expensive rework or cause premature failure.

The objective of this research has been to investigate the application of a hybrid GMAW control

system for robustly controlling the weld profile of single-pass fillet welds in the presence of

common production perturbations and disturbances. The purpose of this is to control fatigue

properties of weldments to increase service life or decrease design requirements. It has been

shown that fatigue properties are strongly affected by weld geometry and discontinuities '

. For

fillet-welds, this includes all geometrical surface aspects of the weld face and weld root

including any discontinuities. This application of a hybrid weld profile control system requires

integration of multi-input/multi-output, feedforward and feedback control loops to perform joint

tracking, adaptive fill, contact-tip-to-work (CTWD) regulation, and weld symmetry control.

Complex weld profile features such as weld symmetry and weld profile are addressed as reference tracking and disturbance rejection control problems. Robustness to disturbance

* WeldWare, Inc., Columbus, Oh.

^ Caterpillar, Inc., Peoria, 111.

^ The Ohio State University, Columbus, Oh.

71
rejection of common production perturbations such as tack welds, root gaps, variable heat

sinking, joint deviations, and changes in CTWD are considered. This research promises to lead

to better control of weld properties beyond what has been previously accomplished.

WELD PROFILE CONTROL SYSTEM

The control system developed for this research implemented a 6-axis welding robot interfaced

with a laser-based, structured light machine vision sensor (Figure 1). The welding robot is a

standard ABB IRB2400 6-axis articulated arm robot with 84 controller interfaced to a Lincoln

DC600 power supply. A Servo-Robot M-Spot 90 laser sensor with CAMI II controller is utilized

for in-process sensing. The M-Spot 90 laser sensor was modified to output 1 OOmW visible laser

in order to increase image-processing robustness during welding. An industrial PentiumTM based

PC is utilized for the weld profile controller.

Three separate software programs were developed

to run simultaneously on the three separate

computers to comprise the weld profile control

system. The robot program is written in the

RAPID language and runs on the robot controller.

The RAPID program controls the overall

sequence of the welding operation along with

manipulation of the robot arm and communicates

with the ADAPT and WEGE programs. The

modified fill algorithm, which runs in the

feedforward loop, is written in the ADAPT

language developed by Servo-Robot and runs on a

dedicated digital signal processor (DSP) in the

CAMI II system. The weld profile control

program is written in C++ and runs on the

industrial PC residing on an ISA bus within the CAMI II system

6-axis robot

Welding

6Power

Supply

Link

Proprietaty

Robot Controller Weld Geometry

Controller

Figure 1 Components of weld profile

control system

The weld profile controller consists of separate but integrated feedforward and feedback control

loops. The feedforward control loop performs the tasks of joint finding, joint tracking, CTWD

regulation, and modified fill. The feedback control sub-system performs the task of weld

symmetry control.

Feedforward Control Loop

A block diagram for the feedforward loop is shown in Figure 2. All four control tasks rely on

sensory information from the laser-based, structured light machine vision sensor mounted

parallel to the welding torch. The joint finding and joint tracking tasks utilize information about

the joint centroid, calculated during a pre-weld scan of the weld joint to adjust the torch offset

and contact-tip-to-work distance while welding. The modified fill task utilizes information about

the joint such as tack size and gap area, along with the required fillet weld leg length to control

deposition rate during welding by adjusting travel speed, wire feed speed, and arc voltage. All

four tasks in the feedforward control loop utilize linear control with heuristics.

72
Weld

Joint

_1_

Desired

Leg Length

1

Pre-weld

Sensing

Tacic Area

Gap Area

Joint Center

Feedforward

Control

Voltage Power

Supply

Energy Input

WFS, TS

TO. CTWD

Robot

Control

Disturbing

Inputs

Mass Input GMAW

Energy Orientation

Final

Weld

Figure 2 Feedforward control sub-system block diagram

One problem with automatic and robotic arc welding applications is maintaining the proper

alignment of the welding arc (torch) with the weld joint. Dimensional tolerances of component

parts, variations in edge preparation and fit-up, distortion during welding, and other dimensional

variations can affect the exact position and uniformity of the weld joints from one assembly to

the next. Joint finding coupled with joint tracking can overcome these limitations by adjusting

the welding torch trajectory relative to the weldment as the welding torch proceeds along the

joint. Joint finding and tracking technology is commercially available and will not be discussed

in detail here^'"*.

During the joint finding task, the robot scans near the perceived beginning of the weld joint in a

pre-programmed pattern to locate the exact beginning of the weld joint. The entire

preprogrammed robot path is then globally offset by the difference between the pre-programmed

starting point and actual joint location.

Next the robot places the welding torch at the weld start location and conducts a pre-weld scan of

the joint for the joint tracking and modified fill tasks. The robot moves in the pre-programmed

direction while the laser sensor scans the weld joint, gathering imaging information about the

joint location and volume. As the torch proceeds along the weld joint, the torch offset and

contact-tip-to-work distance changes are continuously calculated and saved to a path file for later

playback during welding. This new tool center point (TCP) is calculated every 2.5mm (0.1 inch)

along the joint. In addition to the joint tracking and CTWD calculations, information about the

weld joint shape is processed and utilized for the adaptive fill task.

The image processing technique used for the joint

finding and joint tracking tasks are diagrammed in

Figure 3. The laser sensor scans the T-joint designated

by the continuous plate (cp) and the discontinuous plate

(dp). The image is processed to find two line segments,

namely Isl and ls2. At the intersection of these line

segments is the joint root, which is designated as the

tracking point. The tool center point or tcp is then

calculated perpendicular to each line segment, and a

fixed distance defined as the contact-tip-to-work

distance from the tracking point.

Laser Sensor

T-JoInt Processed Image

Figure 3 T-joint showing joint

finding/tracking attributes.

Adaptive fill control compensates for changes in the required volume of welds due to variations

in part dimensions, edge preparation, fit-up, tacks, and distortion during welding. Utilizing

machine vision, the adaptive fill controller measures the joint dimensions and volume, then

73
determines changes needed in welding variables to compensate for changes in the joint

geometry. Such changes in wire feed speed, arc voltage, and or travel speed adjust the weld

deposition rate while maintaining arc stability and the desired weld metal transfer mode.

A customized version of adaptive fill control call "modified fill" was developed for this research.

The modified fill algorithm operates similar to the standard adaptive fill algorithm in that travel

speed, wire feed speed, and voltage are varied to compensate for changes in the required

deposition area due gaps and tacks. The standard adaptive fill algorithm is designed to maintain

a constant amount of weld reinforcement while maximizing productivity by utilizing the fastest

travel speed feasible. The modified fill algorithm is designed to maintain the minimum required

amount of weld reinforcement while producing an optimal weld profile, conducive to improved

fatigue performance.

The image processing technique utilized in the

modified fill algorithm is shown in Figure 4. As

the laser sensor scans the T-joint formed by the

continuous plate (cp) and the discontinuous plate

(dp), it may encounter gaps or tacks. The imageprocessing algorithm detects the presence of gaps

and tacks and measures their size. The modified

fill algorithm then calculates the required

deposition rate based on the desired fillet weld leg

length, and the presence of a gap or tack.

Laser Sensor

tack leg^

length width

T-JoInt Processed Image

Figure 4 Processed image of T-joint

showing modified fill attributes.

'Deposition Area + Gap Area

The required deposition area for a fillet weld with a specified leg length is calculated by:

deposition area = 0.5 x required leg length - tack area + gap area. A 10mm fillet weld in the absence of a gap or tack

requires a 50mm^ deposition area. When gaps

ranging from 0mm to 3 mm are encountered, the

required deposition area increases from 50mm^ to

84.5mm^ in order to maintain a 10mm leg length

fillet weld (Figure 5). Likewise, when tacks

ranging in size fi-om no tack i.e. 0mm to 8mm leg

lengths are encountered, the required deposition

area decreases from 50mm^ to 2mm^. It should be

noted that these calculations are ideal, made in

two dimensions and assume a 100% deposition

efficiency. Although the actual deposition rate is

constant, the resulting weld bead dimensions and

profile will vary along the length of the weld.

60

55

SO

J,S 40

e

.2 35 M

o

a.

25

20

15.

Deposition Area - Tack Area

2 3 4 5 Gap or Tack Size (mm)

Figure 5 Change in required deposition

area for a 10mm fillet weld.

74
The changes in wire feed speed and travel speed for a required deposition area are calculated by:

wire area x

travel speed

, . . wire feed speed x wire area x deposition efficiency

deposition area = — —

200

150

tW

100

100

—1 1 r- —I— 1 1 r-

_l ' I t_ 10 20 30 40 50 60

Fill Area mm^

70 60 90 100

Figure 5 Characterization of the modified

fill algorithm.

For this research, a 95% deposition efficiency was

assumed while using a standard 1.32 mm (0.052

inch) diameter ER70-S3 wire was used with

90%Argon-10%CO2 shielding gas. The optimal

ranges for wire feed speed and travel speed to

produce welds with the desired profiles were

determined experimentally in prior research using

a Design-Of-Experiments (DOE) approach^. The

range for wire feed speed was limited to 89 to 1 14

mm/sec (350 to 450 ipm) and travel speed to 3.0

to 5.08 mm/sec (7 to 12 ipm). Arc voltage was

varied linearly with wire feed speed within the

range of 35 to 40 volts. It should be noted that

this range for wire feed speed is considerably

narrower than that typically utilized in the

standard adaptive fill algorithm. Likewise, travel speed was slower and narrower, and arc voltage was narrower and higher. The characteristic behavior of the modified fill algorithm is

shown in Figure 5. Initially, in the low deposition area, the algorithm minimizes wire feed speed

and adjusts travel speed. Once the minimum travel speed is encountered, the algorithm begins to

increase wire feed speed. Based on the requirements for a 10mm leg length fillet weld discussed

in the previous paragraph, the adaptive fill algorithm will slow travel speed in the presence of

tacks upto about 3.4 mm in leg length where the limits were reached and minimal deposition

occurs. For gaps, the algorithm provides maximum deposition with wire feed speed at its

maximum and travel speed at its minimum.

Feedback Control Loop

The feedback control loop, shown in Figure 6, performs the task of weld symmetry control. The

weld profile is measured in real-time during welding by the laser vision sensor. The inputs to the

controller are the error in weld symmetry and its rate-of-change. The weld symmetry is

quantified by measurements such as the difference in fillet weld lengths known as differential leg

length (DLL). Adjustments to compensate for weld asymmetry are made by the controller to the

torch offset (TO) and/or work angle (WA). The weld symmetry control task is performed by a

fuzzy control algorithm.

75
Desired Weld

Symmetry

de

dt

Fuzzy

Control

TO

WA

Robot

Control

Energy

Orientation

Disturbing

Inputs

1

GMAW

Final

Weid*

Post-weld

Sensing

Actual Weld Symmetry

Figure 6: Feedback control sub-system block diagram.

During welding, the controller determines the degree of weld symmetry from a scan of the weld

profile from the laser-sensor. The fuzzy controller then determines what the torch offset and/or

work angle should be. Every 2.5mm of weld, the robot controller polls the weld profile

controller for corrective measures to the torch offset and work angle. The average overall

control rate is 1 .4 hertz.

The development of the rule base for this research relied on the evaluation of the data from the

modeling effort described in Barborak, et. al.^. From this work, it was determined that torch

offset and work angle were to be the control variables, whose physical limits were determined to

be +/-2.5 mm and +/-5 degrees respectively. The state variables were determined to be

differential leg length (DLL), and rate of change in differential leg length (DDLL). These state

and control variables were tested in various combinations of single-input/single-output (SISO)

and multi-input/single-output (MISO) controllers.

An example of a SISO controller utilizes DLL as the input, and TO as the control variable. By

studying the dynamic response surface produced under varying conditions, a simplified linear

relationship of DLL with respect to the two control variables TO and WA can be surmised as shown in Figure 3. In order to construct a rule-base for control, this relationship is simply

inverted.

Figure 3 Generic linear response surface of the effect of work angle (WA) and torch offset (TO)

on the resulting weld symmetry or differential leg length (DLL).

76
The set of linguistic rules for this simple linear controller would be as follows:

1 . If weld is Skewed Left Large then move torch Right Large.

2. If weld is Skewed Left Small then move torch Right Small.

3. If weld is Symmetric then move torch None.

4. If weld is Skewed Right Small then move torch Left Small.

5. If weld is Skewed Right Large then move torch Left Large.

Various combinations of input variables, output variables, number of membership functions, and

gains were tested experimentally. See Passino and Yurkovich^ for a thorough treatment of fiizzy

control.

SUMMARY

In this research, we developed a hybrid robotic weld profile control system. This control system

employs an articulated arm welding robot interfaced with a laser-based, structured light machine

vision sensor. The laser sensor is utilized in the feedforward controller by pre-scanning the joint

prior to welding, and is then utilized in real-time in the feedback controller. The feedforward

control sub-system integrates the tasks of joint finding, joint tracking, and modified fill. The

controlled variables in the feedforward loop are travel speed, wire feed speed, arc voltage, torch

offset, and contact-tip-work-distance. The feedback control sub-system performs the task of

weld symmetry control and controls the variables torch offset and/or work angle. Linear control

with heuristics is utilized in the feedforward sub-system while fiizzy control is utilized in the

feedback sub-system,

REFERENCES

' A. Matting and M. Neitzel, "Evaluation of weld defects in fatigue testing," Welding Research

Abroad, XII(7), Aug-Sept, 1966.

A. Matting and M. Neitzel, "The influence of weld defects on the fatigue behavior of welded

construction," jSnY/.s'/? Welding Journal, 14(1), 1967.

^ R.W. Richardson, "Robotic weld joint tracking systems - theory and implementation methods,"

Welding Journal, 65( 1 1 )43-5 1

, 1 986.

R.B. Madigan, et. al. "Monitoring and Control of Welding and Joining Processes," Welding

Handbook, 9* ed., vol. 1, chapt. 10, pp:421-448, American Welding Soceity, 2001.

^ D. Barborak, R. Richardson, D. Farson, and H. Ludewig, "Empirical models for GMAW fillet

weld profiles", Proc. P"* Int. Conf. On Computerization of Welding Information, NIST special

publication, vol. 949, pp.76-87, 1999.

^ Passino, K. and Yurkovich, S. "Fuzzy Control", Addison Wesley Longman Inc. ISBN #0-201-

18074, 1997.

77

WELD PENETRATION CONTROL DURING GTA WELDING USING

WELD POOL OSCILLATION SENSING

M.J.M. Hermans*, B.Y. Yudodibroto* and G. den Ouden*

ABSTRACT

In this paper attention is given to on-line control of weld penetration during GTA welding by

means of weld pool oscillation frequency measurements.

Weld pool oscillations can be triggered by applying short current pulses. Due to changes in arc length as a result of the oscillation of the weld pool, the frequency of the oscillation can be

measured from the arc voltage signal. Different modes of oscillation can be distinghuised. For

each mode the oscillation frequency depends on the weld pool geometry, the density of the liquid

metal and the surface tension. It appears that a dramatic change in oscillation frequency occurs

when a transition takes place from partial to full penetration or vice versa. This phenomenon is

used as a sensing tool for penetration control.

By applying a feedback loop in the measuring and control system, a situation can be obtained

where full penetration can be maintained by welding current adjustment.

Experiments were carried out both in bead-on-plate welding as in orbital tube welding, which

show that good results can be obtained. It appears that shielding gas composition plays an

important role as it influences the electric field strength in the arc and thus the detectability of the

changes in arc voltage/length.

KEYWORDS

GTA welding, sensing and control, weld penetration control, weld pool oscillation

INTRODUCTION

Over the last decade a large amount of research has been carried out in the field of sensing and

control during welding. Development in sensor systems for automation of welding processes is

essential to increase productivity and to detect defects in an early stage of production. A large

variety of tactile and non-tactile sensing systems have been developed for seam positioning,

seam tracking, process control, penetration control and detection of weld pool dimensions

[refs.1-10]. Especially through-the-arc-sensing is an elegant and relatively simple method, as it does not involve additional equipment.

This paper deals with penetration sensing and control during GTAW based on the occurrence of

oscillations in the weld pool. The oscillation frequency of the weld pool is calculated from

variations in the voltage. As the mode and the frequency of the oscillation are related with the

* Delft University of Technology, Joining Technology, Delft, The Netherlands

79
state of penetration, this information can be used for penetration control. The benefits of the

developed feedback system incorporate adjustment of the current in order to prevent

overpenetration due to accumulation of heat or underpenetration, for example as a result of castto-cast variations, and makes additional NDT superfluous.

In order to obtain oscillations in the weld pool, a short current pulse superimposed on the

welding current is applied, which increases the arc pressure temporarily. Due to the variation of

the arc length and thus the arc voltage, the frequency of the oscillation can be calculated from the

collected voltage data. It must be noted that as the sensor is based on measuring arc voltage

variations, the detectability in the case of GTAW with helium gas shielding is better than with

argon gas shielding, as the electric field strength in the arc column is higher (Ar: E = ~1 V/mm,

He: E = ~2 V/mm).

A weld pool can oscillate in different modes depending on the external circumstances. The

dominant modes in the case of a partially penetrated weld pool are schematically depicted in

Figure 1. Mode 1 represents an up-and-down movement of the weld pool, whereas mode 2

represents a front-to-back movement. As these modes are surface oscillations, the oscillation

frequencies are relatively high. They can be expressed by the following equations [2]:

Mode 1

y;=5.84

Pi)

D

-3/2 (1)

Mode 2: /a =3.37 D

-3/2 (2)

In these equations y represents the surface tension, p the density of the liquid metal in the weld

pool and D the (equivalent) diameter of the weld pool. It can be seen that the oscillation

frequency decreases with increasing weld pool width and that mode 2 oscillation exhibits a lower

oscillation frequency. A transition from mode 1 to mode 2 oscillation occurs when increasing

trigger duration, trigger current and travel speed. Especially travel speed has a strong influence,

as with increasing travel speed the weld pool becomes elongated and the position of the impulse

of the arc pressure shifts towards the front part of the weld pool.

In the case of full penetration the weld pool oscillates as a membrane, see Figure 2. This bulk

type of oscillation has a relatively low frequency, which can be expressed by:

Mode 3: /a =1.08 (3)

with H the thickness of the workpiece. Higher order modes of oscillation in the full penetration

situation are to be expected in case of a more eccentric position of the arc with respect to the

centre of the weld pool [Ref 11]. However, the amplitude of these higher order oscillation

modes is expected to be relatively small.

80
top view Ride view

(a) (Q)

(b) c1)

Figure 1: Top and side view of the

oscillation modes in partially penetrated

welds: a) mode 1, b) mode 2,

top view side view

o

Figure 2: Top and side view of full

penetration oscillation mode, mode 3.

The principle of the weld pool oscillation based penetration sensing is that the oscillation

frequency of the weld pool changes abruptly during the transition from a partially to a fiilly

penetrated weld pool. By applying a simple feedback control system, the welding current can be

adjusted in order to maintain correct penetration over the entire weld length. Overpenetration and

underpenetration can both be detected and automatically repaired by adjusting the welding

current.

To apply the penetration sensing method based on weld pool oscillations, two different

approaches can be followed.

The first approach (single frequency approach) can be used in the case of constant current

welding [Ref 8]. The basic principle of this method is to predefine and maintain an optimal

oscillation frequency (control frequency) for a correct fully penetrated weld pool. During

welding the oscillation frequency is measured and welding parameters are adjusted in such a way

that the requirements of the full penetration control frequency are met. The advantage of this

method is the better detectability of the mode 3 oscillation compared to the partially penetrated

oscillation modes (mode 1 and 2) as the amplitude and thus the variations in arc voltage are

relatively high. This approach is therefore suitable for small weld pool top width, i.e. relatively

high welding speed conditions, and for welding under argon gas shielding. Selecting the

appropriate control frequency is the key to success of this approach.

The second approach (double frequency approach) can be applied in the case of pulsed GTAW

[Ref 7]. Basically, the procedure is similar to the single frequency approach. The oscillation in

the weld pool is triggered by a short current pulse and the frequency is measured during both the

pulse and the base period. Correct penetration is achieved when during the pulse period an

oscillation frequency is measured corresponding to the oscillation frequency of a fiilly penetrated

weld pool, while during the base period the oscillation frequency corresponds with the frequency

of a partially penetrated weld pool. In this approach mode 1 oscillation should be monitored, as the difference between the magnitude of the mode 2 oscillation frequency and the mode 3

oscillation frequency is small. Therefore the travel speed has to be relatively low.

The focus of this paper will be on the single frequency approach.

Momentarily, research is carried out concerning the influence of metal transport on weld pool

oscillations during GTAW with filler wire.

81
EXPERIMENTAL

A large number of experiments are carried out on mild steel and stainless steel in the form of

plate and tube of different dimensions. The physical properties of the metals are listed in Table 1. For welding a transistorized power source was used in constant current mode. Trigger pulses

were superimposed on the welding current. As shielding gas both Ar and Ar-5%H2 were used.

The welding parameters are listed in Table 2.

Table 1 : Physical properties of the steels used.

Workpiece metal Surface tension yo (N/m) Density p (kg/m^)

Mild steel, Fe 360

Stainless steel, AISI 316L

1.0

1.2

7.0- 10"^

7.5 • 10^

Table 2: Welding conditions.

Electrode W-2%Th02, 02.4 mm, tip angle 60°

Arc length 1.0 -2.0 mm

Travel speed 7.5-24 cm/min

Shielding gas Ar,Ar-5%H2l01/min

Base current 30-150 A

Trigger pulse frequency 2 Hz

Trigger current 100-300 A

Trigger duration 2-3 ms

Labview (National Instruments) was used to control the power source and shielding gas supply,

for applying the trigger pulses, for data acquisition and for feedback control. Figure 3 shows a

data flow chart. The input parameters are: initial base current and trigger current, trigger

duration, trigger frequency and control frequency. During welding, voltage data was collected

with an acquisition rate of 20.48 kHz. During every cycle 4896 data points are captured, see Figure 4. From this set of data the first 400 data points (including data before the trigger pulse

and the data of the trigger pulse) and the next 400 data points (just after the trigger pulse) are omitted for data analysis. Thus, a sub-set of 4096 voltage data points remain for determining the

oscillation behaviour. The DC component is removed from the signal. This data was then filtered

with a band pass filter and on-line analysed by means of Fast Fourier Transformation. The

calculated oscillation frequency was compared with the selected control frequency. The outcome

of this comparison was then used for current feedback.

The response rate in current adjustment depends on the difference in the measured oscillation

frequency and the required control frequency. If necessary, current adjustments were carried out

after each cycle. As an additional safety in the system the amplitude of the peak frequency is also

taken into account. In the ceise that the amplitude is too low, for instance when generation of a

proper oscillation fails, the signal is disregarded by the feedback system.

The raw and analysed data is stored on disk. Therefore, at any position in the weld, the data of

the oscillation behaviour is traceable and provides information about penetration quality.

82
fixfc • lb <Ib

D+1 n

fi»fc lb >Ib

n+1 n

Figure 3: Flow chart of data analysis

18

•M

5 10 or

6^

4

2 Q06 ai

11nne(s)

ai5 02

Figure 4: Example of collected voltage data

RESULTS AND DISCUSSION

Data acquisition and analysis for partial and full penetration

Oscillation frequency measurements were carried out under various experimental conditions.

Figure 5 shows the signals obtained in the case of a partially penetrated weld pool during beadon-plate welding of mild steel. Figure 5a depicts the arc voltage as function of time after

triggering when the weld pool is partially penetrated. A good oscillation signal can be directly

identified by the occurrence of damping. This signal is processed by FFT, which is displayed in

Figure 5b. The oscillation frequency of a partially penetrated weld pool is situated at the higher

regions of the frequency domain and can be associated with surface oscillations.

The situation in which full penetration occurs is presented in Figure 6. In this case, the relatively

low frequency, characteristic for bulk oscillation is observed.

The measured oscillation frequency, in both situations, is in good agreement with the calculated

oscillation frequency (equation 1 and 3).

zoo

150

150 -I 1 . 1 0 0.05 0.1 0.15 0.2

0 50 100 ISO 200 Fr«qu«ncy (Hz)

Figure 5: Partial penetration, a) voltage

oscillation signal, b) frequency domain.

250

200

0 50 1 00 1 60 200 Fr<qu*noy (Hi)

Figure 6: Full penetration, a) voltage

oscillation signal, b) frequency domain.

83
Sensing and control during bead-on-plate welding

For the single frequency approach a control frequency in the full penetration range has to be

chosen.

As an example, Figure 7 shows the results of a bead-on-plate weld on a 3 mm thick mild steel

workpiece welded at a travel speed of 2 mm/s. The initial base current was set at 115 A. The

control frequency is set at 50 Hz. This value is calculated by means of equation 3.

During the first part of the weld the feedback system was not activated. As can be seen the

current is constant. At the start of welding a partial penetration situation exists and the oscillation

process has to be stabilised. This period (until t= 6s) is associated with the occurrence of a

relatively high frequency. As the weld pool size increases during welding, full penetration is

obtained and the oscillation frequency starts to drop (from t= 6s). Finally, severe sagging of the

weld pool occurs. At t=22s the feedback system is switched on. As can be seen in the figure, the

base current is reduced in order to increase the oscillation frequency of the weld pool by

reducing the weld pool bottom width. The measured oscillation frequency is increasing until the

control frequency is reached. The width of the penetration remains constant when the feedback

system is active.

b)

80 -1 I

70 - >>u

c 60 - «

3 50 - 1

40 - C 30 - o1

20 -

"o 1 0

-

(A o 0

-

con tro I fre q u e n cy

C)

1 0 20

15 0 -I < 14 0

-

c 13 0-

3 12 0-

O

o> 110-

e 10 0

- 1 9 0

- 80 - ^ Feedback activated

d)

1 0 2 0 3 0 Time (s )

4 0 50 60

Figure 7: Bead-on plate weld on 3 mm thick mild steel; a) top view of the weld, b) bottom view

of the weld, c) oscillation frequency as fimction of time, d) welding current as function of time.

84
Sensing and control during orbital tube welding

The oscillation frequency and the current response were also measured during orbital tube

welding on 2.0 mm thick stainless steel AISI 316L. The initial base current was 50 A and a

control frequency of 40 Hz was selected. The feedback system was activated before welding

started. An example of the results obtained is presented in Figure 8. Figure 8a shows the

measured oscillation frequency as function of time. After fijll penetration is achieved, the

oscillation frequency is maintained at the control frequency by small current adjustments (Figure

8b). The reduction in current in the final part of the weld is directly related with the accumulation

of heat in the tube. Less current is required to maintain full penetration.

Inspection of the welds shows that correct penetration is achieved along the entire weld and that

the overall quality is excellent, see Figure 8c.

It should be expected that the welding position, in the case of orbital welding, has an influence

on the weld pool behaviour. However, it appears that the influence of gravity on the oscillation

behaviour is limited and therefore can be neglected [2].

"nISO

^160

o 140 § 120 §100 * 80

.2 60

S 40 o 20

(0 O 0

L—

20 40 60

Time(s)

80 100

a)

c)

b)

Figure 8) Orbital weld on 2mm stainless steel; a) oscillation frequency versus time, b) welding

current versus time, c) penetration appearance.

85
CONCLUSIONS

On the basis of the results of the research carried out in the field of penetration sensing by means

of weld pool oscillation monitoring the following conclusions can be drawn:

1 . Partially and fully penetrated weld pools oscillate in different oscillation modes.

2. The oscillation frequency depends on the mode of oscillation, weld pool size and physical

properties of the liquid metal.

3. The oscillation frequency drops when full penetration is achieved. This change in oscillation

frequency can be used for in-process control of weld pool penetration.

4. The use of sensing in combination with feedback results in excellent weld quality.

ACKNOWLEDGEMENTS

The authors wish to express their gratitude to Y.H. Xiao, A.J.R. Aendenroomer and B. Hu for

their contributions and to W.A.J. Brabander, F.J.A.M. Bosman for their technical assistance.

REFERENCES

1. Madigan R.B. and R.J. Renwick; 'Computer based control of full penetration GTA welds

using pool oscillation sensing', Proc. 1** Int. Conf On Computer Technology in Welding,

London, UK, 1986, 165-174.

2. Xiao Y.H. and G. den Ouden; 'A study of GTA weld pool oscillation'. Welding Joumal, Vol. 69, No.8, 1990, 289s-293s.

3. Sorensen CD. and T.W. Eagar; Modeling of oscillation in partial penetration weld pools',

Joumal of Dynamic Systems, Measurement and Control, 1990, 1 12, 469-474.

4. Xiao Y.H. and G. den Ouden; Weld pool oscillation during GTA welding of mild steel'.

Welding Joumal. Vol 72, No.8, 1993, 428s-434s.

5. Bicknell A., J.S. Smith and J. Lucas; 'Arc voltage sensor for monitoring of penetration in

TIG welds', lEE Proc. Sci.Meas.Technol. Vol 141, No. 6, 1994, 513-520.

6. Anderson K., G.E. Coock, R.J. Bamett and A.M. Strauss; 'Synchronous weld pool

monitoring and control', IEEE transactions on industry applications Vol. 33, No. 2,

March/April 1997, 464-471.

7. Aendenroomer A.J.R. and G. den Ouden; 'Weld pool oscillation as a tool for penetration

sensing during pulsed GTA welding', Welding Joumal, Vol 77, 5, 1998, 181s-187s.

8. Hu B. and G. den Ouden; Weld penetration sensing and control during GTA welding using

weld pool oscillation, in Proceeding Int. Conf on trends in welding research. Pine Mountain,

Georgia, USA, Juni 1998, 1 125-1 130.

9. Rehfeldt D. and T. Polte; 'Arc sensor as a quality assurance element for uniform full

penetration welding', Schweissen und Schneiden. Vol. 52, No. 10, 2000, E228-E231.

10. H. Wohlfahrt, K. Thomas and S. Wiesner; 'Welding voltage measurements during TIG and

MIG welding with the objective of controlling the weld penetration', Schweissen und

Schneiden. Vol. 53, No. 2, 2001, E29-E31.

11. Maruo H. and Y. Hirata; 'Natural frequency and oscillation modes of weld pools'. Welding

Intemational, Vol. 7, No. 8, 1993, 614-619.

86
REAL-TIME MOLTEN POOL AREA EXTRACTION FOR CONTROL OF GAS

TUNGSTEN ARC WELDING

Y. Z. Wang, Z. Jandric and R. Kovacevic*

ABSTRACT

This paper presents a real-time molten pool area extraction method for the control of gas

tungsten arc welding (GTAW) with a wire filler in the hybrid rapid prototyping and tooling

(RP&T) technique based on welding and CNC milling. The molten pool shape and size were

strongly influenced by the two-dimensional geometry of the built part. A coaxial machine vision

unit is used to acquire images of molten pool and its surrounding area through the torch. After

image processing, the boundary of the molten pool is extracted. Because the molten pool is

partially hidden by the tungsten electrode, its boundary is incomplete. The molten pool area is

selected as the feedback to the closed-loop control of the GTAW process. In order to calculate

the area of the molten pool with an incomplete boundary, a neural network is used. By changing

welding parameters, the neural network is trained for different sizes of molten pools. The testing

of the neural network is performed by another group of molten pool images.

INTRODUCTION

Nowadays, industry is under constant and growing pressure for shortening the time-to-market

lead time. Rapid prototyping (RP) techniques have been developed for fulfilling this demand.

For many years, rapid prototyping techniques have been used almost exclusively on materials

such polymers, waxes, or paper (Ref 1). The rapid prototyped parts had limited dimensions, and

they had a tendency to distort due to shrinkage.

Three-dimensional welding has the ability to rapidly produce strong, fully dense metallic parts in

the form of layers (Refs. 2, 3). To increase the surface quality and accuracy of the metallic parts

made by the 3D welding, a promising hybrid rapid prototyping and tooling (RP&T) technique

based on gas tungsten arc welding (GTAW) and CNC milling has been under development at the

Research Center for Advanced Manufacturing (RCAM) at Southern Methodist University,

Dallas, TX (Ref. 4). This hybrid process allows the fabrication of metallic parts with high

dimensional accuracy and with complex external and internal geometrical features.

In the hybrid RP&T technique, the GTAW process with a wire filler provides depositing

materials and energy to fuse the added material. For the needs of RP&T, the welding process is

required to provide lower heat input and lower bead penetration that is exactly opposite to the

conventional welding process. In order to generate uniform and fairly flat beads and layers, a

metal deposition rate and molten pool size should be invariant of the alternations of the welding

Research Center for Advanced Manufacturing, Southern Methodist University

1500 International Pkwy, Suite 100, Richardson TX 75081

* corresponding author.

Tel: 1-214-768-4865, Fax: 1-214-768-0812, Email: kovacevi@seas.smu.edu

87
conditions. The geometry of the molten pool can provide abundant, accurate and instantaneous

information about the welding process. Hence, closed-loop control of the welding process, with

the information of the molten pool size as a feedback, is a necessity.

Although several indirect approaches, such as monitoring the pool oscillation, the ultrasonic

technique, infrared sensing, and radiography, have been proposed to sense the geometry of the

molten pool, machine vision, being non-intrusive, seems to be the most promising technique for

obtaining the clear geometry of a molten pool.

Machine vision systems have been extensively used for the molten pool sensing and seam

tracking for more than two decades, mostly with the idea to simulate the eye sensing and process

actuation of experienced human welders. Previous research works for sensing the molten pool in

GTAW with machine vision was conducted without a wire filler.

In the GTAW process with a wire filler, it is necessary to ensure that the wire is always fed in

front of the moving arc. In order to avoid blocking the optical path between the molten pool and

the CCD camera by the wire filler, a coaxial machine vision unit, developed by Richardson et al. (Ref 5), is used in this paper. The lens of a CCD camera is coaxially mounted with the electrode.

The electrode tip is used to block the bright core of the arc light from the overpowering exposure

on the CCD target. After the image of the molten pool is captured, the Laplacian operator is used

to detect the edges. Then, the image was segmented into a binary one with an adaptive threshold.

The boundary of the molten pool is extracted from the binary image.

The molten pool size and shape in the hybrid RP«feT are strongly influenced by the twodimensional geometrical features of the part to be built. Fig. 1 demonstrates the molten pools at

the flat surface (point 6), the edge (point 5), and the comers with different angles (points 1 to 4),

while keeping the welding parameters constant. This paper is concentrated on the acquisition and

processing of the shape of the molten pool at the flat surface (point 6).

Figure 1 . The shapes of the molten pool as a function of 2D geometrical features of substrate

EXPERIMENTAL SET-UP AND EXPERIMENTAL CONDITIONS

Fig. 2 illustrates the schematic presentation of the hybrid RP&T machine, consisting of a central

controller, a CNC milling machine, a GTAW unit, and a coaxial machine vision unit. The central

controller acquires the measured data and controls the welding current, wire feeding speed, and

88
wire feeder's position, while the workpiece motion is controlled by the CNC machine

microprocessor. The filler metal is fed directly into the molten pool. The step motor 1 is used to

ensure that the wire is always in front of

the moving arc. The step motor 2 is used to

control the filler metal feeding speed.

Path Genaation

The coaxial machine vision unit used for

the molten pool image acquisition, the

image processing, and the molten area

extraction, is composed of a computer

equipped with the frame grabber, a CCD

camera, and the image processing

software. The image resolution is 640x480

pixels with 256 gray-levels.

The experiments are carried out with the

following parameters:

• The workpiece traverse speed is 23

cm/min;

• The wire feeding speeds are 20 and 40

cm/min;

Pcsitioiung Systan

ControUcT CNC

Machine

Central

Controller

Digital I/O Board

TimCT I/O Board for the PC "^ Wire Feeder Speed Control

Image

ProcesBiiig

DAQ

(Trigger)

Path Generation

Wire Feeder 1 Speed Control r

Cunent

Control

Current

Signal Monitor

Frame

Grabber GTAW Power

Supply

Voltage

Signal Monitor

Step Motor 2 00

Wire Feeder

X-axis Figure 2. Experimental set-up

The welding currents are changed from 100A to 133A with a 3 -A interval.

The welding voltages are changed from 9V to 1 IV.

The filler metal is mild steel with a diameter of 0.89 mm.

DESCRIBING MOLTEN POOL GEOMETRY

In conventional GTAW without a wire filler, the shape of the front part of the molten pool

always keeps semicircle with the half-maximum-width as the radius despite different welding

conditions, but the sharpness of the rear of the molten pool is changed with the penetration. Also,

the shape of the molten pool can be thought as symmetric along the welding direction (Ref 6).

The following geometrical parameters were used by different researchers to characterize the

geometry of the molten pool: the maximum width, the maximum length, the half-length, the area,

and the rear angle.

In the case of RP«&T based on the deposition by

welding, the shape of the molten pool is no longer

symmetric along the welding direction, as shown

in Fig. 3. Thus, the width and length are no longer

reliable parameters to represent the molten pool.

In such conditions, the molten pool area is the

only reliable parameter for the control of GTAW.

For a molten pool with a closed boundary, the area

is equal to the number of the pixels inside the

Figure 3. Asymmetric molten pools

89
(a) Complete

boundary. In order to calculate the area of a

molten pool with an asymmetric shape and

inconplete boundary, the molten pool must be

characterized by the adequate parameters.

The selection of the characteristic parameters is

crucial. Three criteria must be satisfied. First, all

parameters must have a common reference point.

Second, the area of the molten pool must be

calculated using the selected characteristic

parameters. Third, although more parameters could describe the

molten pool more accurately, the increase in the number of selected

characteristic parameters may complicate the calculation and

increase the calculation time. Thus, the number of parameters must

be optimized.

Intuitively, the center of the molten pool is a suitable reference

point. But because of the difficulty of finding the molten pool

center, a relative fixed point is another choice. The position of the

electrode shadow, the dark spot in Fig. 4, is unchanged in all

images. The top point L of the electrode shadow is always inside of

the molten pool. Based on this position, the reference point R is

determined as follows:

(b) Incomplete

Figure 5. The illustration of the selected

characteristic parameters

Figure 4. Definition of

reference point

• Find the line segment AB that passes through point L and is perpendicular to the direction of

welding as shown in Fig. 4. AB intersects with the boundary of the molten pool at points A

and B, respectively;

• Find the middle point R of AB . R is defined as the reference point for all selected

characteristic parameters.

For a closed-contour of the molten pool, in which each radius vector from the center of the

boundary intersects the boundary at only one point, a one-dimensional series of real numbers can

be acquired by measuring the length of successive radius vectors that are angularly equispaced.

This series is denoted as {R(a)), where R(a) is the radius vector at the angle a.

In our case, the boundary of the molten pool is incomplete. A series of radius vectors is selected

as the parameter to calculate the area of the partially hidden molten pool. Fig. 5(a) shows the

simulation of a completed molten pool and a series of radius vectors composed of R1--R12. The

angle between two adjacent radius vectors is 30°. From Fig. 4, it can be seen that the front part of

the molten pool is hidden by the electrode shadow. It was experimentally determined that only

part of the radius vectors, Rj-R?, can be extracted from every image. The rest, Rg-^Ru, are not

always available. Two radius vectors, denoted as Rg and R12 are used to connect the reference

point R and the two end points. As shown in Fig. 5(b), the angles ai and a2 may not be 30°.

Thus, RjtoRs and R]2 plus ai and a2 are the parameters used to calculate the area.

90
MOLTEN POOL BOUNDARY EXTRACTION

After the image of the molten pool is taken, it is smoothed using a median filter. A Laplacian

operator is used to detect the edges. Then, the image is enhanced and segmented into a binary

one with adaptive thresholding. All possible edge points are detected. The molten pool boundary

is then extracted by making use of the continuity of the molten pool boundary with the following

steps as shown in Fig. 6: • Draw a line that passes through the reference point R, defined in previous section, and is

perpendicular to the direction of welding (Y direction) as shown in Fig. 6(a);

• Find all possible edge points that the line intersects. For example, points 1~6 in Fig. 6(a); • Starting from these points, trace the connected edge points to a negative Y direction to form

edges, while counting the lengths of all edges;

• Reserve the longest edge as the upper part of the molten pool boundary as shown in Fig. 6(b),

and ignore the others; • Starting from points 3 and 5, along the positive Y direction, trace the possible edge points

connected with the upper part of the boundary to find its lower part. The result of this

operation is show in Fig. 6(c).

>x -x

(a) starting points (b) the upper part

Figure 6. Molten pool boundary search

(c) the boundary

(a) original image (b) possible edges (d) extracted boundary (e) radius vectors

Figure 7. Molten pool boundary extraction

Based on extracted boundary, nine radius vectors and two angles are determined to calculate the area of the molten pool. Fig. 7(a) shows the original image acquired under the following welding

conditions: the workpiece traverse speed is 23 cm/min; the wire feeding speed is 40 cm/min; the

welding current is 115A; the welding voltage is 10.5V; and the filler metal is mild steel with a

91
diameter of 0.89 mm. The images after each processing step are shown in Fig. 7(b)-(d). The

values of the geometrical parameters of the molten pool are tabulated in Table 1. The image

processing time is within 300 ms.

Table 1. The nine radius vectors (pixel) and two angles (degree)

Ro CCi Ri R2 Rs R4 Rs R6 R? Rs 0C2 102.7 58.9 74.0 67.0 71.6 83.0 85.5 78.5 75.0 77.9 15.6

DEVELOPMENT OF NEURAL NETWORK MODEL FOR MOLTEN POOL AREA

CALCULATION

The area of the molten pool can be correlated using the nine radius vectors and the two angles.

However, the correlation is non-linear. To approximate the complicated non-linearity, a twolayer multi-layer perceptron (ML?) neural network is used to build the correlation. Log-sigmoid

function is applied as transfer function in both layers. The lengths of the nine radius vectors plus

two angles are used as the input vector. The output of the network is the molten pool area.

Back propagation (BP) is a well-established method to train the MLP neural network. In order to

ensure the convergence, a momentum method is applied in the BP learning algorithm. The

training method is then called back propagation with momentum (BPM). Properly trained BP

networks tend to give reasonable answers when presented with inputs that they have never seen

before. Typically, a new input leads to an output similar to the correct output for the input vector

used in training that is similar to the new input being presented. This generalization property

makes it possible to train a network on a representative set of input/target pairs and get good

results without training the network on all possible input/output pairs.

Wire feeding speed = 20 cm/mm

Wire feeding speed = 40 cm/mm

I = 100A I = 109A I=118A I=127A I = 133A

92
Figure 8. Molten images under different wire feeding speeds and welding currents

A MATLAB program is developed to train the neural network by BPM using the molten pool

images with different sizes as samples. While the workpiece travels to different directions, the

molten pool images are acquired under the following experimental conditions: the filler metal is

mild steel with a diameter of 0.89 mm; the workpiece traverse speed is 23 cm/min; the wire

feeding speeds are 20 and 40 cm/mm; and the welding currents are changed from lOOA to 133

with a 3-A interval. Some of the acquired images are given in Fig. 8. It is clear that the molten

pool size increases while the welding current increases.

(a) Extracted boundary (b) Approximated boundary (c) The area Figure 9. Molten pool boundary approximation

In order to measure the molten pool area, a part of a circle is used to approximate the incomplete

molten pool boundary. Fig. 9(a) shows the incomplete boundary extracted from Fig. 7(a). A

circle is determined by three points a, b, and c on the boundary as shown in Fig. 9(a). Points a

and b are the two ends of the extracted boundary. The segment ac is perpendicular to the

welding direction. The approximated boundary is shown in Fig. 9(b). The molten pool area, the

white area as shown in Fig. 9 (c), is then determined by counting the number of pixels.

The values of the geometrical parameters

in Tab. 1 and the area, 20,346 pixels, is an

input/output pair for the neural network.

One hundred input/output pairs are calculated from the acquired molten pool

images. Eighty input/output pairs, the

smallest and the largest ones are 12,166

and 44,738 pixels, respectively, are used to

train the neural network model. The other

20-input/output pairs test the trained neural

network. Fig. 10 provides a comparison of

the measured molten pool areas (presented

by 'o') and the neural network calculated

results (presented by 'x'). There is a good

agreement between the measured molten

pool area and the calculated ones.

3.6

^32 if)

a. 2,8,-

in;

2

18-

16

'o' measured molten pool area

'x' calculated molten pool area

Q

6

9

4 .6 8 10 .12 14 ,16 18 20

,

, The Wumber oflh;a:Molteti; Pool; :

Figure 10. Comparison of measured molten

pool areas with the calculated ones

93
CONCLUSION

Machine vision is an efficient sensing method for monitoring the molten pool in the GTAW

process. In order to control the welding process of GTAW with a wire filler for the need of the

hybrid RP&T technique based on welding and CNC milling, the area of the molten pool was

used as feedback to control the GTAW process. The image of the molten pool and its

surrounding area was acquired by a CCD camera coaxiallv integrated with the tungsten

electrode. The boundary of the molten pool is extracted aitei the image processing. The

boundary is incomplete because the molten pool is partially hidden by the electrode. A two-layer

neural network is established using MLP to calculate the area of the molten pool with its

incomplete boundary. The neural network was trained by back propagation learning with a

momentum term using molten pool images acquired for different welding conditions. The testing

results by another group of the molten pool images show that the neural network provides a good

result.

ACKNOWLEDGEMENTS

This work was financially supported by THECB, Grant No. 003613-0022001999, NSF Grant

DM1-9809 198, and by the US Department of Education, Grant No. P200A-80806-98. The

authors would like to express the gratitude to Weldware Inc. Columbus, OH for providing free of

charge the GTAW torch with coaxial machine vision system for the need of this project.

Assistance of Mr. I. S. Kmecko during the experiment is also gratefully acknowledged.

REFERENCES

1. Knith, J. P.; Leu, M. C; and Nakagawa, T. 1999. Progress in adaptive manufacturing and

rapid prototyping. CIRP Annuals 47 (2): 525-540.

2. Beardsley, H. E.; and Kovacevic, R. 1999. New rapid prototyping technique based on 3-D

welding. Technical Paper - Societv of Manufacturing Engineers. PE. No. PE99-126, SME,

Dearborn, MI, USA: PE99-126-1 - PE99-126-6.

3. Spencer, J. D.; Dickens, P. M.; and Wykes, C. M. 1998. Rapid prototyping of metal parts by

three-dimensional welding. Proceedings of the Institution of Mechanical Engineers, Part B:

Journal of Engineering Manufacture 212 (B3): 175-182.

4. Kovacevic, R. 2001 . Rapid manufacturing of functional parts based on deposition by welding

and 3D laser cladding. Proceedings of the Mold Making 2001 Conference, presented by

Mold Making Technology Magazine: 263-276.

5. Richardson, R. W.; Gutow, D. A.; Anderson, R. A.; and Parson, D. F. 1984. Coaxial arc weld

pool viewing for process monitoring and control. Welding Journal 63 (3): 43-50.

6. Kovacevic, R.; and Zhang, Y. M. 1995. Machine vision recognition of weld pool in gas

tungsten arc welding. Proceedings of the Institution of Mechanical Engineers. Part B: Journal

of Engineering Manufacture 209 (B2): 141-152.

94
Session A3: Sensing and Control II: VPPAW

95

MONITORING STABILITY OF KEYHOLE

FORMATION IN VPFAW

G. Tao, H. Wang and R. Kovacevic*

ABSTRACT

There are a number of factors that influence the quality of the weld formation in variable polarity

plasma arc welding (VPPAW). Stability control of weld formation in VPPAW is required. One

of the control strategies is to apply feedback control. The key for the successful application of

feedback control is to acquire a characteristic signal that reflect the status of the controlled object.

Airborne sound generated in the keyhole mode of VPPA welding process is used to characterize

the presence of the keyhole and its size. Short-time spectrum analysis is used to study the

frequency characteristics of the sound signal during the welding process. Short-time spectra are integrated along the frequency domain to obtain the power at different frequency intervals. The

power is then used as the input to an artificial neural network (ANN) model to classify the

keyhole diameter.

KEYWORDS

Sound signal, Time-frequency analysis, ANN, VPPAW

INTRODUCTION

Variable polarity plasma arc welding (VPPAW) is a promising welding technique for joining

aluminum alloys. It has been used successfully in production, such as in the fabrication of the

space shuttle external tanks (Ref. 1-3). VPPAW can generate high welding quality and high

productivity at relatively low cost. However, the keyhole molten pool is very dynamic, and it can

collapse and generate bum-through holes during welding, especially when welding plates with

thicknesses above 4.0 mm. Thus, the selection of the welding parameters and the implementation

of controller to ensure the stability of the weld formation in real-time remain a challenge.

Recently, it was found that the presence or absence of a keyhole could be determined by

measuring the ratio of hydrogen to argon in the plasma arc column with an optical spectrometer

(Ref.4). However, the size of the keyhole cannot be determined and the welding process cannot

be distinguished from the cutting process according to that signal. At present, two problems are associated with sensing of the keyhole weld pool in VPPAW: 1) Inaccessibility from the frontside of the weld pool because of the limited torch standoff-distance and the interference produced

by the strong arc light. 2) Detection of the keyhole from the back-side of the workpiece is related

to the complexity of the weld structure, for instance, in the welding of pressure vessels.

Research Center for Advanced Manufacturing, Southern Methodist University

1500 International Pkwy, Suite 100, Richardson, Texas 75081

* corresponding author

Tel: 1-214-768-4865, Fax:1-214-768-0812, Email: kovacevi@seas.smu.edu

97
Previous work shows that keyhole size captured by a machine vision system can be used as a

feature signal to the feedback control of the weld formation in VPPAW (Ref.5). However, it is

difficult to precisely monitor the keyhole size by machine vision when the keyhole is very small

or very large. A new approach to characterize the presence and the size of the keyhole is needed.

One of the potential solutions is to relate the signature of the airborne sound signal during

welding to the geometrical characters of the molten pool. The acoustic signal has been used

successfully in the monitoring of different welding processes. For example, the time-frequency

characteristics of airborne signals were investigated in laser welding (Ref6). It was found that

the acoustic spectrum of good-quality, full-penetration welds could be differentiated from the

spectra of poor-quality welds, defined as either partially penetrated welds or welds having a gap

between the sheets being joined. Welding sound signature in GTAW was also studied to reveal

the conditions that generate weld defects (Ref7). It was found that the sound signature produced

by GMAW contained information about the behavior of the arc column, the oscillation of metal

in the molten pool, and the metal transfer mode. The analysis of the acoustic signal from arc welding, oxy-flame cutting, and water jet cutting were carried out also (Ref.8). In addition,

acoustic emission monitoring of laser beam welding was investigated to detect laser misfiring,

loss of power, and improper focus (Ref.9).

In this paper, the sound generated in the VPPAW is studied. The investigation is focused on the

following: 1) Frequency and time-frequency analyses of the sound signal generated in VPPAW

to study the welding status. 2) Establishment of the model to describe the relationship between

the sound signal and the keyhole diameter of the weld pool.

EXPERIMENTAL SET-UP AND PROCEDURES

Welded

material

LaserStrobe

System

Torch

Experimental Set-up

The experimental set-up, as shown in Figure 1,

consists of a VPPAW

machine, a CNC

positioning system, an

ultra high-shutter-speed

camera assisted with a

pulsating nitrogen laser

(Laser Strobe system) and

a microphone with a data

acquisition system. The

Laser Strobe system is

used to monitor the

keyhole size from the

backside of the workpiece as a reference. The image acquisition and sound signal acquisition is

synchronized. The frequency of image acquisition is 2 Hz while the frequency of the sound

signal acquisition is 50 kHz,

Back-side Keyhole Image

Figure 1. Experimental set-up

Acquired Sound Signal

98
Experimental Procedures

The experiments are performed in several steps:

1) The keyhole is generated by a stationary torch in order to study the sound signal at different

welding modes.

2) A copper plate with pre-drilled holes is used to study the relationship between the size of the

hole and the generated sound signal by the plasma.

3) A set of experiments is performed in order to study the relationship between the keyhole

diameter and the level of sound signal simulating the real welding conditions.

SPECTRUM ANALYSIS AND TIME-FREQUENCY ANALYSIS

Spectrum analysis is used to study the process characteristics. In this paper, an averaged

periodical diagram is used to obtain the spectral analysis to speed up the calculation and to

increase the resolution. The data is separated into several segments with overlapping. For each

data segment, p, the periodicity can be calculated by equation (1)

:

2 M

i=\

ii)e

-jrkIM ,k=l,2,...M/2or(M-l)/2 (1)

The Hann window is used for truncation to decrease the power leakage and to get a smoother

result. Finally, the average periodicity is obtained by averaging the periodicity of each segment,

/? , for p = l:L

p=\

(2)

Thus, the expectation of the standard deviation of the spectral analysis can be decreased to 1/L,

Due to the variation of the VPPAW welding process, the acoustic signal is dynamic and time

dependent, so the time-frequency distribution is an effective tool for studying this process. The

time-frequency distribution can be viewed as a transform which represents the energy or density

of a signal simultaneously in both time and frequency domains. It reveals the time-dependent

features that the ordinary spectrum analyses fail to show. In this paper, a short-time spectrum

analysis is used to study the time-frequency character of the VPPAW. Equation (3) shows the

expression of the short-time spectrum analysis:

2 SSPEC,{tJ) = \STFTAtJ)\ = \x{t')w{f-t)e-^^''^'df (3)

Where x{t) is the acoustic signal windowed by a suitable function w{t) (in this paper, it is the

Hann window), and T is the interval of integration. Equation (3) can be expressed in a discrete

version of the short-time spectrum analysis:

2 SSPEC^{i,k) = \STFT^{Uk)\- = M

(4)

The short-time spectrum at time / ( the / the second of the sound signal) is obtained by averaging

the periodical diagram. The results of the short time spectrum are integrated in different

frequency intervals from f^^ to f^^ order to present the frequency properties around the central

frequency, , in the same time (equation (5)):

99
INSSPE,{i,f,)= ^SSPEC,{i,k) (5)

k=fk\

In this paper, the real-time spectrum analysis is performed for the duration of one second by

averaged periodicity. The next second, the previous data should be discarded in order to generate

data for a new short-time spectrum analysis.

SOUND GENERATION FOR DIFFERENT VPPAW MODES

During welding, the pulsation of the arc plasma, and the oscillation of the weld pool are the

major sources of the sound signal. The formation of the weld pool in VPPA welding passes

through three stages: a no-keyhole weld pool or a fusion weld pool, a transition weld pool, and a

keyhole weld pool. In the case of the no-keyhole weld pool, there is a layer of unmelted metal

below the weld pool that allows for strong stirring of the weld pool. So, the weld pool generates

slightly stronger oscillations at low frequencies. In the case of the transition mode, the keyhole is

not formed, but the weld pool is generated along the entire thickness of the welded material. In

this weld mode, the natural oscillation frequency of the weld pool is low. So, resonance could

occur and the sound signal at the low frequency should be the strongest among the all three

welding modes. In the case of the keyhole mode, there is a keyhole at the center of the weld pool

and the stirring effect of the jet is decreased. So, the intensity of the sound signal at the lower

frequencies is less pronounced than in the case of the transition mode. It is expected that the

analysis of the intensity of the sound signal will be useful in distinguishing the modes of the weld

pool in VPPAW.

EXPERIMENTAL RESULTS

Generation of the Keyhole bv Stationary Torch

>

8 »

0 0.4 0.8 1.2 1.8 2.0 2.4 2.8 3.2 3.8 4.0 Time fs)

0 4> «.g 144 1«.2 24 28.8

Frequency (kHz)

a) b)

Figure 2. Sound signalfor stationary torch welding

a) Time domain b) Frequency domain

In the case of keeping a fixed position of the VPPAW torch with respect to the aluminum plate

with a thickness of 3.18mm, all three modes of the molten pool are formed by controlling the

intensity of the welding current. The signature of the sound in the time domain (Fig.2 a ) does not

100
show too much variation and only a number of randomly occurring singularities are present.

Figure 2 b shows the power spectrum of the sound signal. There are several dominating spectra

Transition mode

No-keyhole

a

140

120

100

Keyhole mode

TratisitioTi tnode

>

No-keyhole \

&O

Ph

D 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4

Keyhole mode

Stop we Idig

a) Time (s)

0 0.4 0.8 1.2 1.6 2.0 2.4 2.6 6.2 3.6 4 b) Time (s)

Figure 3. Diagram ofthe welding current (a) and short-time spectrum analysis results of

keyholeformation process at the frequency of2.2 kHz (b)

peak values at the frequencies such as: 2.2 kHz, 3.9 kHz, 8 kHz,12 kHz.

The sound signal is studied by integrating a short-time spectrum analysis around these peak

values. The time-frequency analysis around the frequency of 2.2 kHz shows that there is a strong

relationship between the power spectrum of the sound signal and the phases of the molten pool

formation (Figure. 3).

In this experiment, based on the changing of the welding current, the transition mode and keyhole mode will happen at 1.6 and 2.5 seconds respectively. Figure 3 shows the changing of the

welding current and the short-time spectrum analysis results during the stationary torch welding

at the frequency of 2.2 kHz. It is shown that the power spectrum at the frequency of 2.2 kHz will

increase during the transition mode and then will drop back down when the keyhole is generated.

It is clear that the spectral analysis of the sound signal provides a useftil tool in distinguishing the

welding pool modes in VPPAW,

Simulation of the Keyhole Size Effect on the Sound

In order to study the relationship between the intensity of the air-bome-generated sound by

plasma passing through pre-drilled holes and the diameters of these holes, a number of

experiments are performed. For these experiments, a special device is designed and constructed.

>

B

o

a)

14.4 19.2 24 26

Frequency (kHz)

b)

Figure 4. Copper plate with cooling channels (a) andpower spectrum of

sound signalfor hole diameter of3.18 mm

101
Frequency (kHz)

Figure 5. Power spectra ofsoundfor different

hole diameters

Diameter (mm)

Figure 6. Relationship between

power spectrum and hole

diameter at thefrequency of 7.2

kHz

The device is in the form of a copper plate equipped with cooling channels and with a set of

through-drilled holes of different diameters. Cooling of the copper plate prevents the degradation

of the holes by the plasma flow. There are four holes tested with different diameters: 2.38mm,

3.18 mm, 3.98 mm, and 4.78 mm. The cooling copper plate and the sound signal power spectra

are shown in Figure 4a and 4b. The spectrum of sound obtained for this experiment is much

different from the spectrum obtained in the stationary keyholing. The energy of the power

spectrum is mainly focused on the very low frequency interval. Figure 5 shows the spectral

results from all four hole diameters. The area under the curve decreases with an increase in the

diameter. The power spectrum of the sound at frequency of 7.2 kHz (as representative of low

frequencies spectra) as a function of the hole diameter is shown in Figure 6. The results show that

there is a relationship between the hole diameter and the intensity level of the sound generated by

flowing plasma through the holes.

Classification of Keyhole Diameter bv ANN Model in Case of Continuous Kevhole Welding

>

o

Oh

In order to simulate the change of the size of the keyhole, a bead-on plate with changing heat

transfer conditions is used. The two 25 mm slits are placed parallel to the welding line. The

constant welding parameters: welding current, flow rate of inert gas, welding speed, wire feeding

speed, etc.) are optimized in order to

guarantee a keyhole mode. The

keyhole diameter enlarges when the '^^^^^^

torch passes through the region with |^

the slits. This is caused by

worsening of the heat transfer from

the welding zone. The diameter of

the keyhole at the back-side of the

plate as well as the air-borne sound

generated by the plasma at the top

face of the plate are simultaneously

recorded during the welding. A high-shutter-speed camera assisted with a pulsing nitrogen laser

is used to acquire the images of the back-side of the keyhole with frequency of two images per

second. The welded sample with the power spectrum of sound is shown in Fig 7.

Frequency (kHz)

Figure 7. Welded sample with correspondingpower

spectrum ofsound signalfor continuos keyhole welding

102
The classification of GMAW transfer modes using the spectrum analysis results of the sound

signal was previously reported (Refs 10,1 1). In this paper, the analysis of the sound signal is used

to classify the keyhole diameter into classes: small, medium, and large. The three layer neural

network is designed to perform this classification.

The input layer and hidden layer each have 20 nodes, and the output layer has three nodes. For

each second, the sound signal generated by the plasma is processed by a short-time spectral

analysis, and the power spectra uniformly distributed at 20 central frequencies along the whole

spectra are used as the network input. The output has three binary nodes that indicate three

classes of the keyhole diameter (small, medium and large). After the diameter class is

determined, the output node corresponding to this class is set to one and the other two nodes are

set to zeros.

A back-propagation (BP) algorithm is used to minimize the loss function. In order to speed up

the training process, the convergence fast algorithm proposed by Chan and Fallside (Ref.l2) is

used to train the network based on dynamically changing the learning rate. Figure 8a shows a real

time measured diameter

and the corresponding

diameter classification.

The diameter of the

keyhole acquired by the

high shutter speed camera

from the back-side of the

welding plate is classified

i

«3

O

I

a) Time fs) ^) Time (s)

Figure 8. Classification ofkeyhole diameter

a) by measuring b) By ANN

as small, medium, and

large by thresholds of 5.4

mm and 6.6 mm.(up to

5.4 mm~small, from 5.4

mm to 6.6mm including

the boundary—medium, and over 6.6 mm large). Figure 8b shows the test results of the neural

network. The "o" presents the keyhole diameter class predicted by the neural network. From the

test results, the classification error is less than 10%. So, based on the sound signal analysis, the

ANN output could be used to predict the keyhole diameter in the continuos keyhole VPPA

welding and is potentially useful as a feedback signal to control the keyhole diameter.

CONCLUSION

A sound sensing technique is used to classify the keyhole size in VPPAW into small, medium,

and large. The experimental results show that the sound signal can be used to determine the

instant when the keyhole is established as well as to clearly distinguish among the modes of the

molten pool: no-keyhole mode, transition mode, and keyhole mode.

103
ACKNOWLEDGEMENT

This work was financially supported by NSF's Grant No. DMI-9900011 and by the U.S.

Department of Education, Grant No. P200A80806-98, and by the Brown Foundation.

REFERENCES

1. Nunes A. C. 1984. Variable polarity plasma arc welding on space shuttle external tank,

Welding Journal 63(4): 27-s to 35-s.

2. Martinez Luis F. 1994. Effect of weld gases on melt zone size in VPPA welding of Al 2219.

Welding Journal 10: 50-s to 55-s.

3. Howard Woodward. A. 1996. U.S. Contractor for the International space station, Welding

Journal 3: 35-s to 40-s.

4. Martinez L. F. 1992. Front side keyhole detection in aluminum alloys, Welding Journal 5: 49-

s to 52-s.

5. Zheng, B., Wang, H., Wang, Q., and Kovacevic, R. Control for weld penetration in VP?AW

of aluminum alloys using the front weld pool image signal. Welding Journal/Research

Supplement. Dec.2000, p363s-370s.

6. D. Farson, K. Hillsley, J. Sames and R. Young. 1996. Frequency-time characteristics of airborne signals from laser welds. Journal of Laser Application. 7., p33-p42.

7. D. Saini and S. Floyd. 1998. An investigation of gas metal arc welding sound signature for

on-line control. Welding Journal/Research Supplement. Apr., pl72s-179s.

8. T. Morita, Y. Ogawa and T. Sumitomo. 1995. Analysis of acoustic signal on welding and

cutting, 1995 OMAE-Volume III. Materials Engineering ASME , p547-p552.

9. M. C. Jon. 1985. Non-contact acoustic emission monitoring of laser beam welding, Welding

Journal. Sep, p43s-p48s.

10. Metteson,A., Morris, R., & Raines, D.,An 1992. Optimal artificial neural network for

GMAW arc acoustic classification Proc. 3rd International Conference Trends in Welding

Research. Gatlinburg, TN.

ILL. Quintino. R.Ribeiro. J.Raria. 1999. Classification of GMAW transfer modes using neural

networks" Int. J for the Joining of Materials. 1 1(1), 6-8.

12. Chan, L.W. and Fallside, F.1987 An adaptive training algorithm for backpropagation

networks. Computer Speech and Language. 2, 205-218.

104
SYSTEMS TECHNOLOGY FOR VARIABLE POLARITY

PLASMA ARC WELDING OF ALUMINUM ALLOYS

B. Zheng*

ABSTRACT

This paper summarized further researches in variable polarity plasma arc welding of aluminum

alloys on the issues of arc behavior, monitoring of a keyhole, and real-time closed-loop control

for weld formation. Development of a unique welding power source system, configuration of a

machine vision system to monitor a keyhole weld pool, and implementation of an intelligent

closed-loop control for weld formation have been made. The results achieved by both experiment

and theoretical analysis show an efficient way to systematically improve the weld quality.

INTRODUCTION

Aluminum is one of the important structural materials in industry. However, the tenacious oxide

film existing on its surface strongly impedes the flow of molten metal in the weld pool during

welding and easily leads to the formation of poor welds. Also, the hydrogen that is easily

absorbed in the weld pool becomes trapped to form porosity as it solidifies (Ref. 1-2).

Variable Polarity Plasma Arc Welding (VPPAW) of aluminum alloys has been developed and

applied to the fabrication of key products such as space shuttle external fuel tanks and nuclear

magnetic resonance devices. This welding technology features higher quality and productivity at

relatively lower cost: it removes the oxide film on the workpiece surface and generates a unique

keyhole-mode weld pool that forces the porosity to be flushed out of the weld pool (Ref. 3-12).

However, there are still some crucial problems to be solved, such as severe deterioration of the

tungsten electrode, short service life of the nozzle, arc instability, unstable penetration state, and

inconsistency of weld formation (Ref 4-5 and 12-20).

This paper presented further research of VPPAW technology on arc behavior, monitoring of a

keyhole, and real-time closed-loop control for weld formation.

EXPERIMENTAL PROCEDURE

The experimental setup consists of a welding system and a monitoring and control system for

weld quality. The welding system are the following: an AC plasma arc power source, a DC pilot arc power source, a welding current redistribution device, a programmable sequence controller, a

plasma gas controller, an all-position supporter for the workpiece, a computer-controlled wire

feeder, and a water-cooled plasma torch rated at 300 amperes. The hardware of the monitoring

and control system includes a CCD camera with a band-pass filter, a monitor, a frame grabber, a

VCR, a data acquisition system, and a PC-Pentium computer. The camera is attached to and

positioned in front of the plasma arc torch with its axis at an angle ((])) of 43° from the workpiece

surface plane. The software functions as a system platform that integrates the welding system

' Edison Welding Institute, 1250 Arthur E. Adams, Columbus, OH 43221

105
and the monitoring and control system for weld quality. Main modules that were designed are: welding operation routine, arc current redistribution control, welding parameters and weld pool

image acquisition, image processing routine, keyhole monitoring, and control decision making.

Butt-jointed welds of 5.0-10.0 mm-thick 2024 aluminum plates are made under the shielding of

pure argon with wire feed. Welding parameters have the following ranges: a DCEN to DCEP

time duration of 13-22 ms to 3.0-7.0 ms, DCEN current 80-210 A, DCEP current 80-240 A,

welding voltage 25-33 V, pilot arc current 15-30 A, plasma gas flow rate 4.5-5.5 1/min.,

shielding gas flow rate 6.0-8.0 1/min, welding speed 80-100 mm/min, torch stand-off distance

5.0-6.0 mm, angle between workpiece surface plane and the horizontal plane 85°-90°, orifice

diameter 3.2-4.0 mm, orifice length 3.5-4.0 mm, tungsten electrode diameter of 4.0 mm, and a

tungsten electrode setback of 3.2-4.0 mm.

RESULTS AND DISCUSSIONS

Arc behavior

Arc behavior explored in this paper includes the stability of a combined arc (consisting of an AC

plasma arc and a DC pilot arc) and the characteristic of heat and force generated from a plasma

arc. The combined AC arc welding can guarantee the initiation of a plasma arc in the beginning

of each period of direct current electrode positive (DCEP). However, in the rest part of each

DCEP period, partial current from an AC power source may flow through a DC pilot arc power

source and partial current from the pilot arc power source may also flow through the AC power

source. The existence of this type of current flow will result in double arc that makes the weld

formation worse and sometimes damage the rectifier inside the pilot arc power source. Although

a resistor with the variable resistance can be added to the output circuit of the pilot arc power

source to limit this type of current flow, the potential of the nozzle that contacts the arc column

will be changed under this condition. Since the directions of both electric field and current for

the pilot arc are completely adverse to those for the plasma arc in the DCEP period, the change

of the nozzle potential may lead to an extremely unstable arc discharge (arc interference) because

of the neutralization of electric charges (Ref 13). To guarantee the weld quality, a trade-off

between initiation of each DCEP plasma arc and avoidance to the arc interference must be

electrically made. A unique solution to this is shown in Fig. 1, which makes use of the pilot arc and the arc between the nozzle and the workpiece at the transition fi-om the DCEN period to the

DCEP period during Current Redistribution Device

the welding process. , I \

The DC power source I gtrsJ otriJ J ^ V^'^-^ |

T""fften

p ^ ^g4s

[^

scR4

1 is also used as a

continuous welding

power source, but its

output current is

changed into an AC

current with the help of

transistors GTRi

through GTRs, which

forms an H-bndge. The p.^ ^ ^ Push-pull-arc Solution to Arc Interference in AC PAW

1—DC welding power source 2—AC welding power source

106
Current Modulation During

DCEN Period

/

Fig. 2 Waveforms of Welding

Currents and Voltages Related to

Push-pull-arc Solution

comes from the plasma arc column to

heat and melt the worlq)iece. 2) More

heat is generated in the DCEN period

than in the DCEP period. In

applications, a maximum-width

cathodic cleaning area is accepted as a

rule if no special consideration should

be given to the deterioration of the

tungsten electrode. To evaluate and

better take advantage of an AC

plasma arc as well as improve the

weld quality, the following

investigation is made. Welding

currents with different AC frequencies

shown in Fig. 3 (50 Hz, 25 Hz, 16.7

Hz, 12.5 Hz, and 4.54 Hz) are used by

enabling SCRa-SCRe, Di, and D2 in

Fig. 1. The corresponding ratios of

current Ipi from power source 1 is used as a pulse

current for initiating the plasma arc at the beginning

of each DCEP period. This means that the arc between the tungsten electrode and the nozzle is a

pilot arc for a very short period: from t2 to t4, as shown in Fig. 2. So, the pilot arc is a

discontinuous arc. After the plasma arc is initiated,

during the following time (from ts in the DCEP

period to t2 in the next DCEN period), the current

from the DC welding power source 1 is also used as a part of the plasma arc current. The current will

flow through the conducting plasma arc column

between the tungsten electrode and the worlq)iece.

Consequently, the pilot arc and the plasma arc

consist of a push-pull arc: the DCEP plasma arc is

initiated with the help of the discontinuous pilot arc

that is initiated with the help of the DCEN plasma

arc. A weld bead with a high depth-to-width ratio is

preferable for AC plasma arc welding (PAW) of

aluminum alloys in the keyhole mode, which is

directly related to the heat and force generated from

an arc. For AC PAW of aluminum alloys, two

statements have been made. 1) The temperature of

the plasma arc column is higher than that for other

types of arc columns. Therefore, compared with the

cathodic and anodic regions, much more heat

20 ms 13.6 ms ..6.4 ms

Fig. 3 AC Voltage Waveforms with Different

Frequencies

a. 50 Hz b.25Hz c. 16.70 Hz d. 12.5 Hz e. 4,54 Hz

107
• Cleaning width * Crown width Back luidth

5 10 15 20 25 30 35 40 45 50 55 60

Frequency (Hz)

Fig. 4 The Widths of the Weld Bead

and the Cathodic Cleaning Area vs. AC Frequency

DCEP duration to DCEN duration are 47.06%,

19.05%, 11.94% and 8.70%, respectively. The

voltage for each of the weld beads was initially an

AC frequency of 50 Hz, and then it was switched to

an AC frequency corresponding to Fig. 3. The

resultant widths of both the weld beads and the

cathodic cleaning area versus the AC frequency are shovm in Fig. 4. It can be seen that the width and

penetration of a weld bead decrease as the voltage

frequency decreases. When the frequency of the

voltage is changed to 12.5 Hz, the workpiece is not

penetrated by the arc. It should be mentioned that the

width of the cathodic cleaning area, in this case, is

enough to form a weld bead with a smooth surface.

In other words, the weld bead with the narrower

width and partial penetration resulted from less energy input, not from an insufficient cathodic

cleaning area. Another interesting phenomenon observed is that the cathodic region spots on the

workpiece surface move freely and quickly in a half circular edge ahead of the keyhole weld

pool. Thus, this area may get more heat because the DCEP current goes through it into the

workpiece and is larger than the area heated by a DCEN arc due to the scattering DCEP arc. The

above results reveal that the cathodic region also plays the main role in the heating and melting

of the workpiece. Calculated results for the corresponding experiment show that although the

heat supplied to the plasma arc by the power sources in the DCEN period is more than that in the

DCEP period, the heat resulting from the cathodic region potential (11~13 V) is more than that

from the anodic potential (1~2 V). During the welding, the role of the DCEP arc, scattered on the

semi-circular edge in front of the keyhole weld pool, is not only to bombard the oxide film on the

workpiece but also to provide the coming DCEN arc with heat for melting and penetrating the

workpiece. The DCEN arc concentrates on the central spot of the semi-circular edge to pierce

through the workpiece. Therefore, if a maximum-DCEN-duration approach is used, enough heat

must be generated in the cathodic cleaning region during the DCEP period in order to achieve a

high depth-to-width ratio weld bead. For a keyhole AC PAW of aluminum alloys, good weld

formation can not be guaranteed if the cathodic cleaning area is too wide.

The capability of deep penetration from a plasma arc is attributed to the higher arc pressure

resulting from the nozzle orifice, cooling arc column, and radial electromagnetic force inside an

arc. However, the mechanism of this arc pressure generation has not been completely disclosed

(Ref. 17-19). The velocity of a plasma arc jet is a direct measurement of its pressure: the higher

its velocity, the larger its pressure. The exit velocity V2 of a plasma arc jet coming out from an

orifice can be expressed as (Ref. 13):

Qp T2 M2 P2

V2 = —X — X — X — X (

1 + ct)

A2 Ti Ml Pi

(1)

where Qp is the plasma gas flow rate, A2 is the cross-sectional area of the orifice at the exit, Ti,

Ml, Pi and T2, M2, P2 stand for the temperature of plasma arc jet, molecular weight of the plasma

gas, and atmosphere pressure, respectively at the entrance and exit of the orifice, and a is the

108
ionization intensity of the plasma arc jet. According to this formula and the experimental data,

different calculated velocities of the plasma jet under different conditions are listed in Table 1.

Table 1 Calculated Velocity of a Plasma Arc Jet under Various Arc Conditions

Different Conditions

Velocity of Plasma Arc Jet

(m/O Common Conditions

An arc is not initiated 5.31

Ti-298°K,

T2=13500°K,

Pi=l .01 33x10 N/m , P2=l. 01 33x10^ N/m^

M1=39.95 g/mol,

M2=39.95 g/mol,

Qgas=4.0xlO"^mVmin,

Onfice diameter and length

are respectively 4.0 mm and

4.0 mm,

An arc is initiated, but no

consiuerdiion or piasuid gas

ionization is made (a=0).

An arc is initiated and the

consideration of plasma gas

ionization is made (a=0.46)

351.30

An arc is initiated and the

consideration of both plasma gas

and arc static electromagnetic

force is considered.

386.43

The results show that the velocity of the plasma arc jet depends heavily on its temperature that

relates to the level of the welding current. This temperature-related component of the velocity is

the major contribution to the arc pressure. The arc pressure is generated by the dynamic pressure

resulting from the arc temperature, the static pressure from the electromagnetic force inside an

arc, and the restricted orifice dimensions that provides a mechanical compression on an arc column. To simplify the control for weld formation, efficiently dynamic regulation of the arc pressure is required. However, the necessity of constant heat input to a weldment with certain

thickness limits the regulation of arc pressure to adjusting the welding current level upon which

the dynamic pressure of a plasma arc jet depends. Also, no quick adjustment to the arc pressure

during a welding process can be achieved by regulating the plasma gas flow rate that influences

the temperature of an arc column. In addition, it is impractical to change the orifice dimensions

during a welding process. Consequently, the only way to regulate arc pressure is changing the

static arc pressure resulting from the electromagnetic force of an arc. Accordingly, the current

redistribution device, shown in Fig. 1, is designed to modulate welding current (see Fig. 2)

during the DCEN period so that static arc pressure can be regulated. The results achieved showed

that the crown and root bead widths significantly decrease as the modulation frequency for

DCEN current increases up to 1 3 KHz, which was very favorable to maintaining a weld pool with

small dimensions when the total heat input to the workpiece was kept nearly constant.

A VPPAW process can be divided into three segments: (1) start-up segment, during which the

keyhole is generated; (2) main body segment, during which the keyhole is dynamically

maintained; and (3) the terminal segment, during which the keyhole collapses and the crater is

filled. Very often, a smooth transition can not be made from the start-up segment to the main

body segment and the weld fails because the keyhole weld pool is not properly generated. The

keyhole weld pool during the start-up segment can be categorized as one of three modes: heat

conduction mode, strong force mode, or heat and force mode. The reason for generating a heat

109
(200,0)

conduction mode is that the weld pool melts through mainly by the heat accumulation in the

workpiece. A higher plasma gas flow rate accompanied by a lower initial welding current and

shorter welding current rise time will result in a higher and more concentrated arc force, which

will generate a strong force mode weld pool. The heat and force mode can be achieved using the

appropriate heat accumulation and proper temperature gradient between the weld pool and heat

affected zone. The efflux through

the keyhole can not expand freely

because of the limited sizes of the

weld pool and the keyhole. Using

this mode (achieved by increasing

both the plasma gas flow rate and

the welding current from their

initial values to the values for main

body segment) the transition from

the start-up segment to the main

body segment is smooth and the

as-welded result is good.

(0,0)

(0,60)

(0,80)

1(100,0)

JH..^ 1

S

b(ioa,so)

(200,80)

a

Fig. 5 Schematic for Image Processing

a. Image division

b. A keyhole traced by the algorithm for image processing

Visual sensing a keyhole weld

pool

The keyhole is actually a cavity

through the weld pool, with a

profile resembling a trumpet. The

cross section of this cavity varies

both in diameter and shape

(irregular ellipse). The maximum

cross sectional diameter occurs at

the top surface of the workpiece

(top section diameter). The cross

section with the smallest diameter

is located about 1.5-2.5 mm up

from the bottom surface of the 6.0

mm thick workpiece (throat section

diameter). The diameter of the cross section on the bottom surface

of the workpiece is larger than the

throat diameter, but less than the

top section diameter. Therefore,

the keyhole weld pool is a

deformed weld pool, and is much

different from the weld pool in

GTAW and GMAW. A typical

image of a keyhole weld pool with

wire feed is shown in Fig. 5, the

black trace line on which is

E

TS E —(D E—

o

ordinary wori<piece

varied heat sinl< workpiece

oj 8500

•If 6750

CD .2< ^ S 5000

1 S 3250 o

1500

a, 210

tf 190

I 1 170

I i

150

£ '5 130

110

60 90

Time (s) a >

——ordinary workpiece

A varied heal sink workpiece

1 1 1 1 1 1—1—t—1

»

30

o) 80

60 90

Time (s)

120 150

60 90

Time (s)

Fig. 6 Variation of Keyhole Parameters

a. Bottom diameter of a keyhole b. Nominal keyhole area

c. Nominal keyhole width d. Nominal keyhole length

110
achieved after image processing operation and stands for the boundary of an enclosed keyhole.

The thermal state of a weld is the most crucial and complex factor influencing weld formation

because of its variability, immeasurability, and uncontrollability. In a keyhole welding process,

the cutting usually occurs easier than in the melting-in mode welding process. Once a cutting

process occurs, the weld bead is not acceptable and must be repaired. Thus, avoiding a cutting

process that results mainly from the variation in thermal conditions is the first priority and a

prerequisite for achieving consistent uniformity of a weld bead (Ref. 13, 19-23). Fig. 6 shows the

variation of the bottom diameter of a keyhole with the time when the thermal conditions of a

workpiece are changed. The origin of the time axis represents the instant when workpiece

movement begins after the keyhole size reaches a certain value. The off-line measured width

between the bottom oxide films along a weld bead, which is the trace of the arc efflux, is used

for representing the bottom diameter of a keyhole. It can be seen that the variation in bottom

diameter of a keyhole agrees well with the monitored keyhole size acquired from an sensed

image.

Weld formation control

An artificial BP neural

network of three layers with

9 parameters as inputs and

the bottom diameter of a

keyhole as an output are used to identify the

penetration state. The

number of elements in the

hidden layer is

experimentally determined

as six based on the training

results for the model.

The keyhole size may be

regulated by adjusting the

wire feed speed and the heat

input may be controlled by

changing the welding

current. The wire feed speed

is selected as the first

controlling variable for the

control model to avoid the

occurrence of a cutting

Fig. 7 A Weld Bead in a Butt Joint with the Closed-loop

Control of Consistent Weld Formation Using both Wire

Feed Speed and Welding Current as Controlling Variables

a. Top face b. Bottom face

process. To achieve the consistent uniformity of a weld bead during a welding process, the

welding current should be controlled for regulating the heat input to the workpiece.

Corresponding photographs of the weld bead, shown in Fig. 7, show that the consistent

uniformity of a weld bead in VPPAW of aluminum alloys by the keyhole mode can be made in

real time by closed-loop feedback control.

Ill
CONCLUSIONS

Problems related to quality welds in variable polarity plasma arc welding of aluminum alloys are filmy surface oxide, deterioration of tungsten electrode, unstable penetration state, arc instability,

short service life of a nozzle, inconsistent weld formation. To solve these problems, arc behavior,

online front face sensing and monitoring of a keyhole, and real-time closed loop control for

consistent uniformity of weld formation have to be addressed. Arc performance can be improved

greatly by applying an innovative push-pull variable polarity arc. The establishment of a quality

keyhole weld pool during the start-up segment is one of the key techniques. This can be

implemented by ramping up both welding current and plasma gas flow rate. The dynamic

keyhole dimensions measured accurately reflect the state of weld pool. The real-time intelligent

control using both wire feed speed and welding current as controlling variables can guarantee the

consistent uniformity of weld formation even when thermal conditions of a butt-jointed

weldment change.

ACKNOWLEDGEMENT

The author expresses his great acknowledgement to the supports of National Key Laboratory of

Advanced Welding Production Technology, P. R. China and Edison Welding Institute,

Columbus, Ohio.

REFERENCES

1. Craig E.. 1988. The Plasma arc process—A review. Welding Joumal 2: 19 to 25.

2. Tuttle W.. 1991 . Understanding aluminum welding. Welding JoumaU 70: 43 to 46.

3. Vancleave B. P.. 1981. Keyhole plasma arc welding of aluminum. American Welding

Societv. 2501 N. W., Seventh St. Miami, Fla, 33125, [Proc. Conf 1980].

4. Tomsic M. and Barhost S.. 1984. Keyhole plasma arc welding of aluminum with variable

polarity power. Welding Joumal 63(2): 25 to 27.

5. Nunes A. C. and Bayless E. O.. 1984. Variable polarity plasma arc welding on space shuttle

external tank. Welding Joumal 63(4): 27 to 35.

6. Howard Woodward. 1996. A U.S. contractor for the international space station. Welding

Joumal 3:35 to 40.

7. Irving B.. 1992. Joining experts meet to exchange ideas about welding in space. Welding

Joumal 1 :67 to 69.

8. Barhorst S.. 1985. The cathodic etching technique for automated aluminum tube welding.

Welding Joumal 5: 28 to 31.

9. Kyselica S.. 1987. High-frequency reverse arc switch for plasma arc welding of aluminum.

Welding Joumal 1: 31 to 35.

10. Eagar T. W.. 1989. Recent trends in welding science and technology.. ASM Intemational. S.

A. David, Ed. P. 341, Materials Park, Ohio

11. Pang Q. and et al.. 1994. Workpiece cleaning during variable polarity plasma arc welding of

aluminum. Transactions of ASME, Joumal of Engineering for Industry 1 16(1 1): 463 to 466.

112
12. Torres M. R.. 1992. Gas contamination effects in variable polarity plasma arc welded

aluminum. Welding Journal 4:123 to 130.

13. Zheng B.. 1995. The stability of weld formation in kevhole plasma arc welding of aluminum

alloys. Ph. D. dissertation, Harbin Institute of Technology, P. R. China

14. Martinez L. F.. 1994. Effect of weld gases on melt zone size in VPPA welding of Al 2219.

Welding Journal 10:50 to 55.

15. Jiang H. Z.. 1988. Arc welding and slag welding: Beijing, P. R. China: Mechanical Industry

Publications.

16. Lancaster J. F.. 1986. The physics of welding: 2nd ed. Edition: New York: Pergamon Press.

17. Ando K.. 1985. Arc phenomena: Beijing, P. R. China: Mechanical Industry Publication

(Translated version from Japanese).

18. Martinez L. F.. 1992. Front side keyhole detection in aluminum alloys. Welding Journal 5:

49 to 52.

19. Metcalfe J. C. and Quigley M. B. C. 1975. Keyhole stability in plasma arc welding. Welding

Journal 11:401 to 404.

20. Steffens H. D.. 1972. Automatic control for plasma arc welding with constant keyhole.

Welding Journal 6:40 to 45.

21. Agapakis E. and Bolstad J.. 1991. Vision sensing and processing system for monitoring and

control of welding and other high luminosity processes. International Robots & Vision

Automation Conference : 23-29.

22. Nakata S; Huang J.; and Tsuruha Y.. 1988. Visual sensing system for in-process control of

arc welding process. Welding International 12: 1086 to 1090.

23. Hoffman T.. 1991. Real-time imaging for process control. Advanced Materials & Processes

9: 37 to 43.

113

Session A4: Imaging

115

CHARACTERIZING THE MICROSTRUCTURE OF A GTA WELD IN-PROCESS

USING HIGH-SPEED, HIGH-MAGNIFICATION, DIGITAL IMAGING

A.C. Hall*, G. A. Knorovsky*, C. V. Robino*, J. Brooks^

D. O. Maccallum*, M. Reece*, G. Poulter*

ABSTRACT

A high quality zoom lens and a high-speed CCD camera have been used to image gas tungsten

arc (GTA) welds in stainless steel. Both the trailing (solidifying) edge and the leading (melting)

edge of the weld pool have been observed. A number of solidification phenomena have been

captured including: dendrite growth, melting, and weld ripple formation. Significant information

about the evolution and structure of the solid-liquid interface can be extracted from these videos

using computerized image analysis techniques. This information can be directly related to the

microstructure of the finished weld. Video clips will be presented, techniques for extracting

microstructural information from those clips will be discussed, and the extracted information will

be related to the microstructure of the finished weld.

KEYWORDS

WELD VISUALIZATION, MACHINE VISION, WELD RIPPLE, SOLIDIFICATION,

MELTING

INTRODUCTION

Over the last decade, significant advances have been made in digital photography and related

computer technology. Combination of these technologies has given rise to a field known as machine vision. It is now relatively straightforward to capture and process images using a

computer. Robust software exists that allows the user to extract quantitative measurements from

digital images. This technology has found widespread application in the world's manufacturing

industries [1-3]. Parts are routinely inspected using machine vision technology to see that they

have been assembled properly or manufactured to desired tolerances. Surprisingly, this

technology has not been widely adopted in the scientific community. It affords the researcher

with a powerful new tool set that allows large amounts of information about dynamic physical

processes to be accessed and extracted in an automated fashion. Solidification of metals is a

field that is ripe for the exploitation of such technology.

Conventional techniques for inspecting the solid-liquid interface in a metal typically involve

rapidly quenching a sample and then polishing, etching, and examining the quenched sample [4-

6]. Experimental techniques based on such schemes are very useful but have a significant

* Sandia National Laboratories; Albuquerque, NM

^ Sandia National Laboratories; Livermore, CA

117
limitation. They only provide the researcher with a "snapshot" of the solid-liquid interface. No

direct information about the motion or the evolution of the solid-liquid interface is available from

a quenching experiment. The only experimental schemes that do provide information about the

motion and the evolution of the solid-liquid interface are based on visualization of transparent

materials (like succinonitrile) that solidify in a manner analogous to metals [7, 8]. These

experiments have provided a wealth of information about dendrite growth and the evolution of

the solid-liquid interface. However, these experiments have a significant limitation. Transparent

materials often do not exhibit the complex phenomena that occur during the solidification of

multi-component engineering alloys. Solidification mode transformations, which are commonly

observed in stainless steels [9], are an example of complex phenomena that are not exhibited by

transparent materials. Complex phenomena like this demand new experimental techniques that

can provide information about the dynamic behavior of solid-liquid interfaces in engineering

materials.

In this paper, we describe a technique for using machine vision technology to characterize the

behavior of the solid-liquid interface in a gas tungsten arc (GTA) weld. Dendrite growth,

melting, and weld ripple formation have all been observed at high spatial and temporal

resolution. A large amount of quantitative data can be extracted from these experiments in a

relatively straightforward manner. An example showing how data is extracted and related to the

microstructure of the finished weld is discussed in detail.

EXPERIMENTAL PROCEDURE

The solid-liquid interface in a GTA weld was filmed using a Kodak EktaPro camera equipped

with a Navitar® Zoom 6000 lens. The Kodak EktaPro camera is capable of capturing digital

images at up to 12,000 frames / second. In practice, we have found that filming at 2000 frames /

second provides temporal resolution that is more than adequate for a GTA weld. The Navitar®

Zoom 6000 lens is designed specifically for machine vision. It is a parafocal zoom lens system

capable of imaging objects within fields of view ranging from 146.6mm to 0.03mm on each side.

Figure 1 : Camera, lens, X-Y-6 stage, and welding torch

118
The camera and welder are fixed with respect to each other and the sample is translated below

them. See Figure 2. This arrangement keeps the solid-liquid interface stationary with respect to

the camera.

Figure 2: Schematic showing the arrangement of the camera, sample, and welding torch

The entire apparatus (camera, motion control stage, and welding torch) is contained inside a

glove box so that an inert atmosphere can be maintained around the weld. This prevents oxide

from forming on the surface of the weld pool and obscuring the phenomena of interest. Welds

were made at a variety of amperage, arc length, and speed combinations. Solidification and

melting phenomena were filmed by pointing the camera at the trailing or leading edge of the

weld pool.

RESULTS AND DISCUSSION

Figure 3 is a still image taken from a video of a 150 amp GTA weld in 304 stainless steel

traveling at 4.2 mm/sec. Dendrites can be seen at the solid-liquid interface.

Figure 3: Dendrites at the solid-liquid interface in a GTA weld in 304 stainless steel

119
Figure 4 is a still image showing melting in Inconel 718. Liquation of the interdendritic laves

and/or niobium carbide eutectic-like constituents can be seen at the solid-liquid interface. Figure

5 is a still image showing weld ripples in 304 stainless steel. Multiple weld ripples can be seen

behind the solid-liquid interface.

Figure 4: Melting at the solid-liquid interface in Inconel 718, liquation can be seen at the

interface

Figure 5: Weld ripples at the trailing edge of a GTA weld in 304 stainless steel

Data Extraction

A large amount of information about the solid-liquid interface and its motion with time is present

in these video recordings. Quantitative information can be extracted from these video clips in a

relatively straightforward manner using computerized image analysis schemes. We have

accomphshed this using National Instruments^" Lab View and IMAQ Vision software.

Our image analysis scheme proceeds as follows. First, a digital movie is recorded as a series of

image files. Each movie typically contains between one and two thousand image files each of

120

j
which are 500KB in size. Image analysis software is used to open each image file in sequence

and measure a feature of interest (a dendrite arm spacing, the location of an interface, etc. . .). All

measurements are made in a pixel coordinate system that uses the top left comer of the image as an origin. After each measurement is made, it is associated with the frame number of the image

and written to an output file. Frame number and pixel coordinates must then be converted into

meaningful units like time and position. Frame number is easily translated into time by

multiplication with the framing rate, A dimensional measurement like primary arm spacing

requires a simple unit conversion to an appropriate length measurement. Filming an accurate

scale provides this, as well as a velocity calibration. A measurement like interface velocity

requires a frame of reference correction in addition to a unit conversion. A frame of reference

correction is necessary because all measurements are made in the camera frame of reference. If

information like interface velocity is to be translated into the sample frame of reference the stage

velocity must be measured accurately and accounted for.

Example: Weld Ripple

In the video that Figure 5 was extracted from the motion of the solid-liquid interface that is

associated with the formation of a weld ripple was seen. That motion was quantified by

measuring the position of the solid-liquid interface in each frame of the video. The interface

position is determined from a marked intensity change in the digital image. Figure 6 is a graph

of this information. The upper curve shows interface position in pixels coordinates with respect

to time.

I

8

s

e

45

SO

Interface Position

^ 55

60

65

70

75

1.25

0.75

0.5

0.25

kTterfac^elocityJ

-0.25 0.000 0.020 0.040 0.060

Time (seconds)

0.080 0.100

»

o

«<•

o

o

3

3

(Do

o

3

a.

Figure 6: Data from machine vision analysis of a rippling weld.

The raw data associated interface position with frame number. Converting frame number to time

was simple. This video was filmed at 1000 frames / second with an exposure time of 1/1000

121
second. Dividing the frame number by 1000 converted the X-axis to time in seconds.

Converting the Y-axis was also straightforward. At this magnification 360 pixels is equivalent to

2 mm, so dividing the Y-axis coordinates by 180 gave the interface position in mm. This

position was measured relative to the camera. To measure interface velocity in the frame of

reference of the sample we had to account for the stage velocity. This was done by numerically

differentiating the position versus time data and adding a vector representing the stage velocity to

the differentiated curve. The addition of this vector placed the data in the frame of reference of

the sample. The converted data is also shown in Figure 6. The interface behavior associated

with the formation of a weld ripple can now be clearly seen. When a ripple is formed the solidliquid interface appears to go thorough a cycle of advancing and remelting.

Evidence of this change in interface velocity can be seen in the microstructure of the finished

weld. Figure 7 is a micrograph showing the transverse cross section of a rippled weld in 304

stainless steel. Light colored bands in the shape of the solid-liquid interface can be seen in the

image. Similar features can be seen in longitudinal cross sections of rippled welds. These

features are associated with the ripple formation seen in the video. As shown in Figure 7, the

ripple bands extend all the way to the bottom of the weld pool.

Figure 7: Cross section of a GTA weld showing evidence of rippling in the microstructure.

Like the metallographic cross section shown in Figure 7, the video clips mentioned above are two-dimensional representations of three-dimensional phenomena. Microstructural evidence of

cyclic interface motion deep within the weld pool suggests that data gathered at the surface of the

pool can be correlated with phenomena that occur deeper in the pool.

SUMMARY AND CONCLUSIONS

The example given above illustrates how machine vision technology can be used to measure the

solid-liquid interface velocity associated with weld ripple formation. Computerized image

analysis techniques were used to process a large numbers of images. This allowed solid-liquid

interface velocity to be measured with high temporal resolution. A cyclic solid-liquid interface

122
velocity was observed and was associated with microstructural features present throughout the

depth of the weld. This suggested that data collected from this type of experiment can provide

information about phenomena deep within a weld pool. By appljang the same technique to the

other videos mentioned in this paper, primary and secondary dendrite arm spacing, and local

melting velocity can also be measured as a function of time and welding conditions. Clearly

machine vision has tremendous potential as a data collection technique for scientific

experiments. This potential is not limited to solidification science; in fact machine vision could

be used to study almost any dynamic phenomena. In the case of solidification science it has

allowed us to view and quantitatively measure the dynamic behavior of the solid-liquid interface

in an engineering material under actual welding conditions.

ACKNOWLDEGMENT

Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin

Company, for the United States Department of Energy under Contract DE-AC04-94AL85000

REFERENCES

1. Zuech, N., When you're hot, you're hot: Machine vision and the semiconductor roller

coaster. Advanced Imaging, 2000. 15: p. 3.

2. Babb, M., Machine Vision Moves into The Mainstream of Manufacturing. Control

Engineering, 1995 (July 1).

3. Hogarth, S., Machines with vision. Manufacturing Engineering, 1999. 122: p. 102-104.

4. Brooks, J.A., N.Y.C. Yang, and J.S. Krafcik, On the origin offerrite morphologies of

primaryferrite solidified stainless steel welds. Int. Trends Weld. Sci. Technol., Proc. Int. Conf Trends Weld. Res., 3rd, 1993: p. 173-80.

5. DuPont, J.N., et al, Solidification of Nb-bearing superalloys: Part II. Pseudoternary

solidification surfaces. Metallurgical and Materials Transactions a-Physical Metallurgy

and Materials Science, 1998. 29(11): p. 2797-2806.

6. Huang, X.B., et al, Effect of solidification rate on the microstructure of a Ni-base

superalloy. Materials Transactions Jim ;, 1997. 38(11): p. 1016-1021.

7. Glicksman, M.E., et al, Dendritic Growth of Succinonitrile in Terrestrial and

Microgravity Conditions As a Test ofTheory. Isij International, 1995. 35(6): p. 604-610.

8. Glicksman, M.E., et al. The isothermal dendritic growth experiment. Solidification and

Gravity, 1996. 215: p. 179-189.

9. Elmer, J.W., S.M. Allen, and T.W. Eagar, Microstructural Development during

Solidification of Stainless Steel Alloys. Metallurgical Transactions A, 1989. 20A

(October): p. 2117-2131.

123

Vision-based Weld Defect

Detection and Depiction

C. Reichert

Applications Engineer

Edison Welding Institute

614-688-5247 EUlf

Overview

• Principles of Laser-Based Machine Vision

Technology

• Software Aspects and Involvement

• Inspection Examples

- Pipeline Corrosion - Fillet Weld Surface Quality

EUJi

125
Why improve on Visual Testing?

• Advantages of VT

- Efficient, little operator training, relativr'y

inexpensive

• Disadvantages of VT

- Limited to detection of surface

discontinuities

- Subjectivity on assessment of weld

- Existing codes and specs may lack

sufficiently clear and objective criteria for

all weld surface conditions encountered

EUlf

Laser-Based Machine Vision Technology

• Principles of Triangulation • Assembling 3D measurements

• Types of Laser Sensors

-Spot

- Laser Line/ Spray of Light

EUJi

126
Principle of Triangulation

ELUi

Laser Spot (1D) & Laser Line (2D)

[if

127
Laser-Line System

Height Pixels

CCD Camera

Movement

of sensor

Planar pixels

Weld to be

inspected

ELUi

Principles of Operation

• CCD Camera

- Laser light is captured by tlie "detector" which is a

grid of charge-coupled devices (CCD)

• Two Types of Data Obtained From CCD Detector:

- Range Data-Quantitative Data used for

measurements of surface profile

- Intensity Data-Light Reflectance Level Data used

for reconstructing image

EUJi

128
1

CCD Image of Fillet Weld

EUlf

Data Processing

• Software is the Key

- The laser is simply the tool to gather data - Software is the means for transforming the data

into actual measurements

• Examples of Processing the Range Data

- Segmentation

- Curve Fitting

EUJi

129
Software Examples .

• Segmentation of Fillet

Weld Range Data

• Line Segmentation &

Curve Fitting

0.000 0.500 1.000 1.500 2.000 2.500 3.000

x(inche8) ELUi

Inspection Example:

Pipeline Crawler with Spot Sensor

12 EUlf

130
Principles of Operation of Crawler

• Small, portable robotic vehicle with magnetic

wheels

• Motorized circumferential slide

• On-board computer

• Field-ruggedized for harsh environments

• 100-ft umbilical cable attached to workstation

• Laser sensor moves along circumferential

slide taking measurements

ElUi

131
1

Benefits of Pipeline Inspection System

• Data collection Efficiency/Reduced Inspection

and Data Assessment Time

• Operator Independence

• Increased Data Quality and Integrity • Baseline for Comparison with Other

Technologies (calibration for other tools)

EUJI

Fillet Weld Inspection

• Industrial Need for Automated Inspection

• Advantages of a Laser-based System

• Previous Developments

• Current Developments

ElUi

132
Previous Developments

• Portable System for Inspection of Fillet Welds

for Shipbuilding Industry - Hand-held system - Measured 2 and 3-dimensional defects

• Development of System for Inspection of

Girth Weld on Pipe - Measured Toe Angles and Radii - Measured Misalignment

ElUi

Previous Developments

• Semi-automatic System for Inspection of Fillet

Welds for Automotive Industry - Tube to plate fillet weld - Measures 2 and 3-dimensional defects - Indicates defect type, size and location - Improved upon previous technologies

EUlf

133
Inspection Example:

Fillet Weld Inspection with Laser Line

^P" f^su»» $SWc> IHft>t Mitt

76 ! t«WUh TisW: iaf|

lnch«K LttAwUmit Uj>petLnt TwRatfa.irO.roq, 0.000 5.000 '"^ToeflKSutj;^ 0.000 ' 0.000 5X00 ^^UnddcUl ', OlOOd; 0.000 OJIM

NOe'PlslsAnsta ! ~" oi es 95

F>^Lesljns(hl : " S.OiB; 0150 ttWO UsLanathZ jaooSj 0,150 0400

Conwdly f TdlB; |.0.25o] 0,260

19 EUlf

Two-Dimensional Measurements

• Leg Lengths

• Weld Face Widths

• Weld Toe Angles

• Theoretical Throat

• Convexity/Concavity

• Plate Angle

ELUi

134
Three-Dimensional Measurements

• Surface Porosity

• Surface Cracks

• Undercut

• Spatter

ElUi

Current Developments

• Adaptive Welding

- Bug-based system with adaptive control of

welding parameters using laser-based sensing for:

» Pre-weld joint tracking

» Weld joint template selection

» Post-weld inspection

EUlf

135

COMPUTER MICRO-IMAGE ANALYSIS IN THE

HEAT-AFFECTED ZONE OF HQ130 HIGH STRENGTH STEEL

Li Yajiang* and Wang Juan*

ABSTRACT

The microstructure and grain degree in the heat-affected zone (HAZ) of HQ130 high

strength steel, which was welded by gas shielded metal arc welding, are researched by

means of microscope and computer technique. A series of sample are cut from the heat

affected zone of HQ130 steel to make metallographic analysis. The relative content of different

microstructure in the HAZ is researched by means of XQF-2000 micro-image analyzer. Fine

structures in the HAZ are analyzed by means of Transmission Electron Microscope

(TEM) and electron diffraction. Experimental results indicate that with the increase of

weld heat input (E), cooling time (^g/s) is longer and impact toughness deteriorates. The main

microstructure in the HAZ changes from lath martensite (ML) and low bainite (ML+BL)

to lath martensite, low bainite and upper bainite (ML+BL+Bu), and the grain becomes

coarser, especially, the austenitic grain diameter increases to 1 5 8|i.m when weld heat input

(E) is 22.3kJ/cm. By controlling weld heat input under 16kJ/cm to form ML+BL in the HAZ,

avoiding producing Bu and preventing grain coarse, the microstructure and performance in the

HAZ of HQ130 steel can be ensured.

KEY WORDS

High strength steel. Heat-affected zone, Microstructure and Micro-image

INTRODUCTION

HQ130 steel is newly developed low alloy high strength steel used in welded structures, the

tensile strength of which is 1300MPa. The various parts in the heat affected zone (HAZ) of

HQ130 steel experience different weld thermal cycle. The HAZ is considered as a weak part in

the welded joint of the high strength steel (Ref 1,2). It is very important to ensure the

microstructure and mechanical properties of welding zone, especially for the low alloy high

strength steel. Previously researchers made some qualitative analysis in the heat-affected zone of

high strength steel by means of traditional metallography (Ref.3-5).

*School of Materials Science and Engineering, Shandong University, Jinan 250061 , P.R.China

E-mail: yaj li@jn-publie. sd.cninfo.net

137
Operators observe the microstmcture under the metallograph with slow velocity and high

requirement for the operator's skill and the result with inaccuracy. The use of advanced computer

technology to analyze the microstmcture in the weld zone, will be significantly increase the

accuracy of determining the microstmcture in the HAZ.

In this paper, the toughness in the HAZ of HQ130 has been studied through the weld heat

simulated test. We have measured the relative content of different microstmcture and grain

degree in the HAZ by means of XQF-2000 micro-image analyzer, which will decrease the

artificial influence to analyze the microstmcture. We also make further analysis of fine

microstmcture in the HAZ by means of Transmission Electron Microscope (TEM) and Scanning

Electron Microscope (SEM), which provide experimental and theoretical basis for the

determining welding technology parameters.

EXPERIMENTAL METHODS

Chemical compositions and mechanical properties of HQ130 steel after the heat treatment are

listed in Table 1. The thickness of the base metal is 12 mm in the test. Using gas shielded metal

arc welding (GSMAW) with single V groove at the place of butt joint and with different weld

heat input (E), such as smaller (9.6kJ/cm), middle (16.0 kJ/cm) and larger (22.3kJ/cm).

Table 1 Chemical compositions and mechanical properties of HQ130 high strength steel

Chemical compositions /%

C Si Mn Cr Mo Ni B S P

0.18 0.29 1.21 0.61 0.28 0.03 0.0012 0.006 0.025

Mechanical properties

0 b/MPa 0 s/MPa 5/% W/o/o HRC Akv/J

1370 1313 10 43 40.5 64 (20°C)

The weld thermo-simulation test of HQ130 steel was carried out by means of a Gleeble-1500 test

machine. The test parameters were: peak temperature (7p) 1350°C and cooling time (h/s ) 5s,

10s, 20s, 40s. A series of sample are cut from the heat-affected zone of HQ130 to make

metallographic analysis. The relative content of microstmcture in the HAZ is measured by means

of XQF-2000 micro-image analyzer. The relative content of different microstmcture in the HAZ

is calculated using the computer according to the percent that the microstmcture represents by

each color covering the field of vision.

The grain degree was measured by the cutting-line method. The formula of grain degree index is:

G-3.2877+6.6439 Ig (mX N/L)

Where: L is the gauged lattice length (mm); M is the magnifying multiple; N is the cutting dot

number on the gauged lattice.

138
Thin slice from the HAZ by line-cutting machine was made into film sample. The fine

microstructure was analyzed using TEM and electron diffraction in the chosen zone.

EXPERIMENTAL RESULTS AND ANALYSIS

Impact toughness in the HAZ

Impact work and hardness in the simulated HAZ have close relationship between peak

temperature (Tp) and cooling time (/g/s). When Tp=1350°C, the result of thermo-simulation test

for in:q)act work and hardness in the HAZ of HQ130 steel is listed in Table 2.

Table 2 Impact toughness and hardness in the simulated HAZ of HQl 30 steel (rp=l 350°C)

^8/5 /S 5 10 20 40

Ef k] -cm' 9.2 13.1 18.6 26.4

Akv/J 65.7, 56.1,65.4 55.9,51.7, 53.4 60.3, 55.0, 46.8 23.7, 22.6, 33.2

(62.4) (53.7) (54.0) (26.6)

HV (372) (365) (318) (255)

Note: 1) Relationship between U/s and E was calculated by using the D.Uwer formula (Ref3);

2) Number in the bracket is the test average value.

When 7p=1350°C,which is corresponded to the coarse grain heat-affected zone(CGHAZ) and

^8/5=40s (with considerable weld heat input), impact work decreases obviously. The test results

indicate that tg/s should be controlled smaller than 20s, because the larger the weld heat input is,

the longer tg/s and the easier the deterioration of impact toughness in the HAZ is.

In practice, most welded joints are formed by the multi-layer welding technology. The

heat-affected zone will undergo multiple weld thermal cycles. Impact work in the HAZ of

multi-layer welding is obviously increased, which is favorable to welding of HQl 30 high

strength steel. The test result of impact work in the practical welded joint zone of HQl 30 steel is

shown in Table 3.

Table 3 Impact work in the practical HAZ of HQ130 high strength steel

Positions Coarse grain zone The fiision zone Base metal

Akv/J

80.5,91.8, 84.2

(85.4)

97.1,99.6,111.8

(102.8)

60.5, 63.2, 68.8

(64.2)

Note: Impact work in the fusion zone virtually included a part of weld metal and HAZ,

Microstructure in the HAZ of HO130

The austenite grain in the CGHAZ near the fiision zone for HQl 30 steel grows up obviously and

grows into even-distributed austenite, which will change into coarse low carbon lath martensite

(ML) when cooling rapidly after the welding (see Fig. la). The coherent lattice interface lies

between the ML and the former austenite, which is non-diffusion phase transform. Fig. lb shows

the feature of the lath martensite (ML) in the CGHAZ and the fiision zone of HQ130 observed

under SEM. The main feature is that the low-carbon martensite lath is distributed in bigger angle

139
and Matensite laths are nearly parallel to form a martensite zone. If the former austenite grain is

finer the width of the martensite lath would be smaller and toughness would be better.

a)CGHAZ 1500X b) CGHAZ and the fusion zone 1500X

Figure 1 Feature of microstructure in the HAZ of HQl 30 high strength steel

Metallographic analysis shows that there is low banite (BL) in the coarse grain heat affected zone

(CGHAZ) of HQ130 steel besides a large number of ML. Some of BL are parallel to ML, others

transverse martensite lath and cut apart the austenite grain. When austenite changes into ML

during the cooling, more than ten closed lathes have the same orientation and make up a bond of

lathes. When BL+ML mixture microstructure is produced in the CGHAZ, the first separated BL

would carve the former austenite effectively, which makes more opportunities for ML core and

limits the growth of ML. In contrast to a unitary ML, the mixed microstructure of BL+ML has

more larger angle boundary. Crack developing is hindered on the border of ML bunch or ML-BL

border and changes direction, the length of crack developing in unit becomes shorter, so the

impact toughness increases obviously.

The relative content of microstructure and grain degree

The relative content of various microstructures is not easy to be determined accurately. The

contents of the BL and Bu are very important to toughness in the HAZ. The type of

microstructure and their relative contents in the HAZ with different weld heat input were

analyzed by means of XQF-2000 micro-image analysator. The result of quantitative analysis is

listed in Table 4. Effect of weld heat input on microstructure is showed in Fig.2. The larger weld

heat input is, the more easily the Bu would be produced in the HAZ. When the weld heat input

(E) is controlled under 16.0 kJ/cm, the mixed microstructure of ML+BL may be acquired in the

CGHAZ where there is no Bu. A certain angle exists among BL lath and separates martensite lath,

which leads to the increase of sub-boundary and the decrease of crack sensitivity and make the

toughness in the HAZ increase obviously.

When the weld heat input is 22.3 kJ/cm, the main microstructure in the HAZ is ML+BL+Bu,

among which there is 9.8 percent Bu. The orientation of crystallization among Bu lath is very

small, cleavage crack can run through Bu lath continuously, corresponding to lower cleavage

140
fracture stress, which makes the toughness decrease in the HAZ. The weld heat input in welding

of HQ130 steel should be limited (£"^16 kJ/cm) in order not to produce Bu in the HAZ.

Table 4 Relative content of microstructure in the HAZ of HQ130 steel

Weld heat input

/kJ • cm-'

JVlL/% dU /o DW13., /O//o

Main

microstructure

9.6

92.9, 93.0, 93.6

(93.2)

4.6,5.0,5.1

(4.9)

0 ML

16.0

85.1,83.8,83.6

(84.2)

10.2, 11.4, 12.7

(11.4)

0 ML+BL

22.3

60.2, 68.8, 68.5

(65.8)

22.2, 18.3, 19.2

(19.9)

9.6, 10.1,9.9

(9.8)

ML+BL+Bu

Note: Number in the bracket is the average value of three version field

60 •

50 « ' '

9.6 16 22.3

BU/cnr

Figure 2 Effect of weld heat input (E) on the relative content of microstructure in the HAZ

Affected by the peak temperature, the austenite grains become coarse in the CGHAZ of HQ130

steel. The width of the CGHAZ and the diameter of austenite grain are different using different

weld heat input. Under the condition of different weld heat input, the width of the CGHAZ and

austenite grain degree is evaluated using XQF-2000 micro-image analyzer, and the grain

diameters are calculated by means of the computer. The analysis results are shown in Table 5.

Table 5 the width, the grain degree and diameter in the CGHAZ of HQl 30 steel

Weld heat input

/kJ.cm'

Width of CGHAZ

/mm

Grain degree

/grade

Maximum grain

diameter /|4,m

9.6

1.03,1.10, 0.98

(1.00)

3.0, 3.5, 4.2

(3.6)

105

16.0

1.28,1.16,1.29

(1.24)

2.9, 3.2, 3.7

(3.3)

115

22.3

1.58, 1.56, 1.52

(1.55)

2.4, 2.0, 2.5

(2.3)

158

Note: Number in the bracket is the average value of three version field

141
From the measured data we can see that the larger the weld heat input is, the broader CGHAZ is

and the larger the size of austenite grains are. Especially when £"=22.3 kJ/cm, the maximal

diameter of austenite grain in the CGHAZ reaches 158 |im. So in order to prevent the grain from

being coarse in the CGHAZ, the weld heat input must be limited, which ensure the impact

toughness and avoid producing brittleness and crack.

Fine microstructures in the CGHAZ

Fine microstructures in the HAZ of HQ130 steel have great effect on the impact toughness. Thin

slice was cut out from the HAZ by the liner-cutting machine, then abraded into thin slice sample

and finally made into film sample slighted by electrolysis double-spurt method. Using the TEM

and electron diffraction to conduct analysis for the film sample. The feature of ML and BL under

TEM in the HAZ of HQ130 steel taken from [110] direction, the electron diffraction pattern and

schematic index diagram are showed in Fig.3a,b,c.

a) TEM morphology 40000 X b) Electron diffraction pattern c) Schematic index diagram

Figure 3 Feature of the fine structure in the CGHAZ of HQ 130 steel (TEM)

The sub-structure in the HAZ of HQ130 steel observed under TEM is martensite lath with

dislocation, whose dislocation density about (0.3-0.9) X lO'Vcm^. Some carbide distributes

inside the ML lath and on the boundary. There is a part of lattice distortion region near

dislocation where the diffraction strength is different from other parts. So we can observe the

lining shadow of the dislocation, y -*ML forms dislocation sub-structure in microcosmic and

exists crystallographic orientation relationship of (lll)y //(Oll)aand [101] y //[1 11] a between

origin and new phase.

When the weld heat input is 9.6 kJ/cm, self-tempering phenomenon appears in the HAZ even

cooling rapidly. Under TEM, carbide dispersed inside the grain can be clearly observed. A little

of twin martensite can be also observed besides dislocation ML because of cooling rapidly,

which is testified by electronic diffraction analysis. The twin martensite in the ML lath of low

142
carbon quenched and tempered steel is distinct from that of high carbon alloy steel. Only in the

local parts of covering ML appears the twin, others are high density dislocation line, while the

twin in high carbon martensite is distributed with concentrated arrangement (Ref.4)

The toughness of low carbon lath mantensite depends on the sub-structure of high-density

dislocation inside ML lath. The TEM analysis shows that there are a lot of dislocations in the

sub-structure of lath martensite in the HAZ of HQ130 steel. This perhaps because of

"self-tempering" phenomenon that supersaturated carbon in the new-produced martensite in the

HAZ can still spread, assemble and separate out in form of the carbide particle. Self-tempered

lath martensite has certain toughness itself. If the self-tempered ML is produced in the HAZ of

high strength steel, the performance to resist cleavage fracture can be increased and the impact

toughness in the HAZ can be ensured.

CONCLUSION

When weld heat input (E) changes from 9.6 kJ/cm to 22.3 kJ/cm, the microstructure in the HAZ

of HQ130 steel changes from ML to ML+BL and then to BL+ML+Bu, the width of HAZ

increases, and size of austentite grain becomes larger. Even when E=22.3 kJ/cm, the content of

upper bainite (Bu) arrived to 9.8% and the maximum diameter of relevant grain in the CGHAZ

of HQ 130 steel is 158|im. In order to prevent the toughness deterioration in the HAZ because of

the formation of Bu, the weld heat input should be limited under 16 kJ/cm. The TEM observation

and analysis of electron diffraction indicate that there are high density dislocation about (0.3-0.9)

X 10 /cm in sub-structure inside low carbon lath martensite in the HAZ of HQ130 steel. The

closed lathes inside ML lath bundle have small orientation difference and have no obvious twin

structure.

ACKNOWLEGEMENT

The work was supported by the foundation (05170104) of Key Laboratory of Liquid Structure

and Heredity of Materials, Ministry of Education, People's Republic ofChina.

REFERENCES

L Tian D.W, Qian B.N, Si Z.Y and Yang H.X. 1995. Effect of Boron on microstructure and

toughness of simulated CGHAZ of HSLA steel. China Welding. 4(1): 37-s to 43-s.

2. Yang YX. 1992. Behavior of transformation induced plasticity in simulated CGHAZ for HT-80

steel. Proceedings of the International Conference. Beijing, (2): 71 -s to 74-s

3. Zou Z.D, Li Y.J and Yin S.K. 1998. Toughness and TEM analysis for the CGHAZ of HQ130

steel. Transactions of the China Welding Institution . 19(3): 141-s tol46-s.

4. --Tan YX. 1985. TEM analysis for low carbon martensite steel. Acta Metallurgic Sinica . 21(3):

181-s tol86-s.

5. "Li Yajiang, Zou Zengda et al. Effect of weld thermo-cycle on microstructure and performance

in the heat-affected zone of HQ130 steel, Acta Metallurgic Sinica. 32(5): 532-537

143

Session A5: Fabrication Quality and Standards

145

VIRTUAL AND AUGMENTED REALITY FOR QUALITY IMPROVEMENT OF

MANUAL WELDS

P. Tschirner*, S. Nordbnich*'*, A. Graser*

ABSTRACT

The large problem to create manual welds of constant high quality results from missing optical information during the actual welding process. Due to the extreme brightness conditions

e.g. in arc welding and the use of protective glasses even experienced welders can hardly rec- ognize details of the welding pool, the welding seam and the environment. This paper describes a new research project for the development of a system for the support of the welder.

KEYWORDS

Mrtual and augmented reality, manual arc welding, quality control

INTRODUCTION

Welding is one of the most important industrial manufacturing methods. The creation of

manual welds is often used in production of unique pieces where an automation is impossible

or uneconomic, e.g. in shipbuilding with its complicated hull geometry. A common welding

method is the inert-gas arc welding e.g. manual gas tungsten-arc welding (GTAW) or manual

gas metal-arc welding (GMAW). In all these processes the electrical arc transfers energy to

the welding seam. This arc has an extraordinary high brightness and ultraviolet radiation and

for observation a welding helmet with suitable protective glasses is necessary. These glasses

absorb the radiation and darken the entire scene, so even experienced welders can hardly rec- ognize details of the welding pool, the welding seam and the environment. Figure 1 shows a view of the welder recorded with a CCD camera through standard protective glasses.

Figure 1: Simulated view of the welder recorded with a CCD camera and standard protective

glasses.

' University Bremen, Institute ofAutomation (lAT), Kufsteiner Str. NWl, 28359 Bremen, Germany

* FWBI Friedrich-Wilhelm-Bessel-Institut Forschungsgesellschaft m.b.H, Postfach 106364, 28063 Bremen,

Germany

147
A further disadvantage from protective glasses is the restricted field of view of the welder.

Furthermore the welder has no additional information, e.g. the actual parameters of the welding power supply. This is particularly of importance if the welding had to be done in far distance from the welding power supply.

During the welding process the requirements at the concentration and reliability of the welder

are very high because of the insufficient optical information. Due to these missing optical information the creation of manual welds with constant high quality becomes very difficult.

To increase the manufacturing quality and economic efficiency a support system for the

welder is required. This can be achieved by improving the visual information for the welder

as well as by supplying additional information with methods of virtual and augmented reality.

The described system is based on the approach for the optimization of PGMAW using visual

online observation of the droplet transfer in combination with recording of electrical welding

parameters described by Nordbruch, Tschimer and Graser e.g. in [1], [2].

VIRTUAL AND AUGMENTED REALITY

Virtual reality is a new technology, which uses a computer model to generate and present an

artificial environment to a person, evoking the impression of actually moving in this environment.

Augmented reality is a new form of human-machine interface, which inserts information via

head-mounted displays in the users field of view. The insertion is context dependent, i.e. compatible to and derived from the observed object, e.g. the real field of view of a mechanic

is extended by the insertion of instruction sheets. Apart from this the use of wearable computers opens new augmented reality application fields, in which high mobility as well as actual process values, measured or simulated data are required.

DESCRIPTION OF THE SYSTEM

The system combines a conventional welding helmet with modem technology. The system

consists of

• a welding helmet combined with

- two High-Dynamic-Range-CMOS-cameras (HDRC) for observation of the welding

scene

- a head-mounted display for visualization of the welding scene

• a wearable computer for image recording and image processing

• a computer for measuring electrical welding parameters and automatic calculation of characteristic welding process parameters

The basic set-up of the system is shown in figure 2,

148
welding supply

Figure 2: Principle sketch of the system, depicted without the welding protective mask

HDRC

Figure 3: System

A welding helmet is combined with two HDRC cameras for the observation of the welding

scene. A portable computer enhances the image quality and displays the images on the headmounted display, which is also integrated in the welding protective mask.

By methods of virtual and augmented reality, additional information can be inserted depending on the application. This information can be derived from the welding power supply (e.g.

electrical welding parameters, wire feed), directly extracted from the images (dimensions of

the arc) or transferred by another computer (constructional details, like work piece geometry).

High-Dvnamic-Range-CMOS Camera (HDRC)

The principle of HDRC-cameras can be described as follows. The intensity levels in an image

are essentially dependent on the irradiance and reflection properties of an object. The information of an image only depends on the contrasts resulting from varying reflections, and

fewer on the absolute intensity values caused by irradiancy because the reflection property of

a surface is independent of its irradiancy.

149
A CCD-camera maps the absolute intensity values caused by the irradiance of an object.

These light intensities can indicate a relation of up to 10^:1, i.e. in high-energy processes as welding. Due to the technology the dynamic range of a CCD camera is approximately

4000:1, which is not sufficient in order to represent the high light intensities of the welding

process.

The image sensor of the HDRC camera consists of CMOS transistors. Before further processing, the input signal is compressed logarithmically in every cell of the image sensor. Due to

diis compression the HDRC camera maps the contrast caused by different reflections between

two side-by-side object points and the information content of the image is not reduced. Thus

the dynamic range of a HDRC camera is circa 10^:1. This corresponds approximately to the

light intensities of the welding process.

a.

Figure 4: Welding scene recorded with a. CCD camera b. HDRC camera

Signal Processing

The signal-processing unit allows the measurement of specific parameters of the welding

supply (e.g. welding current, welding voltage and wire feed) and the automatic calculation of

typical characteristic process parameters from of the measured current and voltage signals

[1], [2].

Image processing

The image processing unit allows the recording and further processing of the images. Additionally relevant welding parameters, e.g. geometry of the arc, can be automatically extracted

from the image data

Due to the extreme brightness conditions of the welding process and the characteristics of the

camera the images are of low contrast. In order to give the welder a detailed view both on his

envirormient and the welding process (e.g. melting of the electrode and the welding pool) a

further processing of the images is necessary. The contrast has to be adapted to the different

image areas, so that the resulting image contains all image areas with sufficient contrast.

Standard image processing techniques cannot cope with the extreme brightness conditions

and the characteristics of the camera. Objects in far distance the arc may differ only by few

gray levels from the background. In these image areas a contrast enhancement is necessary.

Objects near the are clearly visible against the background. In these areas a contrast en- hancement can lead to a degradation of the image quality (e.g. by exceeding the range of the

gray level images). Therefore the image processing procedure needs to be robust against

varying image contents (e.g. brightaess). An adaptive procedure has been developed [4], that

150
"recognizes" the high-contrast areas which have to be maintained and the low-contrast areas which have to be improved. For contrast enhancement the procedure Contrast Enhancement

Using the Laplacian-of-a-Gaussian Filter from Neycenssac [3] was adapted to the problem.

In this procedure the enhanced image g(x,y) is obtained by the convolution of the original image f(x,y) with a LoG filter LoG(x,y), multiplication of the resulting image g'(x,y) by a contrast factor and subtraction from the original imagef(x,y):

g(x,y) = f(x,y)-pf(x,y)*LoG(x.y) (1)

For the implementation of an adaptive contrast enhancement the procedure was modified by

adapting the contrast factor ^ to the image content. To generate a priority function for ^ a

gradient operator is applied to the original image in order to determine the contrast of the image areas. The resulting gradient image fsob(x,y) is low-pass filtered to create a continuous

transition between the areas. Within high-contrast areas, the low-pass-filtered gradient image

fsob,Tp(x,y) contains high values, and P must be small in order to maintain the contrast. Within

low-contrast areas, the low-pass-filtered gradient imagefsob,Tp(x,y) contains low values, and P

must be high in order to enhance the contrast. The following relationship for ft was experimentally determined:

p = 200-40ln[fsob,Tp(x,y)] (2)

Figure 5 shows the concept of the procedure. An example for a result of image processing

with uniform contrast in all image areas is shown in figure 6.

fl:x.y)

LoGFilter g'(x,y)=f(x,y)*LoG(x,y)

Sobel

operator

feob(x,y)

Lowpass Calculation of the

filter contrast factor fsob,TKx,y)

^

g(x,y)=f(x,y)-p.g'(x,y) © —

pg'(x,y)

p=200-40-ln[fe<,h.TKx,y)]

Figure 5: Flow of the adaptive contrast enhancement procedure

Figure 6: a. Original image; b. Result of adaptive contrast enhancement

151
POSSIBLE APPLICATIONS

In the following the possible areas of application of the system are presented. They represent

the current status of the requirement specification for this research project and were determined in cooperation with experienced welders.

Welding preparation

The first application possibility is the use during welding preparation. Constructional details can be displayed, e.g. the material type, desired values of the welding power supply or the

desired welding seam.

Protective Ons: 82% Ar + I<S% CO"

Material: S 235 JRTxZ,

Sheet Metal Thickness s = 2,0 mm.

Current 1(1) = 570 A,

\ oltajje li(t) = 34 V

Figure 7: Possible insertions: welding seam and welding parameters

Welding Process

During the welding process an online quality control is possible utilizing the parameters extracted from the image data, e.g. the measurement of the welding seam geometry. This also

allows an online adaptation of the welding parameters when welding long seams with

changes in the work piece geometry.

Figure 8: Possible insertions: work piece geometry, welding seam, splashes, wire feed,

height of the arc, welding current and welding voltage

Oualitv control

The system can also be used for quality control. At present this is done via radiometric ex- amination after the welding process, which is very complex and expensive. Additionally this

method does not allow any conclusions on occurrences during the welding process.

152
With the new system during safety-relevant welds the images can be stored together with

relevant welding parameters, which can be easily assigned to each individual picture [1], [2].

The example shows splashes caused by a to high welding current.

Figure 9: a. Image of the welding process; b. Synchronized and simultaneously measured

electrical welding parameters current and voltage including the time of the image

recording

A further possible application is the use in education of welders. By using of a second headmounted display, teacher and trainee can observe the same welding process. The influence of

changing welding parameters on the weld quality can be directly examined.

The system described in this paper combines a conventional welding helmet with a headmounted display and two HDRC cameras. The welding scene is observed with the cameras. A

portable computer provides further enhancement of the image quality and displays the images

in real time in the head-mounted display. The welder has a detailed view on the welding pool,

welding seam and his environment. By methods of virtual and augmented reality additional

information (e.g. welding parameters or constructional details) can be inserted depending

upon the application. The system can be used for getting information about the welding task

before the beginning of the welding process, for the observation of the welding process including online quality control, for quality control after the welding process and for education.

By the visual observation of the welding process in combination with the representation of

relevant welding parameters the welder receives a completely new status of information and

is able to do his work much more precisely. Thus the simplification of manual welding and an

improvement of the welding result are achieved, leading to a better and more constant quality

and therefore to a reduction of the inspection expenditure in quality control

a.

Education

SUMMARY

CONCLUSION

153
ACKNOWLEDGEMENT

The presented research project is supported by the German Federal Ministry for Education

and Research (bmb+f) under 01IRA07A. This research project is executed in cooperation

with

• research institutes: Institute of Automation, University of Bremen, Germany; Institute of

Industrial Engineering and Ergonomics, RWTH Aachen, Germany

• a shipbuilder: Liirssen Yachts - Super Yachts, Sail and Motor Boats, Bremen, Germany

• a manufacturer of welding supplies and devices: EWM HIGHTEC Welding GmbH,

Mundersbach, Germany

• a manufacturer of autodarkening welding masks: Optrel AQ Wattwil, Switzerland

REFERENCES

1. Nordbruch, S.; Tschimer, P.; Graser, A. 2000. Visual Online Monitoring of PGMAW

without a Lighting Unit. International Sheet Metal Welding Conference IX. 18.-20.

October 2000, Detroit, Michigan, USA. Paper Nr. 4-4

2. Nordbruch, S.; Tschimer, P.; Graser, A. 2000. Visual Online Optimization of Pulse

Gas Metal Arc Welding with a HDRC-Camera without a Lighting Unit. UKACC In- ternational Conference Control 2000. 4.-7. September 2000, University of Cambridge,

UK. Paper Nr. 190

3. Neycenssac, F.; 1993. Contrast Enhancement Using the Laplacian-of-a-GaussianFilter. Computer Vision. Graphics, and Image Processing. No. 55, pp. 447-463

4. Tschimer, P.. 1999. Visuelle Analyse von HDRC-Kamerabildem des Werkstoffiibergangs des MSG-SchweiJ3prozesses. Diploma Tliesis, Institute of Automation, University of Bremen, Germany

154
A9 Mission, Current Activities

and

What Should We Do Next?

Bill Rippey, A9 Chairman

• A9 Charter

• AWS Vision

• A9 Status

• Some examples from other industries - Shipbuilding - Electronics Manufacturing - Machining - Discrete parts inspection

• Discussion - where does A9 go from here?

155
i

• A9 currently has a small number of projects

in the committee.

• Other manufacturing industries have

extensive efforts underway in digital

information specifications - should we

follow their examples?

• A9 wants your input on needs, and your

participation if something matters to you!

• Issues involving information manipulated

by computers

• Original projects related to database

software used for WPSs, PQRs, WQRs.

• Scope could include CAD product and

weldment design, CAM process planning

and programming, automated weld cell

control, inspection data, equipment control

integration.

156
• "Major Challenges" - "Welding will be better integrated into the

production cycle"

• Vision (for 2010) - "drivers" will include ". . . . use of information

technology .... in which technologies for

design, fabrication, and inspection are

seamlessly integrated with welding technology

where they are needed."

• "Strategic Goals - Cost/productivity/markets/applications reduce

the average cost of welding by one-third, by

providing better process selection guidance,

increasing the use of automation and robots,

and lowering reject and repair rates; increase

the use of welding by 25%."

157
• "Process technology - Enhance the use of welding in manufacturing

and construction operations by integrating

welding with other manufacturing and

construction disciplines, at the engineering

level and also at the operational level."

• "Developments in Information Technology

- Welding processes based on rigorous

engineering analysis, numerical modeling, and

computer-based automated manufacturing will

be widely used, and will depend heavily on

information technology."

8

158
• "

Integrate welding within entire factory upstream

and downstream"

• "Move to an ^ open architecture' and automation in

welding technology. Make 80-90% of equipment

compatible (plug and play) by developing an industry

standard"

• "Among the techniques foreseen are integration of

design, sensing, and process control, and development

of modular processing equipment to ensure

compatibility of various systems."

• A9A Subcommittee hosts this conference

• A9. 1 - Data Dictionary

• A9.3 - Activity Model

• A9.4 - Network Communications for

IntraCell Components

• Past submissions of CAD<->OLP,

OLP<->Robot, Interface to Weld Procedure

Database.

10

159
Other industries have begun efforts in

integration of computer information

Is the welding industry part of some of

these industries (e.g. shipbuilding)?

Question: is the welding industry enough

like these that we should make similar

efforts?

II

• STEP AP 215 - Ship Arrangement

• STEP AP 216 - Ship Moulded Forms

• STEP AP 2 1 8 - Structural Systems Data for ship

predesign, design, production, and

inspection/survey

• STEP AP 226 - Ship Mechanical Systems

• STEP AP 212 - Electrical Design and Installation

• STEP AP 227 - Piping

• Currently no STEP AP for weldins.

12

160
• IPC - Association Connecting Electronics

Industries

• NEMI - National Electronics

Manufacturing Initiative

• SEMI - Semiconductor Equipment and

Materials International

• CAMX standards ~ Computer Aided Manufacturing ming the

extensible Mark-up Language (XML). - provides for factory floor communication. - standardizes the language used by assembly placement and test equipment. - reduce the amount of time and cost that it takes to integrate a new

piece of electronics assembly equipment into a shop floor

enyironment and start collecting data and controlling that

equipment. - improved interoperability, ie. "plug and play" capability between

equipment also allows manufact\irers to use the best piece of

equipment for the job, regardless of vendor.

14

161
• IPC-25 10 Product Data (GenCAM)

• iPC-2520 Product Data Quality

- The lPC-2524 - facilitates grading the quality of product data packages

delivered to a printed wiring board fabricator. It defines a checklist of

information required in a complete data package.

• lPC-2530 Process Data (Standard Recipe File Format) - The standard recipe file format (SRFF) defined in the IPC-253 1 was

created to standardize the instruction file format for controlling assembly

equipment.

• iPC-2540 Shop Floor Communication

- vendor-independent plug and play environment for electronics assembly,

inspection, and test equipment. - quickly integrate new pieces of electronics assembly equipment into a shop floor line management system and how to manage the vast amounts

of data available in today's electronics manufacturing environment.

• IPC-2550 Execution System Communication - communication protocols and information requirements for

Manufacturing Execution Systems (MES) within a factory.

• IPC-2560 Enterprise Communication - enterprise resource planning system standards.

• IPC-2570 Supply Chain Communication - supply chain

messages that are passed between trading partners,

including requests for quotes, engineering change orders,

and product genealogy reports.

16

162
• VFIIP builds on the work of NEMI's Plug & Play Factory

Project, which was completed in December 1999.

• While Plug & Play focused on interoperability on the

factory floor. Virtual Factory - extends interoperability from within an enterprise to

information systems distributed across business

partners. - is addressing the bidirectional exchange of information

between OEMs and EMS proyiders for board and final

assembly, centering on the exchange of technical

engineering and manufacturing data.

• SEMI E4-0699 - SEMI Equipment

Communications Standard 1 Message Transfer

(SECS-I)

• SEMI E5-0701 - SEMI Equipment

Communications Standard 2 Message Content

(SECS-II)

• SEMI E54-0997 - Sensor/Actuator Network

Standard

18

163
19

• Metrology Interoperability Consortium - scope includes computerized data

describing part design, inspection plans and

programs, and data gathering and analysis.

• DMIS - machine-independent language for

programming inspection (e.g. by CMM)

20

164
CAD Inspection Frogramming

Softtvare.

•AutoCAD

•Catia •I-DEAS

•Pro/E

•Solid Edge

•etc.

Design

•Camio

•Metrolog •PC DMIS •Quindos •Umess

•Valisys

programming

•etc.

Inspection

Pianmng Data

Inspection Program

Meaa. Data

Low-level Inspection

Instructioh Comtnan<i.s

165
• Other industries have begun efforts in

integration of computer information

• Is the welding industry part of some of

these industries (e.g. shipbuilding)?

• Is the welding industry enough like these

that we should make similar efforts? Is

there a need?

23

• What are the needs of - users - vendors - integrators?

• Should A9 Do More? Do Less?

• Who Needs What?

• Who will contribute?

24

r« # 4

166
• Chairman, Bill Rippey

-NIST, 301-975-3417 - williani.rippey@nist.gov

• AWS Secretary, Len Connor

- 800-443-9353 x302 - lcoimor@aws.org

• Next A9 meeting, January/February 2002,

Florida

167
• Capture/record/exchange inspection results,

manual and automated.

• Shop floor communications for better &

easier integration?

• Weldment design data?

• Enterprise level data flow?

. 99? • • •

168
• Example - Chemical Abstract Society has

more than 4 million single- and multi-step

reactions, more than 205,000 records from

journal articles and patents with reaction

information.

- Is there an analogy between "reactions" and,

say welding WPSs, PQRs?

169

Session A6: Sensing and Control IV

171

ON-LINE WELD QUALITY DIAGNOSTICS IN FRICTION STIR WELDING

* D. Jandric, J.H Ouyang, M. Valant and R. Kovacevic

ABSTRACT

The temperature distribution on the plate surface in front of the shoulder and around the pin very

close to the workpiece surface is analyzed in this paper. Consideration is given to the influence

of the welding parameters, the traverse speed and the rotational speed on the temperature

distribution and their effect on weld quality. Infrared sensing is applied in order to collect

relevant data for this analysis.

KEYWORDS

Infrared Sensing, Surface Temperature Distribution, Welding Parameters and Weld Quality

INTRODUCTION

Friction Stir Welding (FSW), invented and patented by The Welding Institute, Cambridge,

England, in 1991, has recently captured the attention of the welding community for fabricating

high-quality joints of aluminum alloys. This is an energy efficient, environment friendly and

versatile process. Friction Stir Welding is a very attractive technology because of its simplicity

and applicability to a wide range of alloys. It offers a variety of new design and production

welding opportunities and should be of particular interest to the shipbuilding, aerospace, and

automotive industries.

The basic principle of FSW is relatively simple. A rotating pin is inserted in the material to be

joined and traversed through the joint line while the shoulder is in contact with the top surface of

the workpiece. Rotary motion generates heat,

which creates a softened and plasticized region

around the pin and contacting surface. A weld

joint is produced by extrusion of the plasticized

material from the leading side to the trailing side

of the tool, Fig.l. (Ref. 1). Welds with higher

quality and finer microstructure than the parent

material can be achieved by FSW.

Recently, several studies of the relationship

Figure!. Friction Stir Welding Principle between the welding parameters and the

temperature distribution in the friction stir

welded joints have been reported. The

Research Center for Advanced Manufacturing

Southern Methodist university, 1500 International Pkwy. Suite # 100, Richardson, TX 75081

'Corresponding author -tel.: 214-768-4865. Email: kovacevi@engr.smu.edu

173
temperature around the pin was measured by thermocouples embedded into the workpiece in

order to get the temperature history during the welding process. Simultaneous sensing is

performed by an infrared camera and by force/torque sensors. Sensing the surface temperature in front of the tool using an infrared camera will provide

conditions for controlling the welding parameters during the friction stir welding process. Very

little has been done in FSW for process monitoring with an infrared camera, but based on

research results from other welding techniques, infrared sensing can be successfully applied to

FSW. This process monitoring technique is used in the analysis of the surface temperature

distribution during the laser beam welding process, (Ref. 2). In Gas Tungsten Arc Welding,

infrared process monitoring represents the basis for weld quality control during the welding.

Variations in the welding process parameters such as bead width, penetration depth and torch

position produce an infrared radiation response, (Ref. 3). In general, the infrared thermal

imaging system is a valuable tool that may be used to pinpoint problem areas and to avoid costly

delays.

Monitoring the pin tool depth during the FSW process is one of the most difficult tasks, but it is

possible using the force/torque sensors, (Ref. 4). The relationship between the axial load and the

pin tool depth during the welding was observed to be constant for all welds made with the same

tool pin geometry and the pin tool depth. By controlling the axial load, the pin tool depth is also

controlled. NASA has introduced (Ref. 5) a new tool known as Retractable Pin-Tool. This tool

will assist in developing the closed-looped force control system capable for performing defect

free welding.

The microstructure of the joint made by the FSW technique has been the subject of several

investigations (Ref. 6). The extrusion and forging operation, coupled with the tool rotation, are

collectively responsible for the characteristic microstructure of friction stir welded joints.

Microstructural analyses gave information about the weld quality and also provided proof that

FSW is a solid state process.

The present paper focuses on the effects of the welding parameters, rotational speed and traverse

speed on weld quality. It is shown that infrared sensing is feasible technique for nondestructive

monitoring of the FSW process.

OUTLINE OF STUDY AND METHODS

Experimental set-up

The experimental set-up consists of a

powerful vertical-milling machine equipped

with in-house-designed and built tooling

and fixturing. The tool consists of a

shoulder and a pin with the ratio of their

diameters of 3:1. The workpiece is

clamped onto a backing bar and secured

against vertical, longitudinal and lateral

movements. In order to measure peak

temperatures during the welding,

thermocouples placed at different distances

from the pin center are used. An infrared

camera mounted on an extension arm Figure 2. Experimental Set-Up

174
records the infrared radiation in front of the shoulder on the plate surface, Fig.2. Additional

software is developed for storing and processing recorded data.

Experimental procedure

A number of FSW experiments are carried out to obtain the optimum parameters by adjusting the

rotational and traverse speeds. Experiments are performed on 6061-T6 aluminum plates,

280x200x12.7 mm in size. Temperatures during the Friction Stir Welding process were

measured by K type NiAl/NiCr thermocouples placed in a series of small holes equidistant from

the specimen's surface. It was found that the existence of these holes does not significantly

affect the temperature field during the welding process, (Ref. 7). Peak temperatures were

measured for five different rotational tool speeds (151 rpm, 344 rpm, 416 rpm, 637 rpm and 914

rpm) and five different welding speeds (linear workpiece speeds, 95 mm/min, 140 mm/min, 190

mm/min, 229 mm/min and 330 mm/min). The temperatures were recorded digitally using the

embedded thermocouples and the data acquisition system.

In the effort to understand the process parameter/joint quality relationship, surface temperatures

near the edge of shoulder, at the centerline of the nugget zone, are recorded by an infrared

camera. The recorded infrared data is processed to obtain the temperature distribution on the top

side of the workpiece in front of the tool shoulder. A frame grabber captures images, and image

processing is performed using LabView and Matlab programming. The stored images are

digitized into 640x480 matrices which are composed of gray level values from the infrared

image.

The cross-sections of the weld beads are etched by a modified Keller's etchant. Microstructural

analyses are performed using a high-resolution microscope.

Results and discussion

As a solid state process, FSW operates below the

melting temperature, which is 585^C for Al 6061-

T6, (Ref 8). Typical welding temperature histories

for different distances from the pin center (8 mm,

16 mm, 25 mm) and at the same depth from the top

surface (0.5 mm) measured by thermocouples are shown in Fig. 3. It is shown that the welding

temperature at 0.5 mm from the top surface is about

570 ^C, which is still below the melting

temperature. Microstructural analysis shows that

there is no melting in the body of the weld during

the FSW process, Fig. 4. (Ref 9). The weld is

characterized by a fine-grained, hot-worked

condition with no entrapped oxides or porosity.

The temperature distribution in front of the

shoulder on the plate surface is obtained by

recording with the infrared camera and correlating

gray levels from isothermal images with the

temperatures measured by the thermocouples.

Matching the time recorded by the infrared camera

175

200 240 2S0 Time in second Figure 3. Temperature history under the

shoulder for the weld made at 637 rpm,

95 mm/min

Figure 4. Typical microstructure of

the weld joint
with the time when thermocouples registered the temperature peak, the thermocouples' positions

are found on the isothermal images. A typical result is shown in Fig.5. The measured

temperatures are not the real surface temperatures but they can be treated as such because the

thermocouples are placed very close to the plate surface.

The temperature of 410 °C, which is about 75% of the peak temperature, was measured 25mm

from the center of the pin. This corresponds to a gray level of 220. The surface temperature in

front of the shoulder, being 70-80% of the peak temperature, as measured by thermocouples very

close to the nugget, could be a good indicator of the process, measured by an infrared camera

placed in front of the tool. Optimization of the welding parameters relative to the gray level

intensity in the thermal images gives a basis for on-line control of the FSW process.

Figure 5. Temperature distribution in front of the tool shoulder

Influence of welding speed on temperature distribution

Welding speed, one of the most important parameters in the FSW process, affects temperature

distribution on the plate surface in front of the tool shoulder. The temperature as a function of

the distance from the shoulder is shown in Fig. 6. This temperature is obtained by processing the

original images recorded by an infrared camera. It follows that the temperature in front of the

shoulder will decrease if the welding speed increases for a constant value of heat input. In a real

welding situation this eventually leads to pin fracture and the formation of weld defects as the

material becomes less formable, (Ref 10). On the other

hand, the temperature on the shoulder edge is measured

by thermocouples as well. It can be shown that for

welding speed of 95 mm/min temperature around the

shoulder is 405 and for welding speed of 190 mm/min

that temperature is 382 ^C. When comparing the

temperature results from thermocouples with results

obtained from thermal images on the shoulder edge and

by extrapolating the curve, see Fig. 6, small difference in

temperature on the shoulder edge can be expected

Figure 6. Temperature in fiinction of because of the 0.5 mm depth from the top surface where

distance from the shoulder ^he thermocouples are placed.

Influence of rotational speed on the temperature distribution

176
576-1

574-

oo 572-

.a 570 -

1

568-

tnperat 566-

564 - H 562-

560 -

558 -

200 400 600 800 1000

Rotational speed

Figure 7. Temperature in function of rotational

tool speed under the shoulder

In the welding process, heat may come from

friction between the workpiece and the tool as well as from the plastic deformation of

the workpiece. The rotational speed of the

pin tool may change each of these. The

influence of the pin tool rotational speed on

the temperature field under the shoulder is

shown on the Fig. 7. It is clear that the

rotational speed has a modest effect on the

peak temperature measured by the

thermocouples, (Ref. 7). The temperature is

increased only 15 when the rotational

speed increase from 314 rpm to 914 rpm.

With the higher rotational speed the

temperature should increase, but increased

temperature reduces both metal flow stress and input torque, which limits the power generation

increase. Because the temperature change is very small with a relatively large change in

rotational speeds, in this paper the rotational speeds' influence on surface temperature in front of

the shoulder is not analyzed.

Determination of cooling rate

As mentioned before, by processing the original images, the temperature distributions in front of

the shoulder can be obtained in the form of contour plots. Using a LabView program it can be

shown that, close to the shoulder, temperature distribution is nearly linear with the distance from

the shoulder, so in this paper that assumption will be used. Fig. 8. The cooling rate can be

calculated using contour plots and known welding parameters. The distance between two

isotherms (s), shown in Fig.9, and welding speeds (v) are given, so the traveling time between

two isotherms can be calculated as t = s/v.

250

200

I

150

J 100

50

Welding speed of

1 90 mm/min

6 8 10 Distance from the shoulder In mm

Figure 8. Temperature distribution with

respect to the measuring point from the tool

shoulder

Figure 9. Contour plot for v=190mm/min

177
By correlating the length in pixels with the length in centimeters using the edge of the end of the

plate as a reference, the measurement relationship is found to be 10 mm =19 pixels (px). If v =

190 mm/min and s = 22 px = 11.5 mm, t = 3.65 sec. The ten^erature difference can be

expressed in pixels, Fig .9, so that the distance between isotherms 1 and 2 is 38 pixels for the

traveling time of 3.65 sec. that the tool takes to pass between isotherms.

Using the assumption that, near the shoulder, temperature linearly decreases as the distance from

the shoulder increases, the cooling rate is obtained. For the welding speed of 190 mm/min the

cooling rate is 20 °C/sec, but only in the narrow region around the shoulder. Although the

temperature in front of the shoulder decreases when the welding speeds increases, it can be

shown, using the same procedure that the cooling rate rises when the welding speed increases

due to the lower average temperature of the surrounding material.

Influence of the weld pitch on the temperature field and weld quality

In real welding situations the surface temperature at the centerline of the nugget in the welding

direction, is the most critical (Ref 10). The temperature in front of the shoulder is shown in

Fig. 10 as a function of the weld pitch (the spindle rotational speed divided by the welding

speed). In general, as the weld pitch increases, the maximum temperature in the weld increases

as well. The temperature may saturate at some value of the weld pitch. It is important to realize

that as the weld pitch is increased by reducing the welding speed, the time that the material

spends at or near the maximum temperature will increase: the slower the weld, the longer the

time the weld spends at or above some particular temperature (Ref 11). As the weld pitch

increases from 0.6 to 3.0 rev/mm, the surface temperature increases rapidly. This heat generation

reduces the quantity of defects and improves the joint quality of the weld. As the weld pitch

continues to increase to 9-11 rev/mm, the surface temperature increase slowly and tends to

saturate at some value of the weld pitch. A surface temperature in excess of 120 grayness level

is required for the pin to accomplish the required forging, see Fig. 1 1

A - 5 px from the shoulder B - 1 6 px = 8 mm from the shoulder C - 32 px = 16 mm from the shoialder

10 12 14 ia It

«»•

«

Delad-freeweMa VMAalticWacIa

Toolbn)li«i

4 6 8 10 12

Roblioray •pMd/WUdlng apMd, RW

16 18

Figure 1 0. Maximum temperature in front of Figure 1 1 . Surface temperature with the weld

the shoulder as function of weld pitch quality defined, as a function of the weld pitch

178
Excessive weld pitch results in overheating with localized melting of the surface material. It can

be calculated from Fig. 11 that welds of good quality are obtained in the optimized range from

2.0 to 9.0 rev/mm, (Ref 12).

The operating parameters of friction stir welding for 6061-T6 Al plate is shown in Fig. 12. The

quality of the joints is judged from the weld appearance and presence of any internal defects.

Low rotational speeds do not produce stable welds because of the insufficient heat generation.

On the other hand, high rotational speed produces excessive heat and causes unsatisfactory

appearance and internal defects.

Typical weld appearance under different welding conditions is shown in Fig. 13. Good weld

joints are obtained for rotational speeds ranging from 344 to 637 rpm and welding speeds from

95 mm/min to 330 mm/min. For the welding parameters 151 rpm and 229 mm/min heat input is

too low to produce sufficient heat to induce the plastic flow of material. This can cause

additional force to be applied to the traversing pin, leading to pin failure, Fig 13 (b). Figures 13

(c) and 1 3 (d) show the weld appearance with severe surface defects caused by excessive heat

input under the conditions of 914 rpm and 140 mm/min, and 914 rpm and 330 mm/min.

The cross-sectional morphologies of the friction

stir welds for different welding parameters,

shown in Fig. 14. An onion-ring-type inner

structure consisting of concentric ovals proves

that the nugget is well-developed. Fig. 14 (a). A

small void is found on the cross section of the

welds created under the welding conditions of

914 rpm and 95 mm/min Fig. 14 (c, d). Tensile

test results show that the good weld quality is

obtained for the rotational speed from 344-637

rpm and welding speeds 95-330 mm/min, (Ref

13).

400

3S0

300

250

200 I

150

I

100

50

Defect-free welds

welds with defects » i Tool broken

0 200 400 600 800 1000 1200

Rotational spaed of tha tool, rpm

Figure 12. Operating parameters for FSW

The shapes and sizes of all of the different zones (HAZ, TMAZ plus weld nugget) are very hard

to predict because they depend on the tool design and the welding parameters, and particularly

on the welding speed (Ref 12).

a) 637 rpm and 190 mm/min; b) 151 rpm and 220 mm/min; c) 914 rpm and 140 mm/min

d) 914 rpm and 330 mm/min

Figure 13. Typical weld appearances ot the friction stir welds under different welding conditions

It is found that the welding speed has a larger effect on the weld zone size than the rotational

speed. The width of the zones (HAZ, TMAZ, and nugget) is the largest, about 60mm under the

conditions of 914rpm and 95mm/min, while the smallest is about 45mm under the conditions of

344 rpm and 330mm/min.

179
a) 416 rpm and 133 mm/min; b) 637 rpm and 95 mm/min; c) 914 rpm and 95 mm/min

Figure 14. Cross-sectional morphologies of the friction stir welds for different welding parameters

CONCLUSION

Using an infrared camera, the temperature distribution in front of the shoulder can be obtained

for different welding parameters. Rotational and linear speeds can be adjusted during the

welding in order to keep the temperature in front of the shoulder constant. The optimal range of

parameters that produce satisfactory weld quality includes rotational speeds from 344 rpm to 673

rpm and welding speeds from 95 mm/min to 330 mm/min.

The influence of the rotational speed and the linear speed on the temperature changes is

presented. The temperature change is very small for variations in rotational speed from 95 to

330 mm/min. The welding speed affects the surface temperature in front of the shoulder, and the

temperature 0.5 mm from the surface under the shoulder, more than does the rotational speed.

The influence of the linear speed on the cooling rate and zones' (HAZ, TMAZ plus weld nugget)

sizes is also observed.

It has to be pointed out that this work is still in its embryonic stage. Some other sensing

techniques will also be investigated. These are very attractive topics for ftiture research in

Friction Stir Welding and also very helpfiil for industrial applications.

ACKNOWLEDGEMENTS

The authors would like to express sincere thanks to Dr. M. Song for providing help in

performing the calculations. This work was financially supported by the U.S Department of

Education, Grant No. P200A80806-98. and the American Welding Society through the Graduate

Fellowship Grant.

REFERENCES

1. E. D. Nicholas and W. M. Thomas, A review of friction processes for aerospace

applications, Int. J. of Materials and Product Technology, Vol.13, Nos V2 , 1998.

2. G. Bruggemann and Th. Benziger, The combination of thermography and image

treatment for monitoring of quality assurance during laser welding. Independent

nondestructive evaluations 33, no. 7; 453

3. Lukens, W. E. and Morris, R. A., Infrared temperature sensing of cooling rates for arc welding control, Welding Journal, Vol. 61, No.l, pp.27-33 (1982)

180
4. Z.Loftus, R. Venable and Glynn P. Adams, Development and Implementation of a LoadControlled Friction Stir Welder, The First International Symposium on Friction stir

welding, CA, USA, 14-16, June 1999.

5. R. Jeffrey Ding, Force Characterization on the welding pin of a friction stir welding

retractable pin-tool using Aluminum-Lithium 2195, The Second International

Symposium on Friction stir welding, Gothenburg, Sweden 26-28 June 2000.

6. Mahoney, M. W. and C. G. Rhodes, J. G. Flintoff, R. A. Spurling, and W. H. Bingel,

Properties of Friction Stir Welded 7075 T65 1 Aluminum, Metallurgical and Materials

Transaction A, 29A: 1955-1964, 1998.

7. W. Tang, X. Guo, J. c. McClure and L. E. Murr, Heat Input and Temperature Distribution

in Friction Stir Welding, Joumal of Materials Processing and Manufacturing Science , v7,

n2, 1998

8. J.R. Davis, ASM Specialty Handbook-Aluminum and Aluminum Alloys, ASM

International

9. J.H. Ouyand and R. Kovacevic, Material Flow and Microstructure in the Friction Stir

Butt Welds of the Same and Dissimilar Aluminum Alloys, Accepted to the Joumal of

Material Engineering and Performance, July 2001

10. O. Frigaard, O Grong and O.T. Midling, Modeling of the Heat Flow Phenomena in

Friction Stir Welding of Aluminum Alloys, Seventh International Conference Joints in

Aluminum - INALCO '98, Cambridge, UK, 15-17, April 1998.

11. A. P. Reynolds, W. D. Lockwood and T.U. Seidel, Processing-Property Correlation in

Friction Stir Welds, Materials Science Forum, Vols. 331-337 (2000), pp. 1719-1724.

12. J. H. Ouyang, R. Kovacevic. M. Vlant and D. Jandric, Experimental Studies on Friction

Stir Welding of Aluminum Alloys, submitted to the Joumal of Materials Science

13. P. Threadgill, Friction Stir Welds in Aluminum Alloys - Preliminary microstmctural

assessment, TWI Bulletin, March/April 1997

181

A REAL-TIME MONITORING AND CONTROL SYSTEM

FOR RESISTANCE SPOT WELDING

K. Matsuyama^ R. Obert#, J-H. Chun*

ABSTRACT

A new monitoring and control algorithm has been developed based on an integral form of an

energy balance model to realize a low-cost real time monitoring and control system for resistance

spot welding. The system captures welding voltage, welding current, and total plate thickness to

calculate the mean temperature of a weld during welding. It predicts both weld diameter for the

non-destructive evaluation of weld quality and splash occurrence for improvement of the working

environment. After training with two stack welds of equal plates, the system can handle two stack

welds of unequal thickness, multi stack welds, and other thickness welds without modification of

the program parameters.

KEYWORDS

Resistance spot welding. Prediction of weld diameter. Prediction of splash occurrence. Integral

form, Energy balance model. Quality monitoring. Improvement of working environment

INTRODUCTION

Industry belief has it that the occurrence of splash, or expulsion, yields good information on weld

melting. Its evaluation has been used as quality assurance in resistance spot welding. Splash,

however, causes some deterioration of the working environment and the quality of welds. Making

a weld for comparison purposes is also wasteful of expensive energy. Furthermore, maintenance

costs are higher than necessary because the metal powders caused by the splash degrade the

moving parts of production robotics.

A new procedure addresses the problems inherent in splash during resistance spot welding. It solves deterioration issues based on the idea that splash or expulsion occurs when a weld part is

overheated, even shortly, during welding.

The authors analyze the facts of splash with numerical simulations and experiments (Ref 1). The

article demonstrates that splash occurs when the corona bond zone at the faying interface suddenly

melts. The article also suggests that continuous monitoring or prediction of weld part temperature,

i.e., of the dynamic behavior of the temperature rising pattern, is important in predicting splash

caused by overheating.

Temperature pattems and history can be continuously predicted if a monitoring procedure is

associated with a prediction system based on a numerical simulation program as an identification

routine of the weld part temperature (Ref 2). The system described in the referenced paper

* Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139

#MS Student, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02 139

183
addresses welding phenomena, current distributions, temperature distributions, and variations over

time for each tested weld. In addition to the physical property data of the workpiece, the system

only requires the monitoring of welding voltage and current.

The procedure, however, has a very high processing cost when using a standard CPU. The length

of the calculation time is 100 to 1000 times that of the actual weld time. The system is a powerful

tool for predicting the weld diameter and temperature history of welds if a high speed CPU, i.e., a high speed Digital signal processing, DSP, device with a large amount of memory, is installed on a welding controller. Therefore, the system is comparatively quite expensive and so used only in

special cases (Ref 3).

Calculation times are dictated by the procedure time required to solve the heat conduction

differential equation. A finite differential method requiring many mesh points to get accurate

temperature patterns is commonly used. The same is true when the finite element method or finite

boundary method is employed.

A new integral form of the heat equation is induced for achieving a real time treatment. The new

concept calculates the energy balance in a weld part to simplify the relationships among heat input

to the weld, heat loss from the weld part, and the temperature rise in the weld.

This paper describes fundamentals of nugget formation process to understand the basic concept, an

energy balance model, new governing equations derived from the new concept, configuration of

monitoring and control system, and experimental results of the adaptation. The effort has

developed a new low cost monitoring/control system to simultaneously end splash occurrences

and estimate weld diameters.

NUGGET FORMATION PROCESS IN RESISTANCE SPOT WELDING

Figure 1 illustrates a typical nugget formation process for resistance spot welding (Ref 4). Similar

nugget growth patterns occur regardless of the type of material used as the workpiece (Ref 2).

Figure l(a-l) to (a-3) illustrates cross sections of welds at different weld times, is the contact

diameter at the electrode-workpiece interface and dc the diameter at the faying interface. Nugget

diameter and nugget penetration are d^ and p^, respectively. Figure 1(b) shows the nugget growth

pattern with contact diameter data at the electrode-workpiece interfaces and at the faying interface.

The observation timings with arrows can also be found in Figure! (b). Broken lines in Figure 1(b)

show the molten diameter and molten thickness of the nugget. After the solid and broken lines

separate, solidification has begun, although the current conduction continues due to increasing

contact diameters during welding.

The contact diameters are inconstant during welding. This variation in diameter has a large

influence on the nugget formation process (Ref 5). Measurement of the contact diameters shows

that the contact diameter at the beginning stage of the weld cycle before forming a nugget is

smaller than at the end. Furthermore, the contact diameter of tips used for many welds is usually

larger than those used with the new electrodes. This change causes the weldable current range to

increase. A similar effect causes a shift in the weld lobe for zinc-coated sheets. The initial contact

diameters are also larger than those for uncoated sheets in spot welding zinc-coated steels.

184
0 Welding time

Figure 1: Schematic Illustration of Nugget Formation Process

V\felcingtimB t wadrgtirre t Welding time t

(a) Below the critical current (b) Near critical splash current (c) Over the critical current

Figure 2: Relationship between Nugget and Contact Diameters at Various Setting Current Levels

Changes in the contact diameter have such a large influence because the heat generation density in

resistance spot welding is a function of the contact diameter raised to the fourth power. For this

reason, the variable contact diameter concept needs to be considered.

Figure 2 illustrates the growth patterns of nugget and contact diameters at various welding current

levels. Figure 2(a) shows a pattern in lower current condition than the critical splash current

185
condition. Figure 2(b), just under the critical splash current condition, and Figure 2(c) in a

splashing current condition over the critical current. All figures show at the same electrode force

condition.

The difference between the contact diameter and nugget diameter is greatest in a low current

condition. This suggests that the mean temperature of a weld, defined as the weld zone bounded by

the contact diameter and both interfaces between workpieces and electrode tips, is lowest among

these three cases. The mean temperature in the splashing condition is the highest. This suggests

that information about the mean temperature of a weld is useful to predict the welding state,

including splash occurrence.

MATHEMATICAL CONSIDERATIONS BASED ON AN ENERGY BALANCE MODEL

The mean temperature of a weld can be estimated with both a finite difference method and two sets

of monitoring data of the welding current and resistance between tips (Ref 2). The latter procedure,

however, requires a huge investment in numerical calculation. An expensive digital signal

processor (DSP) is required to process the data. The problem can be resolved by employing a new

algorithm based on an integral form of heat conduction equation.

Basic Equation written in Integral Form

The mean weld temperature is calculated in a target volume defined as a square zone enclosed by

the four broken lines, shown in Figure 3, where dcis the contact diameter at electrode-workpiece

interfaces. The following heat Q is contained in the target volume at a time t after starting current

flow:

Figure 3: Schematic Illustration of Energy Balance Model

where Q is the heat amount contained in the target volume, / is the time, v is the voltage between

plate surfaces (measured voltage between tips [drop in tips], induced voltage by inductance), / is

the welding current, Ke is the heat conductivity in electrode tip, K is the heat conductivity in the

186
workpieces, dis the contact diameter at plate-electrode interface at time /, 7is the temperature, ht is

the total plate thickness,/(<i^) is the correction factor of current density by fringing effect, h is the

plate thickness of one plate, r is the radius direction, and z is the plate thickness direction.

A mean temperature of weld T in the weld part is defined by the following equation:

Q

(2)

h,CcndllA

Thus, the following equation is yielded:

2 dz

-\-ndh^K—\dr

ar

(3)

This is the basic equation to predict the mean temperature in the workpiece as a function of

welding time.

Estimation of Temperature History during Welding

The resistance between both electrode tip surfaces can be expressed with the following equation:

where dc is the diameter at the time /, and d is the diameter as a function of x.

Equation 5 can be used for the real time prediction of mean temperature during welding using only

welding voltage as the monitoring data. The welding current, however, should be measured

simultaneously to make the appropriate corrections for the effects of electrode tip resistance and

mutual inductance between the secondary welding current circuit and the measuring circuit.

In actual monitoring, the voltage used in Equation 5 is calculated with:

I Ttd

(4)

where p is the resistivity.

The above equation combined with Equation 3 yields:

(5)

187
v = v-'ER-i-M—

dt

(6)

where Vt is the voltage between electrode tips captured/measured, Re is the resistance drop in

electrode tips, Mis the mutual inductance between the secondary welding current circuit and the

lead cable for measuring welding voltage vt.

Equation 5, however, requires the setting of every heat loss term for applying other stack

conditions. That problem can be resolved if the equation )delded is b£ised on a heat loss criterion at

the electrode-workpiece interface, although the detail is not described here. The relation can be

expressed as Equation 7. The heat loss terms are defined only from measuring results for the stack

condition of total plate thickness ho.

T{t) = Cad'

2 72 K^d' dT

IHq dz

(7)

Equation 7 is the same as Equation 5 when the total plate thickness ht= Hq.

Scheme for the Calculation

Equation 8 is deduced as a discrete form of Equation 7 because the heat loss terms ''A " is constant

according to the above result:

d\t + At) C(7phM\ + A)

(8)

where:

A=-

Co . 4/z

A?

dr

J

z=h,l2

(9)

(10-a)

(10-b)

r=fif/2

where Hq is the total plate thickness when the heat-loss term "A " is trained with two-sheet stack of

same thickness h. ( h(p2h), a\, cli is defined in Equations 10-a and 10-b for two stack type of plate

thickness h, and d^ (t) / d^(t + At)U\.0 because At is very small.

Equation 8 can be solved by an ordinary progressive procedure, assuming that values ai and a2 are constant during each weld sequence. This procedure matches the synchronized treatment with the

188
capturing procedure of monitoring data. Equation 8 shows that the system requires only welding

voltage. The welding current, however, should be also simultaneously measured to correct the

magnetic flux effect on the measuring cables of welding voltage. Measurement of electrode tip

movement is also effective to capture the actual total plate thickness in Equation 8. Measurement

of the electrode force is useful to adjust the deflection value of the welding gun when the dynamic

electrode force changes during welding.

Feature of the Calculated Results bv the New Procedure

Figure 4 schematically illustrates a typical result calculated with Equation 8. After setting some

physical properties of materials used and training heat loss term welding voltage v as a

fiinction of welding time and total plate thickness ht are read/captured as the input data for

calculation of the mean temperature.

Welding time t

Figure 4: Schematic Illustration of Calculated Mean Temperature of Weld

The calculated mean temperature of weld increases with welding time to reach a peak value at

welding time tp if the weld time is enough long. The time tp corresponds to the starting time of

solidification, as indicated with the separation of solid and broken lines in Figure 1. This means

that the weld diameter for each weld can be estimated from this peak value of the calculated mean

temperature. When the weld time /w is shorter than the peak time tp, the time tp equals to the weld

time

Figure 5 shows the principle of predicting weld diameters and splash occurrence with the mean

temperature of weld. If the heat loss term "A" could be set in an adequate and suitable value, all

monitored data as shown in Figures 5(a) and 5(b) can be arranged with only one curve as shown in

Figure 5(c).

189
U and L in Figure 5(c) indicate threshold values of the critical splashing condition and the forming

of the minimum weld diameter required by the customer or specifications.

Splash

occurrence

Splash

occurrence

Increasing of

setting curren

0)

E

.2

CD

E

.2

'•B

Splash

occurrence

Increasing

weld number

Welding time t

(a)

Welding cuaent /

(b)

0

0)E

.2

T3

I Splash

occurrence

Required weld

diameter

Target range

for control

U

Peak value of estimated mean

temperature of weld T

(c)

Figure 5: Prediction of a Welding State from Peak Values of the Predicted Mean Temperature

190
SYSTEM CONFIGURATION FOR MONITORING AND CONTROL

Figure 6 shows an example of the system configuration. The system minimally consists of a

welding controller including power control unit and transformer, an electrode force control unit, a

welding gun, a data capturing and treatment computer, an AD converter board, and some sensing

devices including a current sensor. The AD converter board connects to four sensing cables;

welding current, welding voltage, electrode force, and electrode tip movement, although Equation

8 directly requires only one monitoring data of welding voltage.

Welding Controller StartSW

Force Control Unit

Indicator or Display

Power Control Unit

Figure 6: Typical configuration of the New Monitoring/Control System

191
The welding current corrects the influence of electrode tip drop and induced voltage on the

measured welding voltage by the secondary welding current. The information on electrode force

and electrode tip movement is employed to estimate the dynamic behavior of total plate thickness.

Alternatively, the later two monitoring parameters can be replaced with the nominal value of total

plate thickness.

START

Input setting parametes & Threshold

start of W elding

Capture Monitoring Parameters

I

Calculate Mean Weld Temperature

based on the Energy Balance Model

I

Predict Potential of Splash &

W eld D iam ater

Yes

Yes

H

Halt Current Flow

END ^

Figure 7: A Flowchart to Monitor and Control to Eliminate Splash Occurrence

Figure 7 is a flowchart to predict the weld diameter and eliminate the splash occurrence in real

time during welding. The weld diameter and the possibility of the splaish occurrence are predicted

in the stage of" Predict potential of splash & weld diameter" as future values of one or a half cycle

later after the capturing time to control the next ignition angel for current flow. In the prediction of

the next half or one cycle of welding current conduction, the calculation is achieved under an

assumption that the same welding voltage waveform is repeated.

The program is in Visual Basic, run on a Windows-based computer with a 66 MHz bus and a

192
300MHz CPU. Data sampling is repeated in 2.4kS/s. Total CPU time including data capturing,

data treatment, and machine control is less than 60% of the total welding time. This means the

procedure can be realized as a low cost real-time system.

ANALYSIS OF THE NEW PROCEDURE: A REAL TIME PREDICTION

SYSTEM OF SPLASH OCCURRENCE AND WELD DIAMETERS

The new monitoring procedure was experimentally examined to understand how to predict the

splash occurrence and nugget formation process. The experiments were carried out with an AC

spot welding machine mounted pneumatic gun. Two types of zinc-coated steels were used as the

test pieces.

Prediction of Splash Occurrence during Welding

0 jQ?^ -> Time

(a) Splashing during welding

(b) No splashing and making sound weld

Figure 8: Prediction of the Mean Temperature History during Welding with the New Procedure

(jP = 200 daN, t= \0 cycles of 60 Hz, h = \ mm x 2: Organic zinc coated sheets)

193
Workpieces for the first trial were steel with a one-sided organic-zinc coating. Plate thickness was

1 mm. The test pieces were made from two stacked sheets. Electrode tips were the 16 mm dome

type.

The experiments were done with coupons of 300 mm x 300 mm at 200 daN, 10 cycles, and 8.0 kA.

Weld diameters were measured with small coupons of 30 mm x 100 mm to observe shifting of the

weld lobe during electrode tip life test. The weld lobe test was administered every 1000 welds. In

the weld lobe test, the welding current was varied between a no nugget condition to a current value

higher than the critical splashing condition.

Examples of the predicted result with the new monitoring procedure based on Equation 8 are shown in Figure 8. The parameter "A" was determined with a few training data captured for a

two-sheet stack type. A measured welding current /, a modified weld voltage v after reduction of

induced voltage and tip resistance drop, a resistance r between both plate surface calculated with

the current and modified voltage, a dynamic electrode force F, and a predicted mean weld

temperature Tare shown. An arrow indicates the time of splash. A dotted line illustrates the critical

temperature level. The sum of plate thickness stacked is input as the total plate thickness in the

present calculation

Figure 8(a) shows an example of splash. A splash occurs when the temperature is over the

threshold line indicated with a one-dotted line. There is no splash shown in Figure 8(b); the peak

value of mean temperature is lower than the threshold line. The same relationships for other weld

number specimens. This demonstrates that the new concept described in Figures 4 and 5

effectively correlates the splash occurrence with the mean temperature.

Furthermore, the difference of peak times indicates variations in the incubation time needed for

nugget growth. If the peak period does not appear by the end of the current conduction, the weld

time can be increased to produce a bigger nugget.

Arrangement of Weld Diameters and Splashing Conditions with the Weld Temperature

Measured weld diameters are plotted in Figure 9(b). They are shown as a function of the peak

value of the predicted temperature in the weld part. Solid marks in the figure indicate a spleish

condition. Figure 9(a) shows the same data arranged as a function of the RMS welding current.

There is good agreement between the peak value of temperature during welding and measured

weld diameter in Figure 9(b), although the relationship between the welding current and the weld

diameter is not close. Figure 9(b) presents all monitored data arranged on a single curve.

Splash/expulsion (expressed as solid marks) occurs only at temperatures higher than about 1700°C

for organic zinc coated steels. This suggests that the new concept and procedure is applicable not

only to predict splash, but also to predict weld quality, and that to build up a new adaptive control

system would be an easy task.

194
i

I

3-

2-

^=200(itN fslOoydos

A= 1 mm X 2

Orginic zinc coated «t«el« i

a

*

o

D

SoMnurka wen

O

oaoumd sphsh

IS

O Aftor 300 welds

O 1000

A 2000

o

V 3000

a 6000

AS 7.0 73 d.0 8.5 8.0 Welding current IW

0.5 10.0

(a) Weld diameter vs. RMS value of welding current

8.0

6.0

E

I

^0

2.0

0.0 1

A = 1 .0 mm (Organic

zino ooated 8te«lB)

/ = 6 to 10 kA (ac)

F = 200 daN (air sun)

t= 10 cyciae (60 Hz)

Doma ^pa (64. 50R.^6»>

o

a

; aftar 300 walda

1000

2000

3000

5000

! L

900 1100 1300 1600 1700 1900

Estimated temperature of weld T^.peak C C)

(b) Arrangement of the weld diameter data with the peak

temperature predicted by the new procedure during welding

Figure 9: Adaptability of the New Prediction Procedure of Splash Occurrence and

Weld Diameters

Adaptability for Other Stack Conditions

Galvannealed steels coated on both sides were also examined as the second trial for various

combination stacks. The tests were completed during an electrode tip wear test. The electrode tip

195
wear test was carried out in the following condition with 1.0 mm thick coupons of 300 mm x 300

mm. The wear test condition is shown below:

Welding current 8 kA (AC)

Electrode force: 200 daN

Weld time: 1 0 cycles of 60Hz

Test materials: Galvannealed steels sheets (60/60 [g/m^ ])

Electrode tips: Dome type tips (8R, 6(1)-50R)

Welding rate : 1 weld / 3s

Cooling water: 3.5 1/min

Six combinations of plates were examined after about 100 welds and after every 1000 welds with

the test conditions shown in Table. 1. Figure 10 shows configurations of the test pieces.

(a) I

(d)

(b )

(e)

(c )

(f)

Figure 10: Configuration of Stacks used for the Tests

Table 1 : Size of the Test Pieces and Testing Conditions

Plate (mm) /(kA) F (daN) (cycles) Pre-weld

0.8 + 0.8 Varied 200 Varied No & exist.

0.8+0.8+0.8 Varied 200 10 No

0.8+1.8 Varied 200 10 No

0.8+0.8+1.8 Varied 200 10 No

0.8+1.8+0.8 Varied 200 10 No

1.8+1.8 Varied 400 20 No

Weld diameters were measured at each interface between the top plate and the next after a peel or

torsion test. The size of small coupons are 30 mm x 100 mm for 0.8 mm, and 50 mm x 150 mm for

1.8 mm x2. Carbon prints of electrode tips are shown in Table 2.

196
Table 2: Carbon Print of the Electrode Tips during the Test

Observed After 100 welds After 1000 welds After 2000 welds After 3000 welds

Upper

electrode tip •

_

Ijower

electrode tip • 5mm

_L

10 -

8 -

E

E, 5 6-1

B

•S A.

2 -

After 100 welds and

after 3000 welds

Galvannealed steels (60/60)

O:

A::

O:

V:

•;

0.8

0.8 + 0.8

0.8 + 0.8

0.8 + 1.8 0.8 + 0.8 + 1.8 0.8 + 1.8 + 0.8

1.8 + 1.8

(/==200daN, fw=10cycles & Van.)

(F=200daN. A«=10cycles)

(F=200daN. f«»=lOcycles)

(F=200daN. Ai^lOcycles)

(F=200daN. A«=10cycles)

(F=400daN. ^w«20cyc]es)

ZD

O o

i

oo

Electrode wear tests

/ = 8kA

Av=lOcyclas

F =200daN

h= 1.0 mm *2 Galvannealed steels

1 weld /3s

Qw = 3.5 l/min T 1 1 1 1 1 r

5 6 7 8 9 10 11 Welding current / (kA)

(a) Weld diameter vs. RMS value of welding current

I I 1 1 1 i

8 -

E

I

E

After 100 welds and

after 3000 welds

. AO

A

«7

Galvannealed steels (60/80)

O: 0.8 + 0.8 A: 0.8 + 0.8 + O.a : 0.8 + 1.8 O: 0.8 + 0.8 + 1,8 V; 0.8 + 1.8 + 0.8

1.6 •)- 1.8

1 1 r1100 1300 1500

Mean temperature in weld Tw.peaK C^)

O V

(/=^200daN, *«=10cycles S V6ri.) {F=200daN, /walOcycles)

(/==200daN, «w=10cycles)

(F^200daN, **=10cyc(es)

(F=200daN. fw=10cycle3)

(F=400daN, A«=20cycles)[—

700 900 1700

(b) Arrangement of the data with weld temperature

Figure 11: Test results for Galvannealed Steels of Various Stack Conditions

Figure 11(a) shows the test results as a function of welding current. The weld diameters were

197
measured only for welds after 100 welds and after 3000 welds although the wear tests were done

after 4000 welds. Marks with a character "s" in the figure indicate that a splash occurred during

welding. Scatters are observed in the figure when the measured weld diameters are arranged with

the welding current value.

Figure 11(b) shows the relationship between the measured weld diameters and the peak values of

estimated mean weld temperature calculated by Equation 8. The data were separated into two

groups: One for the thinnest stack thickness, 0.8 mm with a 200 daN electrode force; the other for

the two stack type of 1 .8 mm with a 400 daN electrode force.

The contact diameter varies depending on the square root of electrode force F applied on the

weld part because of the contact diameter described by (Ref. 6):

F = C,ayS-g{dJh) (11)

where F is the electrode force applied, Cs is a constant («2.6 - 3.0), Oy is the mean yield stress of

weld part, S is the contact area {itdcl^), and g(Jc ^) is the correction fiinction. The value is

supposed to be equal to 1.0 in this case.

This equation suggests that the measured weld diameter should be normalized with the ratio

yjF I Fq to arrange all data on one curve. Figure 12 plots the normalized data according to the

above consideration. The maximum scatter of weld diameter is less than V//. Galvannealed steels

also had good agreement.

f 6

E

I

c:

I n

.1o

I

OH

After 100 welds and

after 3000 welds

SJK

for 0.8 mmt - 6

Galvannealed steels (60/60)

O: 0.8 + 0.8

A: 0.8 + 0.8 + 0.8 -.0.8+1.8

O: 0.8 + 0.8 + 1.8 V: 0.8 + 1.8 + 0.8 •: 1.8 + 1.8 -I 1

1 r700 900 1100 1300

Mean temperature in weld

r 1600

Vpeak t*^)

I- 9 e

E

o 2^

««- c £ <u

o £

i

- 0

1700

Figure 12: Arrangement of Normalized Weld Diameters with the Predicted Mean Temperature

All weld diameters are greater than the required weld diameter; 5V/2 when the weld temperature is

198
higher than 1200°C. The minimum threshold value remains constant in spite of the coated

materials, as shown in Figure 11(b), while splash occurs when the estimated weld temperature

exceeds 1400°C. The upper threshold value is lower than that shown in Figure 9(b) for organic

zinc coated steels. This suggests that the surface condition can influence the upper threshold

temperature of the critical splash condition.

Alternatively, the normalized weld diameter can be estimated by the dynamic resistance curve

calculated with the welding voltage and welding current because the contact diameter dc is related

to the dynamic resistance based on Equation 4. This ahemative is especially effective on the low

current and long weld time conditions like the class C conditions described on the RWMA

(Resistance Welding Manufacturing Association) table.

Table 3: Threshold Value for the Critical Splash Condition vs. the Stack Configuration

Stack configuration

(mm)

Corrected spiash based on the ^w,spiash shown in Fig. 12

stack configuration* (°C) (°C)

0.8 + 0.8 1400 1400

0.8 + 0.8 + 0.8 1488 1450

0.8 + 1.8 1515 1500

0.8+1.8 + 0.8 1652 1700

* Threshold values were calculated based on the value for the 0.8+0.8 case

and information on the stack configuration of each test piece.

The variation of the critical splash current values depend on the stack condition of workpieces can

be explained by the temperature distribution along the plate thickness direction during welding. If

the analytical solution can be applied for estimation of the temperature distribution, the peak

temperature at the faying surface of each stack condition can be estimated with the experimental

result on a two stack condition, and faying interface position of the stack. Estimated results of

critical splash current for each stack are shown in the middle column of Table 3. The values were

calculated based on the measured value under two sheet stack of 0.8 mm + 0.8 mm. Temperatures

in the left column in Table 3 show the measured results in each stack conditions. Each

corresponding values closely coincide with each other.

This suggests that if the information on the stack configuration of workpieces, the accuracy of

threshold value for each critical splash condition expressed as the temperature can be improved.

CONCLUSION

A new monitoring and control system was developed based on the energy balance model in a weld

part during resistance spot welding. Mathematical analysis deduced a new governing equation and

procedure. The new concept, described in integral form, was proved with prior data and

experimental results. This concept realizes a new adaptive control system that not only predicts

splash occurrences in real time but also estimates weld diameter. Important conclusions of the

present research are listed below:

199
1) A new governing equation of integral form was developed based on an energy balance model

in a weld part during welding.

2) A new discrete equation can be applied to real time prediction of weld diameter and splash

with at least two monitoring data, i.e., welding voltage, welding current, and one

pre-measured value of total plate thickness.

3) If the measured weld size is normalized by the square value of the electrode force, there is

good agreement between the normalized weld diameter and the peak value of mean weld

temperature.

4) The maximum weld diameter is closely related to the applied electrode force for welding.

5) Only the upper threshold for defining critical splash current is influenced by the surface

condition of workpieces. The lower threshold for minimum weld diameter is influenced by

physical properties of the bulk materials.

6) Threshold values for splash occurrence in various stack configuration condition can be

estimated with information on stack configuration and position of faying interface in the stack

based on a measuring result of the mean weld temperature for two stack condition of equal

sheets..

ACKNOWLEDGEMENT

The authors are very grateful for the support provided by Kawasaki Robotics (USA) Inc., Osaka

Denki Co., Ltd, Dengensha Mfg. Co., Ltd., and Robotron Inc.

REFERENCES

1. Nishiguchi, K, Matsuyama, K., et al., 1987, Influence of current wave form on nugget

formation phenomena when spot welding thin steel sheet. Welding in the World. 25 (11/12),

pp 222-244.

2. Matsuyama, K. et al, 1990, Computer-Aided Monitoring System of Nugget Formation

Process, The fifth Svmp. of Japan Welding Society (JWS) in Makuhari. pp 577-582.

3. Fujii, K., Matsuyama, K., et al, 1995, Resistance Spot Welding Monitors, Automotive

Manufacturing International '95 . pp 168-173.

4. Matsuyama, K., Obert, R., Chun, J-H., 2001, Inverse method for measuring weld

temperatures during resistance spot welding, SAE. SP-1621, pp. 13 1-137

5. Nishiguchi, K., Matsuyama, K., et al., 1978, A study on nugget formation process in

resistance spot welding of multi stack plates. Com, on Resistance welding in Japan Welding

Societv. Doc. RW- 148-78 (in Japanese).

6. Matsuyama, K., Chun, J-H., 2000, Splashing mechanism in resistance spot welding, Proc. of

International Sheet Metal Welding Conference in Steringheight IX, Paper. 5-4

200
Session Bl: Modeling I: Distortion and Stress

201

AN INTEGRATED FEA BASED PROCEDURE FOR WELD FIXTURE DESIGN

Z. Yang*, X. Chen*, Y. Dong'

E. Martin*, D. Michael*

ABSTRACT

As mechanical forces and welding induced thermal stress are applied during welding, the

strength of the weld fixture is one of the crucial factors determining a successful or

unsatisfactory welding practice. In the present study, an integrated FEA based procedure for

welding fixture design was established. In the integrated FEA procedure, the welding induced

stress and distortion were simulated by sequentially coupling thermal and mechanical modeling

using a comprehensive welding simulation package. The welding simulation results showed that

thermal induced reaction forces were significant at the constraint locations, which must be

considered for weld fixture design. The magnitudes of reaction forces varied depending on

constraint locations during welding process. The obtained maximum reaction forces were then

input into weld fixture structural analysis. The predicted stress and distortion from the fixture

structure analysis provided a guideline for the weld fixture design.

KEYWORDS

Welding Simulation, FEA, Stress and Distortion, Fixture Design

INTRODUCTION

Residual stresses and distortions are two of the major concerns in welded structures. The

welding induced residual stress can exceed the yield strength of the material and is detrimental to

the integrity and fatigue life of the welded parts. Welding can also cause the welded products to

distort significantly from their original shape, which may require costly post-weld treatment such

as machining or straightening. In recent years, tremendous efforts have been made to reduce

weld distortion. Various methods have been proposed to accomplish this, including precambering, pre-bending, preheating, and thermal tension.

Thermal-mechanical FEA of the welding process is an emerging and rapidly maturing technique.

Computer aided design of the welding process is becoming an efficient and effective approach to

achieve high quality weld products in industry (Ref 1-5). Welding simulation helps to optimize

welding procedure (welding parameters, sequence, and weld joint geometry) and apply

appropriate mechanical or thermal methods to reduce welding induced residual stress and

distortion. In recent years, welding computer models have demonstrated the capability to reduce

fabrication costs, improve weld quality and increase service durability by optimizing the weld

process.

* TechnicayService Division, Caterpillar Inc., 1311 East Cedar Hills Dr., Mossville, IL 61656-1875

* Mining ^nd Construction Equipment Division, Caterpillar Inc., 27 Pershing Rd., Decatur, IL 62525-1817

/

203
The weld fixture is normally used to facilitate assembly of parts and to hold the parts in a fixed

relationship for welding fabrication. In welding industry, it is common to employ techniques

prior to the welding processes that induce distortions in the material that are essentially opposite

to the distortions induced by the welding process. Pre-cambering is such a method that places the

material into a weld fixture, which holds the material in the desired distorted shape until welding

is completed. The intent of the induced distortions is to mitigate the distortion induced by

welding to maintain the desired shape after welding.

Weld fixtures have typically been designed based on empirical data and past experience. Many

trial-and-error tests are needed for designing a new weld fixture, which leads to not only costly

design but also delays in production. Furthermore, a weld fixture for pre-cambering must be

designed to withstand reaction forces from the mechanical distortions induced on the material,

and from thermal distortions produced by the weld process. However, the thermal induced

reaction forces were usually ignored or not fully understood in previous fixture design due to the

lack of knowledge in this area. As a result, the designed weld fixture might deform or even

fracture during welding, which significantly affected the quality and reliability of welded

product.

In this paper, an integrated FEA based procedure for weld fixture design is proposed. In this

procedure, the thermal and mechanical reaction forces were considered in combination to design

weld fixtures for welding processes, especially for pre-cambering technique. Welding induced

distortion and thermal reaction forces were calculated by using a comprehensive welding

simulation tool (Ref. 1-5), which has been proven to be very efficient and effective in prediction

of weld distortion and residual stress in large fabricated structures in industry.

OUTLINE OF STUDY AND METHODS

1. Modeling Algorithm

The algorithm of the integrated FEA based fixture design procedure is shown in Fig. 1. The

procedure can be categorized into three steps: (1) model preparation, (2) welding simulation, and

(3) fixture strength analysis. Steps (1) and (3) are common in FEA for structure analysis. Step 2,

welding simulation, is critical in the present study. It calculates the thermal distortion in the

3D solid model

ProE

Geometry^

' 1 I Mesh generation

MS IDEAS

FE

Thermal stress analysis

ABAQUS fotrces

" Structure Analysis

MS IDEAS

Transient temperaturefield

Thermal Analysis

CTSP

Stress & distortion I

Fixture Design

Model preparation Welding simulation Fixture strength analysis

Fig. 1 : Flow chart of FEA based weld fixture design.

204
weld products and reaction forces to the fixture during welding process. The reaction forces were

then input into weld fixture model for stress analysis and distortion prediction in order to

examine the stiffness of the designed weld fixture.

1.1 Welding Thermal Analysis

The welding simulation was done by sequentially coupling thermal and stress analysis. Two

approaches, numerical or analytical solution, can be taken for thermal analysis. Numerical

solution is the commonly used approach in FEA thermal-mechanical analysis. However, it requires extensive manpower and computer resources to analyze complex weld structures. In the

present study, an analytical based Comprehensive Thermal Solution Procedure (CTSP) was used,

which is able to simulate temperature-time history for various types of weld joints (V-groove, Tfillet and lap joint) in complex welded structures (Ref. 1, 2). The details of CTSP are given in

literature and only its essential features are given here (Ref. 1):

T{C,y,z,t) = T^X^,y,z)-F[;,y,z,t)

Q vC + vR

exp

\oc J

la

\ + erf +

JJ

\ + erf

f vt-R

(1)

(2)

(3)

where T{^,y,z,t) is the transient temperature field, T^^{^,y,z,t) is the quasi-static state

temperature field, F(^,y,z,t) is the transient transform function, Q is welding heat input, X is

conductivity, R is the radius from point heat source, v is welding speed, a is thermal diffusivity,

erfis error function, and ^ = x-vt , with the heat source traveling in the jc direction. It should be

noted that equations (1), (2), and (3) are valid for an infinitely thick plate. For plates with finite

thickness, it is assumed that the heat loss from the surfaces is negligible in comparison with that

due to conduction in an infinite body and a number of reflected heat sources are used to balance

heat loss at surfaces.

1.2 Welding Stress Analysis

The weld residual stress analysis was done using commercial FEA software - ABAQUS. A

special user material subroutine (UMAT) was developed to account for most of the essential

features in welding process, which include the effects of stress/strain history annihilation due to

material melting/remelting, weld metal deposition, and material phase changes. The details for

the weld UMAT and its numerical implementation are given in literature (Ref 3, 4). The

material properties critical to weld residual stress analysis (yield strength, thermal expansion

coefficient, true stress-strain curves) were determined experimentally.

1.3 Fixture FEA Structural Analysis

The objective of fixture FEA structural analysis was to evaluate the stress and distortion of the

fixture due to the reaction forces when parts are pre-cambered and welded in the fixture. The

analysis used the loosely coupled method. The reaction forces at various clamping locations

were obtained from the welding simulation where the clamping locations were assumed to be

205
fixed. The forces were then appUed to the fixture model and the stress and distortion were

assessed using hnear FEA structural analysis.

2. Case Study

In this case study, a pre-cambering robotic welding process was used to control welding

distortion to eliminate post-weld treatment such as straightening and machining. Welding

simulation and weld fixture stiffness analysis were the major tasks to define a pre-cambering

robotic welding process that would hold the flatness tolerance after welding.

2.1 Welding experiments

A large fabricated structure, as shown in Fig. 2, was robot welded using GMAW process. The

weld material is equivalent to ASTM A572 steel. There are total 26 welds in consideration in the

product, as indicated by the red arrows in Fig. 2. The total welding length is about 20 meters.

The welding sequence was optimized for minimum distortion based on welding simulation

results. Single-pass 10 mm and 6 mm fillet welds were used on top and bottom sides,

respectively. The welding parameter for 10 mm welds were: 33.7 V, 400 A, and 4.8 mm/s. The

welding parameters for 6 mm welds were 23.5 V, 265 A, and 7.0 mm/s. Prior to welding, the

structure was tack welded and pre-cambered in the fixture. The magnitudes and direction at

various pre-cambering locations varied depending on the predetermined results from the welding

simulation.

(a) (b)

Fig. 2: Welds in the studied structure: (a) top side and (b) bottom side.

2.2 FEA models

The FEA models for the weld product and weld fixture are shown in Fig. 3(a) and (b),

respectively. The FEA model for the weld product was built with hexahedral solid elements.

There were 19,820 elements and 25813 nodes in the weld product model. The size of elements

within and near the welds was very fine so that welding induced thermal and mechanical features

can be captured. In contrast, the size of the elements far away from weld line was very coarse for

efficient computation. The weld fixture FEA model consisted of three types of elements - solid,

shell, and beam elements. There were 74,374 elements and 80,260 nodes in the weld fixture

model.

206
Fig. 3: FEA models for (a) weld product and (b) weld fixture.

2.3 Results and discussions

2.3.1 Temperature and stress fields

Examples of the calculated transient temperature and stress fields during welding are shown in

Fig. 4(a) and (b). As shown in Fig. 4(a), the transient temperature field due to the moving

welding heat source is well demonstrated. The welding induced residual stress is in the

magnitude of the yield strength of the material (around 450 MPa). This predicted residual stress

magnitude is reasonable and comparable with those reported in literature.

(a) (b)

Fig. 4: (a) calculated temperature field and (b) calculated stress field.

2.3.2 Reaction forces on weldfixture

In the welding simulation, the clamping locations were assumed to be fixed in the welding

model. The reaction forces on weld fixture at various pre-cambering locations during and after

welding are shown in Fig. 5. As stated in previous sections, the reaction forces during welding in

this case study were the combination of the mechanical force from pre-cambering and the

thermal induced force from welding. The mechanical forces due to pre-cambering are shown at time zero in Fig. 5, where the welding process was not yet started. The values of the mechanical

forces at various locations depend on the magnitudes and directions of pre-cambering at the

corresponding positions.

207
200000

150000

100000

S. 50000

CO -50000

(D a:

-100000

-150000

-200000

Welding stage Cooling stage

Aim^ gnnp .eQQO 71300 80 10

Location 4'

-•— Location 1 -•—Location 2 -*— Location 3

Location

Location 5 -•—Locations

Location 7 -—Locations —Location 9

Location 1

• •> Location 1

Location 1

- Location 13

Location 14

Time (s)

Fig. 5: The reaction forces at various clamping locations during welding process

From Fig. 5, it can be observed that the reaction forces varied significantly during the welding

and subsequent cooling stage. Such variations resulted from welding induced thermal stress. It can be seen that the reaction forces at some locations were almost doubled due to welding

induced thermal reaction forces. For example, the reaction at location 4 before welding (time=0)

was around -85 kN and its value reached -153 kN after welding. Therefore, the fixture would

have been significantly under-designed without considering the thermal induced reaction force.

2.3.3 Fixture Stiffness Analysis

The stiffness of weld fixture was examined through linear static analysis, in which the reaction

forces at the final cooling stage from the welding simulation were applied to the fixture FEA

model. This is because the maximum reaction forces at most of the pre-cambering locations were

reached at the final cooling stage, as shown in Fig. 5. The results from the structural analysis

show that the fixture would be significantly deformed, as shown in Fig. 6(a), and the maximum

wai-ts. -I' B.C. ^jiwijicratHt t ,T«.ita*

dtrwwiTiiWr t- a.c- v.ot»--><int.>if-i.is;w.t«ii

1 .5!lV,/ij

-2.05 mm -2.31 mm

3«

(a) (b)

Fig. 6: (a) Deformation of weld fixture and (b) Stress distribution in weld fixture.

208
stress in the "hot" spot in the model is 518 MPa, as shown in Fig. 6(b), which is much higher

than the yield strength of the material. This implies that the original fixture was under-designed.

During welding experiments, it was also observed that some clamps for pre-cambering were

broken during welding due to significant thermal induced reaction forces. The failure of the

clamps in the fixture and its global deformation would deviate the defined pre-cambering scheme

and thus affected the flatness of the bottom plate after welding.

Based on the results of the fixture structure analysis, the weld fixture was modified to increase

the stiffness in order to account for welding induced additional reaction forces. The deformation

and stress distribution in the modified weld fixture is shown in Fig. 7 (a) and (b), respectively.

Compared to that in original fixture, the deformation in the modified fixture was significantly

reduced. Also, the stress in the modified fixture model was reduced to 298 MPa. Therefore the

modified weld fixture is strong enough to withstand the combination reaction forces and

maintain the pre-cambering scheme.

.t»*->Vaw»ii^m4hjeoi>'*ur«.iirt*Jtfa

i- t.t, J rus-i-<ft**vt,-v.'»".vCu.s •.

-0.78 mm

watts.- r jn«S;.j.ti;ni,3«

i.eatt'Ot

Fig. 7: (a) Deformation of weld fixture and (b) Stress distribution in the modified weld fixture.

2.3.4 Effect ofweldfixture stiffness on productflatness

The distortions at critical points on the bottom plate of the welded parts were measured after the

parts were cooled to room temperature and released fi-om the fixture. The flatness check was

4

3.5

E. 2.5 -H

if) n I 1 <D ^ T

B

1.5

0.5 -

4 5 Part number

Fig. 8: Distortions in weld products using original and modified weld fixtures

(Parts 1-3 used original fixture and parts 4-7 used modified fixture)

209
made for seven weld parts, of which three were welded using the original weld fixture and four

were welded using the modified weld fixture. The degree of flatness in these weld parts are compared in Fig. 8. It can be observed that the distortions in the three parts using the original

fixture were much larger than the flatness tolerance (1.5 mm). As mentioned previously, this is

primarily due to the failure of the parts in the fixture (clamps) and the global deformation of the

fixture during welding. In contrast, the distortions were significantly reduced after using the

modified weld fixture that was stiff enough to support the pre-cambering scheme. Almost all

four parts meet the flatness requirement.

SUMMARY AND CONCLUSIONS

An integrated FEA based procedure for weld fixture design was proposed in the present study.

The procedure was applied to examine the strength of an existing weld fixture by taking into

account both the mechanical forces from pre-cambering and the welding induced thermal

reaction forces. The following conclusions can be made based on the present study.

(1) Welding induced thermal reaction forces are significant and must be considered in weld

fixture design, especially when the fabricated structure is large and the number/length of

welds increases.

(2) Weld fixtures may deform or even break during welding if the reaction forces fi*om thermal

distortions are not considered. The deformation of the fixture itself will directly affect the

pre-cambering process for distortion control.

(3) Welding simulation is critical in the integrated FEA based procedure for weld fixture design.

The welding simulation tool used in this study has demonstrated the capability to effectively

predict residual stress, distortion, and reaction forces, which are critical for weld fixture

design.

REFERENCES

1. Cao, Z., Brust, F. W., Nanjundan, A., Dong, Y., and Jutla, T.: Advances in Computational

Engineering and Sciences: Eds. S. N. Atluri and F. W. Brust, p. 630, Tech Science Press,

2000.

2. Cao, Z., Brust, F. W., Nanjundan, A., Dong, Y., and Jutla, T.: A Comprehensive Thermal

Solution Procedures for Multiple Pass and Curved welds. 2000 ASME Pressure Vessels and

Piping Conference, Seattle, Washington, July 23-27,2000.

3. User Manual for UMAT-CAT - A Welding Specific User Material Routine Interfaced with

ABACUS. Version 3.1, Caterpillar Inc., Peoria, IL, September 1999.

4. Brust, F. W., Dong, P., and Zhang, J.: Advances in Computational Engineering Science. Eds.

S. N. Atluri, and G. Yagawa, p.51, Tech Science Press, 1997.

5.1 Brust, F. W., Yang, Y., Dong, Y., and T. Jutla: Advances in Computational Engineering and

Sciences: Eds. S. N. Atluri and F. W. Brust, p. 714, Tech Science Press, 2000.

\

\

210
WELDING INDUCED DISTORTIONS MODELING OF LARGE PLATE STRUCTURES

R. D. Everhart*

ABSTRACT

A method for predicting the welding induced distortions of large plate structures is described.

Predictive, three-dimensional modeling of large structures presents some special numerical

challenges. As models become large (as required by accuracy considerations) demands on

computer resources grow quickly. Run times can grow to days and weeks. Computer memory

can be overrun quickly, and the simulation becomes impractical. To overcome some of these

challenges, a method that is accurate and numerically efficient is detailed. The methodology is

incorporated in a new computer program called EFFECT (Efficient, Fast First-principle's Engine

for Combined Thermal-mechanical). EFFECT incorporates a very efficient thermal solver.

Temperature distributions are generated "on the fly" to drive the mechanical solution. Finite

element shell elements are used to model deformation of the plate structures. The BelytschkoTsay large displacement, geometrically non-linear shell element is employed. A selection of

sophisticated plasticity models that accurately deal with material melting and solidification are

available. Explicit time integration is used for the mechanical solution. The shell element that is

used is computationally efficient, and the explicit time integration greatly reduces computer

memory requirements. A novel time scaling technique is used to deal with the thermal and

mechanical time scale differences. This method is especially useful for predicting the buckling

and out-of-plane warping of large plate structures. Model results are compared with distortion

test data from large steel test structures. The model is able to accurately predict buckling and

warping behavior.

KEYWORDS

Welding, modeling, finite-element analysis, distortion, explicit time integration.

INTRODUCTION

A method for predicting the welding induced distortions of large plate structures is introduced in

this paper. Some background on the problem will be given, the computational methods will be

described, then two examples involving warping and buckling will be presented.

BACKGROUND

As pointed out by Brown and Song [1], accurate distortion modeling of large, complex structures

requires three-dimensional models. Three-dimensional finite element models of these structures

include many thousands of degrees of freedom. This presents a considerable computational

resource challenge even for modem computers. It is not practical for an analyst to wait several

days, or even weeks, for the results of an analysis. A reasonable goal for analysis turn-around

time is for an analysis to run over-night. An analyst can submit an analysis before leaving work

' Edison Welding Institute, 1250 Arthur E. Adams Drive, Columbus, OH 43221

211
for the evening and have the results waiting the next morning. Run times of this order are the

goal of the present development effort.

In order to meet the computational performance goals of this development effort, a paradigm

shift was made in the solution strategy for these types of structures. Implicit time integration

was discarded in favor of explicit time integration. Explicit time integration is used in such wellknown fmite-element analysis codes such as LS-DYNA [2,3].

The use of explicit finite-element analysis techniques produces two benefits that are critical to

the practical analysis of large structures. First, the in-core (RAM) memory requirement is vastly

reduced. Explicit methods do not require the formation and storage of a stiffness matrix.

Second, the explicit solution method is very efficient for large, highly non-linear problems. The

in^licit solution method convergence difficulties for highly non-linear material behavior are avoided.

This added performance comes at a cost however. Time scales must be carefully managed. The

major inhibitor to the use of explicit time integration methods for these class of problems is that

they are conditionally stable and are subject to the Courant condition [3]. The end result is that

time steps are on the order of a fraction of a microsecond. It is easy to see that for welding

simulations (typically several hundreds of seconds) many millions of time steps would be

required for a simulation. This is clearly unacceptable. Therefore, time-scaling methods are employed to manage these time scale disparities. This technique and others are introduced in the

computational methods section.

COMPUTATIONAL METHODS

The computational methods used for modeling welding induced distortions for large plate

structures have been implemented in an analysis code called EFFECT (Efficient, Fast Firstprincipals Engine for Combined Thermal-mechanical). EFFECT can be thought of as a

numerical test-bed for the consideration of explicit distortion analysis methods. In the following

sections, some of the more critical numerical methods employed in EFFECT will be introduced.

There are two types of finite elements used. The plates that make up the structures are modeled

with shell elements. The shell elements follow the formulation by Belytschko and Tsay [4]. A

five-point numerical integration scheme is used in order to give good non-linear response

performance. The weld beads are modeled explicitly with hexahedron continuum elements.

They follow the formulation of Flanagan and Belytschko [5]. Both element types have a long

history of successful application in such codes as LS DYNA [2,3].

The thermal solution is performed concurrently with the mechanical solution in the same explicit

time integration loop. They are fiilly coupled. In order to deal with the disparity between time

scales for the mechanical and thermal solutions (the stable time step for the thermal solution is

much larger), the thermal solution uses sub-cycling. That is, several mechanical solution steps

are performed for each thermal solution step. Using this strategy, the thermal solution time ends

up being a small percentage of the total solution time.

212
The thermal solution algorithms used are similar to those developed by Liu and Belytschko [6].

The method is extended to hexahedron continuum elements and shell elements. The shell

elements use a split integration technique. A two-dimensional transient heat conduction solution

is performed in the plane of the element. A one-dimensional transient heat conduction solution

is performed through the thickness of the element. This allows for full through-thickness effects

to be included. This is important in weld zones.

The heat energy from welding is input to the system in two ways. First, the weld bead goes in

"hot" (as it does in real life). The hexahedron elements that make up the weld bead are activated

at a pre-determined temperature. Usually, this is the melting temperature of the material. The

elements are activated according to the motion of the welding torch (speed and path). Second,

welding energy is input to the base material to form the remainder of the heat affected zone. The

Goldak (et. al.) double ellipsoidal heat source [7] is used to map the welding energy into the base

metal.

Sophisticated material models are available for both hexahedron continuum elements and shell

elements. Virtually all of the mechanical and thermal properties of the material may be input as a function of temperature. Phase changes are easily considered, so that melting and solidification are treated seamlessly in the analysis. To increase the flexibility of the models, information is

input in a piece-wise linear fashion (tabular).

Proper time scale treatment is critical for application of explicit methods to welding simulations.

The time scale differentials between the thermal and mechanical solutions are overcome using

sub-cycling. There are several mechanical time steps for every thermal time step. Mass scaling

is used to manage the mechanical solution time scale. This technique is common in metal

stamping simulations for instance [8]. This technique takes advantage of the fact that the

welding operation is a quasi-static process. Material velocities are vastly smaller than the sound

speed of the material (on which the stable time step size is based). By artificially manipulating

the masses, and thus the stable time step size, the time scale may be manipulated. This may only

be reliably taken to the limit that material velocities remain at least a factor of 10 less than the

artificial sound speed of the material. A check of this condition is made throughout the solution

to insure compliance. This technique allows the character of the solution to be preserved while

promoting computational economy in the solution.

These techniques form the core of the method. The method has been successfiilly applied to

large plate structures, two of which are detailed in the following section.

RESULTS AND DISCUSSION

The explicit finite-element analysis methodology in EFFECT has been used to simulate the

welding induced distortions in large plate structures. Two examples are presented in this paper.

The first example involves out of plane warping, and the second example involves large scale

buckling.

The out of plane warping of a steel plate structure with a welded tee joint (base plate

20"x20"x0.5") was analyzed with a full three-dimensional EFFECT analysis. A schematic of the

213
process is shown in Figure 1, and a photograph of the experimental setup is shown in Figure 2.

A tee joint is welded on both sides simultaneously using a GMAW process. Temperature

measurements were taken with thermocouples, and displacement measurements were taken with

LVDTs. A comparison between measured temperature time histories and calculated temperature

time histories are shown in Figure 3. The calculation is shown to accurately predict temperature

profiles for this experiment. A comparison between measured displacement time histories and

calculated displacement time histories are shown in Figure 4. The calculation accurately predicts

the out of plane warping of the base plate. This calculation was run on a Pentium III Intel

processor operating at a clock speed of 800 Mhz in an overnight run, satisfying the

computational performance goal.

The second example considers the bucking of a very large steel plate structure (8ft.x20ft.x3/16")

induced by welding three tee stiffeners using a GMAW process. Photographs of the

experimental setup are shown in Figures 5 and 6. The stiffeners are welded on both sides, and

they are welded in succession. The welded structure suffers extensive buckling along the free

long edges as evidenced in Figure 7. A ftiU three-dimensional EFFECT analysis was performed

on this configuration. A rendering of the result is shown in Figure 8. Severe buckling is clearly

evident in the calculation results. The method was successftil in predicting the onset and

evolution of buckling. This calculation was also run on a Pentium III Intel processor operating at a clock speed of 800 Mhz in a run time of approximately 24 hours. This is larger than the

performance goal. In is anticipated that future performance gains (hardware and software) will

result in meeting computational performance goals for this problem.

SUMMARY AND CONCLUSIONS

An explicit finite-element based method has been developed to simulate the welding induced

distortions in large plate structures. A computer code called EFFECT has been written that

incorporates those methodologies. The goal of the effort was to produce a computational scheme

that is computationally efficient enough to approach this class of problems on existing computer

platforms. A goal of overnight execution times was established. Two example problems are presented which demonstrate the successful application of the methodology.

ACKNOWLEDGEMENTS

The author of this paper would like to acknowledge the support of the United States Navy and

the Naval Joining Center,

REFERENCES

1 . Brown, S ., Song, H. , Finite Element Simulation of Welding ofLarge Structures,

Journal of Engineering for Industry, 1992, Vol. 1 14, p. 441-451.

2. LS-DYNA Keyword User 's Manual, Version 960, Livermore Software Technology

Corporation, Livermore, California, March, 2001.

214
3. Hallquist, J. O., LS-DYNA Theoretical Manual, Livermore Software Technology

Corporation, Livermore, California, May, 1998.

4. Belytschko, T., Lin, J. I., Tsay, C. S., Explicit Algorithmsfor the Nonlinear Dynamics

ofShells, Computer Methods in Applied Mechanics and Engineering, Num. 42,

1984, p. 225-251.

5. Flanagan, D. P., Belytschko, A Uniform Strain Hexahedron and Quadrilateral With

Orthogonal Hourglass Control, International Journal for Numerical Methods in

Engineering, Vol. 17, 1981, p. 679-706.

6. Liu, W. K., Belytschko, T., Efficient Linear and Nonlinear Heat Conduction With a

Quarilateral Element, International Journal for Numerical Methods in Engineering,

Vol. 20, 1984, p. 931-948.

7. Goldak, J., Chakravarti, A., Bibby, M., A New Finite Element Modelfor Welding

Heat Sources, Metallurgical Transactions B, Vol. 15B, June, 1984, p. 299-305.

8. Jung, D. W., Study ofDynamic Explicit Analysis in Sheet Metal Forming Processes

Using Faster Punch Velocity and Mass Scaling Scheme, Journal of Materials

Engineering and Performance, Vol. 7, No. 4, August, 1998, p. 479-490.
Figure 2 Image of Welded Tee Joint Experiment

216
800

Q I 1 I 1 i i I I I 0 200 400 600 800

Time (seconds)

Figure 3 Temperature Time Histories

217
0.15

5

i

o.

IS b

0.06

-0.05

200

-* Data

hCalc. —K Caic. *—sitCalc. -

- Plat© 1 . LVDT 1 - Plate 1 . LVDT 2

- Plat© 1 , LVDT 3

- Plat© 2. LVDT 1 - Plate 2. LVDT 2

- Plate 2. LVDT 3 - LVDT 1 - LVDT 2

LVDT 3

400

Time (seconds)

600 800

Figure 4 Displacement Time Histories (Up is Positive)

218
Figure 5 Photograph of 8'x 20'x 3/16" Plate Experiment

219
Figure 6 Photograph of 8' x 20' x 3/16" Plate Experiment

Figure 7 Photograph of 8' x 20' x 3/16" Plate - Severe Buckling

220
Figure 8 Rendering of Calculation Results. Buckling is Clearly Evident

221

COMPUTATIONAL MODELING OF RESIDUAL STRESS IN WELDS

S. Yushanov* and K. C. Koppenhoefer*

ABSTRACT

Accurate prediction of stresses, strains, and residual stresses that are generated as a result of

welding is important for a number of practical issues. Hydrogen-induced cracking, stresscorrosion cracking, distortion, as well as fatigue strength of welded structures can all be affected

significantly by the residual stresses that are generated around welds. Due to the complexity

involved in the experimental measurement of residual stresses, numerical simulation procedures

are increasingly used for estimating the residual stresses that arise from welding. Commercially

available simulation software readily allows one to account for the non-linearities due to

variation of material properties and heat transfer coefficients with temperature. However, the

material constitutive models available in most commercial packages do not account for some of

the unique features associated with the welding process, e.g., material melting/remelting as

different weld passes are deposited, solid phase transformation effects, etc.

These features of the welding process influence the prediction of residual stress and distortion.

However, the open literature does not include a systematic study of these factors. The work

reported here uses a multiple-pass butt weld in a cylindrical pipe to study in detail the effects of

advanced material modeling methods on residual stress. Deposition of material during the

welding is carried out in multiple weld passes. At the melting temperature, all accumulated

elastic and plastic strains are set to zero. The interaction of this strain zeroing with the isotropic

and kinematic hardening is examined in terms of the effect on the distribution of residual stress

and welding-induced distortion.

KEYWORDS

Residual stress, kinematic hardening, isotropic hardening, strain accumulation, welding, finiteelement analysis.

INTRODUCTION

Accurate modeling of the material response to welding represents a significant challenge in

welding simulations ([1] and [2]). The large temperature change associated with welding

generates a complex material response. These temperature changes produce difficulties in both

the thermal and mechanical analysis in a welding simulation. However, the material response in

a mechanical analysis represents a greater challenge than the thermal response to the numerical

analyst. The mechanisms of plastic deformation and evolution of the yield surface during

welding continues to be a point of discussion among organizations conducting welding

simulations.

To determine the influence of different techniques for modeling plastic deformation during

welding, the International Institute of Welding (IIW) conducted a round robin of residual stress

' Edison Welding Institute, 1250 Arthur E. Adams Drive, Columbus, OH 43221

223
prediction. Eleven well-recognized organizations conducted analyses of a butt girth weld in a

pipe. The IIW provided each participant with the finite-element mesh, the net heat input of

welding, and the temperature-dependant material properties. The residual stress predictions from

these 1 1 organizations contained significant scatter [3]. A detailed review of the analysis

techniques used by the participants suggests the plasticity routines generated the majority of the

scatter. The work reported here examines the effect of two critical components of welding

simulation, namely the effects of the hardening model (isotropic versus kinematic) and zeroing

the plastic strain during material melting.

COMPUTATIONAL MODELS

The analyses conducted here utilize an axisymmetric idealization of a pipe girth butt weld. The

finite-element mesh used in the IIW round robin forms the basis for all analyses conducted in

this work. The mesh in the weld region contains significant refinement with a coarsening as the

mesh moves away from the weld (see Figure 1).

The analyses consist of a sequentially coupled technique where the thermal analysis is conducted

first. All analysis used the commercial code ABAQUS. A double-ellipsoid function with a

Gaussian distribution of heat flux [4] models the arc heating effects during welding. User

routine DFLUX is utilized to implement a moving body heat input due to welding. Surface

convection is applied at the inner and outer surfaces of the pipe. Radiation heat transfer effects are not accounted for in this analysis. The initial, and interpass, temperature of the model was

room temperature.

Each weld pass is modeled discretely using an element-rebirth technique. In this technique, the

element sets representing each weld pass are generated along with the finite-element analysis

meshes for the base material, see Figure 1 . During analysis, elements associated with higher

numbered passes are first removed and then reactivated at the beginning of the appropriate weld

pass.

When a group of weld pass elements is activated, specified initial temperatures are imposed for

all nodes associated with the weld pass elements. In reality, rebirth of the weld pass elements

occurs at the melting temperature. However, numerical studies indicate [5] that predicted

residual stress distributions do not seem sensitive to prescribed initial conditions for the weld

metal. Thus, rebirth of the higher pass elements occurs at room temperature in the present

analysis.

The material is assumed to follow the von Mises yield criterion and associated flow rules.

Linear isotropic and kinematic hardening models were analyzed. Strain rate effects and phase

transformation effects are not considered in the present analysis. Temperature-dependent

thermal-physical properties used in thermal analysis and material properties for stress analysis

are taken from Reference [3]. These analyses utilize a strain resetting technique, included into

ABAQUS via a UMAT that eliminates all accumulated plastic strains for material once it exceeds the solidus temperature.

224
RESULTS AND DISCUSSION

The first set of results presented in this section show the evolution of different variables at Point

Ai located within the weld filler metal that are deposited during Pass 1 (see Figure 1). This point

experiences melting during Pass 1 and re-melting during Pass 2, see Figure 2. As indicated

previously, the solidus temperature for this material equals 1455°F. Thus, Point Ai remains in

the molten state over 0.5 sec <f< 4sec for Pass 1, as indicated by the region between the dashed

lines in Figure 2(a). Point Ai becomes molten again for approximately 5 sec

(5, 01 2 sec </< 5,017sec) during Pass 2, as shown by the dashed lines in Figure 2(b).

Figure 3 presents the transient equivalent plastic strain at Point Ai. Each figure compares an

analysis where the strains accumulated through the entire welding cycle (dashed line) with an

analysis for which the strains are reset (solid line) at all points where the temperature exceeded

the solidus temperature of the material. The resetting algorithm effectively reduces the

equivalent plastic strain for Point Ai to zero while the material melts and re-melts. Thus, the

analyses that do not include resetting accumulate significantly more plastic strain than the

analyses with resetting. The plastic strains presented in Figure 3 appear independent of the

hardening model (isotropic or kinematic).

Figure 4 shows the variation of the plastic strain in the hoop direction, f , as a function of time

at Point Ai. The resetting algorithm influences this plastic strain component in a similar manner

as the equivalent plastic strain. However, the examination of permits a physical

interpretation of the results to determine the applicability of the resetting algorithm. Introducing

the resetting algorithm changes the direction of the hoop plastic strain from compression to

tension. The tensile plastic strain represents a more physically reasonable result. The weld

metal must contract on cooling after each pass. Thus, the weld metal should experience a tensile

strain. As with the equivalent plastic strain, the strains shown in Figure 4 appear independent of

the hardening model.

The hoop stress computed from these strains using an isotropic hardening model appear in Figure

5 (all stresses are normalized by the room-temperature yield strength, (Tq). As Pass 1 heats the

material, a large compressive stress initially develops ( cr/ cTq = -1 . 1). Additional heating during

Pass 1 decreases the stress to near zero due to the decrease in yield strength and Young's

modulus of the material. After the material solidifies, the hoop stress becomes tensile and attains

a value of (7 / cTq = 1 .5 at the end of Pass 1 . The evolution of stress at Point Ai for Pass 2 appears

in Figure 5(b). During Pass 2, Point Ai experiences re-heating, re-melting, and cooling. Reheating drives the hoop stress from a tensile value ofct/cTo = 1.5 to a maximum compressive

value of (7/ CTq = -0.7 before the material loses strength and stiffness. In the re-melting region,

the hoop stress at Point Ai approaches zero. Cooling of the material generates a large tensile

stress (cr/(To =1.2).

A direct comparison of the analyses with and without the resetting algorithm indicates strain

resetting does not influence the residual stress at any point during the analyses, for the case of

225
isotropic hardening shown in Figure 5. Resetting the strains does not influence the residual

stress due to the lack of influence of these strains on the yield surface, as shown in Figure 6. By

definition, the yield surface may not shift for isotropic hardening. Thus, the yield surface for

isotropic hardening remains unchanged during the resetting of the strains. This unchanged yield

surface produces a constant residual stress since the stress state must remain on the yield surface

during plastic flow.

Figure 7 shows the variation of hoop stress with time for the kinematic hardening case. Figure

7(b) shows the dramatic influence of strain resetting on residual stress prediction. For kinematic

hardening, the yield surface radius does not change with plastic flow. Instead, the yield surface

translates. Figure 8 shows the evolution of the back stress for Passes 1 and 2. For the case of

strain resetting, the yield surface origin returns to the coordinate origin due to an elimination of

the back stress. This modification of the yield surface origin produces the difference in hoop

stress observed in Figure 7.

Figure 9 summarizes the effect of strain resetting on the hoop stress for both isotropic and

kinematic hardening models. Figure 9(a) shows there is no influence of strain resetting on the

hoop residual stress over the entire welding cycle for isotropic hardening. The stresses at Point

Ai attain a value of approximately 1.5 times the yield stress early in the welding and maintain

that value (except in the transients during welding). Figure 9(b) shows the strong influence of

strain resetting over time. For Pass 1, the residual stress attains a value of approximately 2.0

times the yield stress. However, the addition of new material reduces the residual stress to yield

level at the completion of the weld.

Figure 10 shows the final hoop and axial residual stresses along the weld centerline for all cases. The kinematic hardening results show the influence of the strain resetting technique used here.

The isotropic case and kinematic case with resetting show the closest agreement for the hoop

stress. All three cases show reasonable agreement for the axial stress case.

In additional to residual stress, welding simulations may provide predictions of distortion.

Figure 1 1 shows the axial and radial displacement along the internal diameter of the pipe for all cases considered. For both plots, the origin of the x axis occurs at the symmetry plane of the

weld. Strain resetting does not influence the residual stress for analyses using isotropic

hardening. However, the axial and radial displacements show an effect of strain resetting. The

effect of resetting strains appears much greater for kinematic hardening. Without strain

resetting, isotropic and kinematic hardening provide dramatically different displacements.

However, resetting the strains drives the two analyses towards each other.

SUMMARY AND CONCLUSIONS

This work examines the effect of strain resetting during melting in welding simulations. The

resetting procedure does not influence the residual stress prediction for isotropic hardening. The

resetting procedure does not alter the yield surface radius. Thus, the residual stress predicted

from the simulation does not change. For kinematic hardening, the modification of the back

stress produces a strong effect on the residual stress. Strain resetting does alter the predicted

226
welding distortion. By resetting the strain, the difference in distortion predictions for isotropic

and kinematic hardening decreases significantly.

ACKNOWLEDGEMENTS

The authors of this paper would like to acknowledge the help and support of the members of the

IIW Commission X-XV Joint Working Group on Residual Stress and Distortion Prediction and

the organizations who participated in Phase 1 of the Round Robin; TWI, EWI, British Energy,

British Steel, Paton Institute, Technical University - Braunschweig, Technical University - Graz,

SAQ Kontroll AB, Institut of Applied Mechanics - BRNO, Institut de Soudure, and the Ohio

State University. In additional, the authors would like to acknowledge Professor R. H. Dodds for

his many useful discussions.

REFERENCES

1 . Goldak, J. Modeling thermal stresses and distortions in welds, in Proceedings ofthe 2nd

International Conference on Trends in Welding Research. 1989.

2. Lindgren, L.-E. The Use ofSimulations and the Need ofExperiments in Material

Processing, in The sixth Cairo International MDP Conference. 1996.

3. Koppenhoefer, K.C., Gordon, J.R., IIWRound Robin on Residual Stress and Distortion:

Phase 1 Results. 2000, IIW.

4. Goldak, J.A., Chakravarti, A., and Bibby, M.J, A New Finite Element Modelfor Welding

//ear 5owrce5. Trans. AIME, 1984. 15B: p. 299-305.

5. Hong, J.K., Tsai, C.-L., and Dong, P.„ Assessment ofNumerical Proceduresfor Residual

Stress Analysis ofMultipass Welds. Welding Journal, 1988: p. 372s-382s.

227
19 mm

3.18 mm

Figure 1. Weld joint geometry and finite-element model with Point Ai specified.

5010 5020

Time, sec

Figure 2. Variation of temperature with time for Point Aj during (a) Pass 1, and (b) Pass 2.

5030

I 0.12

o

I 0.08

c

5 0.04

3

LU

0.00

(a) w/o resetting

with resetting

-I

'—'—'—

2 4

Time, sec 6

w/o resetting

with resetting

5010 5020 5030

Time, sec

Figure 3. Variation of equivalent plastic strain, , with time for Point during (a) Pass 1, and (b) Pass 2. (All analyses used an isotropic hardening model.)

228
0.03

w/o resetting with resetting

Time, sec

—r-6

0.03

0.02

a> to^ 0.01

I

0.00

i -0.01

°" -0.02

-0.03

1 '

(b) w/o resetting

witti resetting

\

V

5010 5020

Time, sec 5030

Figure 4. Variation of plastic strain in hoop direction, €gg , witli time for Point during (a) Pass 1, and (b)

Pass 2.

w/o resetting with resetting

Time, sec

6

oD

1.2

0.8

w 0.4

?n 0.0

2

CO

Q.

8-0.4

-0.8

-1.2

w/o resetting with resetting

5010 5020

Time, sec

5030

Figure 5. Variation of hoop stress, (7gg , with time for Point A/ during (a) Pass 1, and (b) Pass 2. (Isotropic

hardening.)

w/o resetting with resetting

5010 5020

Time, sec

Figure 6. Variation of the radius of the yield surface with time for Point Aj during (a) Pass 1, and (b) Pass 2. (Isotropic hardening.)

229
Figure 7. Variation of lioop stress, Gqq , with time for Point during (a) Pass 1, and (b) Pass 2. (Kinematic

tiardening.)

COo

(0 CD

Q.O

oX

0.6

0.4 -

0.2 -

0.0

-0.2

(a)

0

w/o resetting with resetting

Time, sec

6 5010 5020 5030

Time, sec

Figure 8. Variation of tlie back stress witli time for Point during (a) Pass 1, and (b) Pass 2. (Kinematic

liardening.)

2.0

d=1.5

0)

v>£ 0.5

W

^0.0

o

==-0.5

-1.0 -

1

, , , , (a)

! f

:

5x103 10x103

Time, sec

2.0

o 1.5

D

I

0.5 ^ ^ 0.0 - Q.O^ -0.5 -

-1.0 -

^

w/o resetting with resetting

(b)

15x1 —I———

—I—

5x103 10x103

Time, sec

Figure 9. Variation of Iioop stress, Cgg , with time for Point during three passes for (a) isotropic

hardening resetting, (b) Idnematic hardening without resetting strains, (c) kinematic hardening with

resetting strains.

15x1(

230
0.0 0.5

Radial Coordinate, x/fi

1.0 0.0 0.5

Radial Distance, r/h

1.0

Figure 10. Variation of stress at centerline of weld for isotropic and kinematic liardening witli and without

strain resetting: (a) axial stress, (b) hoop stress.

Isotr., w/o resetting

Isotr., with resetting

kinem., w/o resetting

kinem., wltti resetting0.0 -

-0.2 -

-0.4

''''!'''

(b)

'!''' !''''!' '

isotr., w/o resetting

isotr. , with resetting

kinem., w/o resetting

........ ......

kinem., with resetting

1 .... 1 ... .

0 12 3 4

Axial Distance, z/h Axial Distance, z/h

Figure 11. Variation of (a) axial and (b) radial displacement along internal diameter of pipe. (Origin of

abscissa equals the centerline of the weld.)

231
I
A STUDY ON THE PREDICTION OF

WELDING DISTORTIONS OF HULL IN SHIPBUILDING (I)

S.B. Shin*, J.G. Youn*

ABSTRACT

In order to evaluate principal factors controlling welding distortions in particular angular

distortion, both dimensional analysis and Finite Element Analysis (PEA) have been performed.

The principal factors were found to be the heat intensity, rigidity and restraint, which were

verified by experimental results. Angular distortion both at the fillet and at the butt weldment of

hull structure can be predicted by an equation expressed with function of heat intensity, bending

rigidity and internal restraint.

KEYWORDS

Angular Distortion, Heat Intensity, Bending Rigidity, Restraint, Dimensional Analysis and Finite

Element Analysis (FEA)

INTRODUCTION

Welding process has been widely used in fabricating steel structures such as ships and pressure

vessels. However, some inherent problems of the welding still need to be solved (Ref.1-3). A

major problem is welding distortion. This is attributed to incompatible strains over the welded

joint, which is developed by uneven temperature distribution during/after welding. Welding

distortions adversely affect the service behavior of welded structure including static and dynamic

stability and buckling characteristics. In order to reduce and prevent distortions, lots of

researches have been performed but welding distortion for actual structures has not yet been

controlled. This may be associated to the fact that the previous studies have been limited

generally in a laboratory scale with simple welding variables. Welding distortions at actual

structures like ships depend not only on welding process and conditions, but also on internal and

external restraint acting on the weldment. It is, therefore, necessary to establish proper

predictive and control methods of welding distortion for actual structures on the basis of these

factors, combined with material itself.

This study has been tried to identify the principal factors controlling welding distortion of the

hull structure, in particular angular distortion and transverse shrinkage both at the fillet and butt

weldment by the dimensional analysis and Finite Element Analysis (FEA). Comprehensive

experiments were also carried to verify the results of the dimensional analysis and FEA. Based

on these results, a predictive equation of welding distortion applicable to actual hull structures

has been proposed. In this study, results and discussion on the angular distortion are described

* Material Research Dept., Hyundai Heavy Industries Co. Ltd., Jeon Ha Dong, Dong Gu, Ulsan, South Korea

233
ANALYSIS AND EXPERIMENT PROEDURE

Dimensional analysis was performed to identify the principal factors controlling welding

distortion as given in below. If effects of material properties including jdeld strength and thermal

expansion coefficient were not taken into consideration because of their temperature dependence,

residual angular distortion of fillet and butt weldment could be expressed as shown in Eqn (1).

5, = f(Q, D„ W) = f([qMdJ\[wr

)

(1)

Where, Q is heat intensity and Db and W are bending rigidity and width of weldment

respectively. Here, equation (1) is rearranged as following equation by substituting the

dimensional formula.

[L]= [FL]"[FLr[L]'

(2)

The equation corresponding to the dimensional homogeneity is defines as follow:

F : a + b=0

L : a + b +c = 1 (3)

Solving Eqn (3), the following exponents are obtained

a=l b = -l c=l (4)

Substituting these values into Eqn (1), the dimensionless form of angular distortion is defined as following equation

(l> = —= f W (5)

As shown in Eqn (5), the angular distortion could be defined as a function of heat intensity (Q)

and bending (Db). It is, therefore, deduced that the principal factors controlling welding

distortion are the heat intensity and rigidity.

AnalyBla Zon«

Angular DivtQrtion)

(a) Fillet Weldment (b) Butt Weldment

Fig. 1 Schematic diagram of fillet and butt weldment

234
From the results of the dimensional analysis, the variables for FEA and experiment were selected

to establish a predictive equation for welding distortion for actual weldment as shown in Table 1

.

Fig. 1 shows the schematic configuration of the models for FEA and experiment. Welding

parameters used in this study were typical FCAW (Flux Cored Arc Welding) and SAW

(Submerged Arc Welding) conditions for actual fillet and butt weldment. In order to evaluate

the effect of internal restraint on the angular distortion, width of weldment is varied from 700mm

to 4000mm. External restraint condition at the weldment was simulated by attaching temporary

fixture to the lateral side of the weldment as shown in Fig. 2. The amount of external restraint is

defined as a function of bending or in-plane rigidity and restraint length (Ls). (Ref. 4, 5)

Table 1 Variables used For FEA and experiment

Base Metal

Heat Intensity

[cal/mm]

Flange [mm] Web [mm]

Thickness Width Length Thickness Height Length

A grade 224 - 522 12-25

500

- 4000

Infinite (FEA)

lOOO(EXP)

12-25 250

Infinite(FEA)

lOOO(EXP)

Fixture

a) Fillet Weldment (b) Butt Weldment

Fig. 2 Schematic diagram of external restraint of fillet and butt weldment

Finite Element Analysis

The transient temperature distribution was calculated based on the assumed quasi-stationary

condition. (Ref. 6) The volume heat source of welding arc was specified by a Gaussian

distribution. Heat loss at all surfaces of the solution domain was governed by natural convection.

Thermal properties of the material used depend on the temperature and an effective conductivity

may be assumed at the temperature above the melting point. To consider the effect of latent heat

at the phase change, the specific heat in the solidification range was modified to be very high.

(Ref 7)

Fig. 3 Mesh design of fillet and butt weldment used for FEA

235
Mesh design used for thermo-mechanical stress analysis consisted of 8-nodes plane element with

general plane strain condition and spring element as shown in Fig. 3. Spring elements attached

to bottom surface of fillet and butt weldment are to prevent reverse angular distortion during the

welding and cooling stage. (Ref 8) While thermo-mechanical strains along the longitudinal

direction of welding line were assumed to be uniform, free expansion and bending in the

transverse direction of welding line were allowed. When the temperature of elements reached

the liquidus temperature, the plastic strains accumulated by that time were assumed to be

relieved. In this region above the melting temperature, thermal strain increment was set to zero. Mechanical properties of weldment were postulated to behave as an isotropic, elasto-plastic and

strain-hardening continuum. Yielding of material was assumed to be governed by von-Mises

criteria.

RESULTS AND DISCUSSION

Verification of Principal Factors

Fig. 4 shows the variation of angular distortion at the fillet and butt weldment as a function of the

heat intensity (Q) and bending rigidity (Db). Angular distortion at the fillet and butt weldment

is strongly dependent on the function of heat intensity and bending rigidity. Good agreements

between calculated results and measured results for angular distortion are found. This verifies

that heat intensity (Q) and bending rigidity are the principal factors controlling the angular

distortion, which was already proposed from the dimensional analysis described in the previous

section.

0.00 5.00 10.00 li.OO 20.00 25.00 30.00 0.00 2.00 4.00 i.OO t.OO 10.00 12.00 f(Q/Db) f(Q/Db)

(a) Fillet weldment (b) Butt weldment

Fig. 4 Variation of the angular distortion at the fillet and butt weldment with the ratio of Q and

Db

Internal Restraint

Welding distortion of the actual welded structure is different from that of small size welding

specimen in terms of amount and deformed shape. Welding distortion of actual weldment

could be over-estimated by a predictive method established by the small size weldment. Fig. 5

236
(a) and (b) show the variation of the dimensionless parameters of angular distortion at the fillet

weldment (^^ ) and the butt weldment (^^ ) with reference to weight per unit weld length (Wul).

Here, dimensionless parameter was defined as the ratio of angular distortion according to

existence of self-weight effect. At the fillet weldment, ^^ decreases with an increase in Wul

but the reduction rate of ^^ is constant irregardless of the ratio of Q and Db. However, the

reduction rate of for the butt weldment strongly depends on the ratio of Q and Di as shown

in Fig. 5 (b). As the ratio of Q and Db increases, the reduction rate of increases.

a

4

"3

.SiJ

a

• f(Q/Db)=S.4

ll(Q/Db)=7.« ^ f(Q/Db)=12.6

0.80

1.20 f— # l(Q/Db)=10.9

f(Q/db)=10.S

l(Q/Db)=».70 A f(Q/Db)=8.10

0.60

a

.ss

4.00 8.00 12.00 16.00 Weight / Unit w«ld lengtli |l(gf/mim[10E-3]

(a) Fillet weldment

20.00 5.00 10.00 15.00 20.00 25.00 Weight/Unit WddUngth [Icgf/mm x lOE-3]

(b) Butt weldment

Fig. 5 Variation of dimensionless parameter of angular distortion at the fillet and butt weldment

with Wul

External Restraint

External constraint using a temporary fixture or jig was widely used for controlling welding

distortion. It is, therefore, important to understand how external constraint affects the welding

distortion at the weldment. Fig. 6 (a) and (b) show the variation of angular distortion at the

fillet and butt weldment with bending restraint intensity {Kg). In this figure, the fuction of heat

intensity (Q) and bending rigidity (Db) are 7.54 for the fillet weldment and 10.67 for the butt

weldment, respectively. Angular distortion of the fillet and butt weldment decreases with an

increase in bending restraint intensity. This may be resulted from the increase in the reaction

stress of the weldment owing to the increase of bending restraint intensity.

Variation of the angular distortion at the fillet and butt weldment subjected to a given external

restraint {K^ - 8640 kgf/rad) is shown in Fig. 7 (a) and (b) as a function of the ratio of Q and Db

and compared with that obtained under free condition. Angular distortion of the butt weldment

decreased by an external restraint regardless of the ratio of Q and Db. If bending restraint

intensity is sufficiently large, the angular distortion of the butt weldment could be

perfectly controled. For the fillet weldment, effect of external restraint on the angular distortion

strongly depends on the ratio of Q and Db. Suppression of the angular distortion at the fillet

weldment by an external restraint could be neglected when the ratio of Q and Db is smaller than 4.

This is attributed to the fact that the amount of elastic spring back decreases when the ratio of Q

237
and Db is smaller than the speicfied critical value depending on A",

Based on the above results, it is possible to propose a predictive equation of angular distortion

for actual steel structures as follow.

Q W

' D K (6)

25.00 5.00 1—

4.00 —

? i.no —

1o

V,5

i3 1

10000.0 20000.0 30000.0 40(90.0 Bending Restrain Intensity [ligfmm/racj50000.0

2.00

1.00

0.00

0.0 10000.0 20000.0 30000.0 40000.0 Bending Restraint Intensity [Icgfrnm/rad]50000.0

(a) Fillet weldment (b) Butt weldment

Fig. 6 Variation of the angular distortion at the fillet and butt weldment with

f(Q/Db) f(Q/Db)

(a) Fillet weldment (b) Butt weldment

Fig. 7 Variation of the angular distortion at the fillet and butt weldment with Q/Db

238
CONCLUSION

Dimensional analysis, FEA and experimental studies have been performed with reference to

various variables such as welding condition, rigidity and restraint to identify the principal factors

controlling welding distortion and to establish a predictive equation of welding distortion for

hulls. The main results obtained are summarized as follow.

1. Angular distortion at the fillet and butt weldment was defined as a function of heat intensity,

bending rigidity, and internal and external restraint condition.

2. Angular distortion decreased by effects of internal restraint. However, reduction rate of

angular distortion at the fillet weldment depends on restraint intensity alone, while that of

angular distortion is determined by internal restraint and ratio of heat intensity and bending

rigidity.

3. The control effect of external restraint (by jig) on the angular distortion increased as the

function of ratio of heat intensity and bending rigidity and bending restraint intensity

increased.

REFERENCES

1. N. R. N. Rao and L. Tall, 1961, "Residual stress in welded plates", Welding Journal, Vol.40,

p.468s-480s.

2. Rodgers and Fetcher, 1938, " Determination of internal stress from the temperature history to

a butt-welded plate". Welding Journal, Vol. 17, p.4-7s

3. Griffith and H. R. George, 1941, "Residual stress in butt-welded steel plates". Welding

Journal, Vol.20, 1941, p.410s-4 14s

4. Satoh K., Matsui S., 1967, "Reaction stress and weld cracking under hindered condition",

Technological Reports of the Osaka Univ., 1967

5. Satoh K., 1972, "An analytical approach to the problem of restraint intensity in slit weld", IIW,

Document X-66 1-72,

6. Yukio Ueda and Taketo Yamakawa, 1971, "Analysis of thermal elastic-plastic stress and

strain during welding by finite element analysis". Trans, of JWS, Vol. 2, No. 2, p.90s-98s

7. E. Friedman, 1975, " Thermo-mechanical analysis of the welding process using the finite

element method". Trans, of ASME, p.209s-213s

8. Satoh K., Terasaki T., 1976, "Effect of welding conditions on welding deformations in welded

structural materials". Trans, of JWS, Vol. 45, No. 4, p.54s-60s

239

NUMERICAL SIMULATION OF SHEAR STRESS DISTRIBUTION

IN THE FILLET OFAN AI-AI2O3 SOLDERING ASSEMBLY

X. Ma*,Y.Y.Qiaii*'

ABSTRACT

By means of finite element numerical simulation, the shear stress distribution in the AI-AI2O3

soldering assembly and the effect of the coefficient of thermal expansion of interlayer alloy and

the fillet geometry had been analyzed. The calculating results showed that, maximum level of

shear stress occurred at the soldering fillet and the interface between AbOsceramic and coated

Cu. Meanwhile, in order to get better shear stress distribution, the coefficient of thermal

expansion of interlayer alloy should match with the Al base metal and the optimized geometry of

soldering fillet is the concave shape with extruding length a little more than the gap height.

KEYWORDS

Finite element numerical simulation, AI-AI2O3 soldering, shear stress, coefficient of thermal

expansion, fillet geometry

INTRODUCTION

Metal-ceramic soldering assembly has been widely used in industrial applications [Ref 1-2]. As

an assembly with different materials joining, cooling stage after soldering will lead to thermal

stress in the assembly due to the mismatch of coefficients of thermal expansion (GTE) of

different base materials. Such thermal stress will further cause crack at the joining interface in

some cases and failure of the assembly [Ref 3-4]. With the consideration of the special geometry

of soldering seam, the mechanical response which is most affected is the shear stress distribution

in the soldering fillet [Ref 5]. Undoubtedly, the CTE of base materials and the geometry of

soldering fillet will play important roles on the shear stress distribution. Similar problem has

been investigated in reliability evaluation of surface mount solder joints used in microelectronic

packaging [Ref 6-7]. But few works had been done for metal-ceramic soldering assembly. In

this work, shear stress distribution characteristics in the soldering fillet of AI-AI2O3 assembly

caused by cooling stage has been studied by finite element numerical simulation, the effects of

the CTE of base materials and the geometry of the soldering fillet had been illustrated.

* National Key Laboratory of Reliability Physics of Electronic Product, CEPREI, Guangzhou 510610, P.R.China

* National Key Laboratory of Welding, Harbin Institute of Technology, Harbin 150001, P.R.China

241
FINITE ELEMENT MODEL

Figure 1 is the schematic of AI-AI2O3 soldering assembly. Cu with 0.2mm thickness was coated

on the surface of AI2O3 ceramic in order to improve the solderability. The interlayer filler is

Sn-based, low melting point (180-190°C) alloy with 0.1mm thickness. The corresponding

two-dimensional finite element model with 8 nodes isoparametric elements is shown in Figure 2.

Since it is axial symmetric, only half part of the assembly was modelled in this simulation.

Sn based alloys

21 H ^ AI2O3

I I I I I I I I I I I I I 1,1,1,1,1,1,1,1,1,1,1 J.I I I I • I I .I .

I

. I . r I j» Cu

Al

25

Figure 1 : Schematic of AI-AI2O3 soldering assembly

Figure 2: Two-dimensional finite element model ofAI-AI2O3 soldering assembly

Commercial finite element analysis software ANSYS5.6 was used in this work. All the materials

were assumed as elastic and the materials properties used in this calculation is listed in Table 1. Cooling stage is the only outer thermal loading, cooling temperature range is from 180°C to 20

°C since the melting point of interlayer alloy is rather low.

Table 1 Material parameters used in FEM calculation

Material E (MPa) V a(10-^K-^)

Al 70000 0.33 25.1

Sn-based alloy 43251 0.36 25.4

Cu 117000 0.34 16.7

AI2O3 300000 0.23 6.7

242
EFFECT OF SOLDERING FILLET SHAPE

From the point of view of geometry, the shape of soldering fillet can be divided into three types:

concave, 45° line style and convex. Their corresponding finite element models are shown in

figure 3.

The shear stress distribution in the above three types soldering fillets are shown in figures 4, 5

and 6, respectively. By comparing the shear stress level, it can be seen that convex fillet is the

worse shape because the maximum shear stress in the fillet is 50MPa more than concave shape

and 45° line style. As to the latter two fillet shapes, the shear stress level is similar, but the site of

maximum shear stress is a little different. The maximum shear stress occurs at the fillet centre for

concave shape while at the upper side of fillet for 45° line style. Furthermore, it can be seen from

the shear stress contours that stress concentration is more serious in 45° line style than concave

shape. Therefore, the mechanical response in the concave fillet shape can be considered as the

best.

(a) Concave fillet (b) 45° line style fillet (c) Convex fillet

Figure 3: Finite element models of different soldering fillets geometry

Figure 4: Shear stress distribution corresponding to concave soldering fillet (Unit: MPa)

243
EFFECT OF CTE OF INTERLAYER ALLOY

The thermal stress caused by cooling stage is due to the mismatch of the four materials in the

soldering assembly. There is the global mismatch between the AI2O3 ceramic and Al base metal,

and also the local mismatch between the adjacent layers. Usually, the properties of the materials

Figure 5: Shear stress distribution corresponding to 45° line style soldering fillet (Unit: MPa)

Figure 6: Shear stress distribution corresponding to convex soldering fillet (Unit: MPa)

to be joined are fixed, and we only can change the property of interlayer material by alloy design.

In this work, the effect of the CTE of interlayer alloy on the maximum shear stress in the

soldering fillet has been studied.

The soldering fillet shape used in this calculation is concave type as shown in figure 3(a). The

calculating results are shown in figure 7. The maximum value of CTE of interlayer alloy is

assumed to be more than Al and the minimum value less than AI2O3. The results showed that

there is minimum shear stress level in the soldering fillet when the CTE of interlayer alloy

244
matched with Al. This is to say, for the multilayer assembly with different materials, the CTE of

the interlayer alloy should match with the Al base metal which has the maximum CTE value. At

this time, the shear stress in the soldering fillet caused by cooling stage could be minimized.

Such results also indicate that the interlayer alloy (see Table 1) that we used in soldering

experiments is a good choice.

Q.

w

^—

CO

a> sz

<nE

E

-6„-1

Coefficient of thermal expansion /10 K

Figure 7: Effect of CTE of interlayer alloy on the maximum shear stress in the soldering

assembly

EFFECT OF GEOMETRIC PARAMETER OF CONCAVE FILLET

As to the concave fillet shape, the most important geometric parameter is the ratio of the fillet

extruding length L to the gap height H. Figure 4 (corresponding to figure 3(a)) shows the shear

stress distribution for the case of L/H=l . Let the gap height H invariable, figures 8 and 9 show

the shear stress distribution in the soldering fillet for the case L/H=0.5 and 1.5, respectively.

Using L/H=l as reference geometry, it can be seen that the maximum shear stress level and its

location change little when the fillet extruding length decreases. On the other hand, the

maximum shear stress level is reduced by lOMPa when the fillet extruding length increases,

meanwhile, its location changes from fillet center to the upper side.

245
Figure 8: Shear stress distribution corresponding to concave soldering fillet with L/H=0.5 (Unit:

MPa)

Figure 9: Shear stress distribution corresponding to concave soldering fillet with L/H=1.5 (Unit:

MPa)

CONCLUSIONS

Cooling stage after soldering will lead to thermal stress in the AI-AI2O3 assembly due to the

mismatch of CTE of different materials. The CTE of interlayer alloy and the fillet geometry play

important roles on the shear stress distribution. In order to minimize the shear stress level in the

soldering fillet, the CTE of interlayer alloy should match with the Al base metal, meanwhile, the

best fillet shape is the concave type with extruding length a little more than the gap height

246
REFERENCES

1. James, I. 1989. Challenge of bonding metals to ceramics. Machine Design 61(24): 95-100.

2. Ohmori, A.; Zhou, Z.; Suzumura, A.; and Arata, Y. 1993. Bonding of ceramics to metals with

activated coating layers made by plasma spraying. Materials and Manufacturing Processes

8(2): 183 to 201.

3. Qian, Z.; and Akisanya, A.R. 1998. An experimental investigation of failure initiation in

bonded joints. Acta Mater. 46: 4895 to 4904.

4. Reimanis, I.E.; Dalgleish, B.J,; and Evans, A.G. 1991. The fracture resistance of a model

metal/ceramic interface. Acta Metall. Mater. 39: 3133 to 3141.

5. Yang, Y.Y.; Munz, D.; and Schaller, W. 1997. Effect of the stress jump at the interface of a

joint on the failure behavior. International Journal of Fracture 87: LI 13 to LI 18.

6. Heinrich, S.M.; and Liedtke, RE. 1993. Effect of chip and geometry on solder joint formation

in SMT. ASME Journal of Electronic Packaging 115: 443 to 449.

7. Charles, H.K.; and Clatterbaugh, G.V. 1990. Solder joint reliability design implications from

finite element modeling and experimental testing. ASME Journal of Electronic Packaging 112:

135 to 146.

247

Session B2: Modeling II: Predicting Microstructure

and Performance

249

ADVANCES IN ALUMINUM WELD SIMULATIONS APPLYING WELDSIM

H. G. Fjaer^*\ O. R. Myhr^**\ S. Klokkehaug^**\ E. J. Holm^*^

ABSTRACT

This paper describes recent developments and applications of the advanced simulation model

WELDSIM. This model is applicable for welding of age hardening aluminum alloys, and

computes the evolution of temperatures, microstructure, residual stresses and distortions. The

model is today extensively applied in seeking adequate welding procedures in the fabrication of

welded automotive parts in aluminum, and it has become an attractive alternative to the

traditional procedure of trial and error based optimization of the welding parameters. In the

present work, results from weld simulations have been compared to corresponding measurements

during welding, and a very good agreement has been obtained. The model has also been applied

investigating the possibilities of minimizing the distortions in the welding of automotive parts.

The simulations have shown how the fixture design, the weld sequence as well as the welding

parameters, significantly affects the resulting weld distortions.

INTRODUCTION

Extruded aluminum profiles are to an increasing extent applied in automotive components like

space frames, engine cradles and windshield frames where weight saving is essential. Welding is

a key operation in the manufacturing of such parts, and robotic GMA welding is by far the most

commonly applied process for high volume production. A major problem associated with

welding is the thermally induced deformations caused by the intense non-homogeneous heating

and cooling of the material. These deformations are unavoidable in welding, but can usually be

minimized to an adequate level by proper selection of the welding parameters and the fixture

design. In order to obtain weld deformations within the geometrical tolerance limits, two

principally different approaches can be applied, as schematically outlined in Figure 1 . The loop

on the left hand side of the diagram (i.e. the "physical welding" loop) illustrates the traditional

trial and error based procedure applying welding experiments and a robotic welding unit. The

welding is followed by measurements of the resulting distortions and the corresponding

deviations from the nominal geometry. If the distortions are outside the tolerance limits, some

adjustments are done for the welding conditions, the fixture- or the geometric design as indicated

in the rectangular window of the figure before another component is welded. The welding is

followed by measurements of the resulting geometry, and this loop is repeated until a certain

combination yields weld distortions that are acceptable.

The loop on the right hand side (i.e. the "virtual welding" loop) utilizes a computer instead of a

welding cell, where the simulation results provide direct information on the positions the

distortions are outside the tolerance limits. It is easy to change the input data systematically in

order to analyze their individual effect on the resulting local and global distortions. A postInstitute for Energy Technology, Box 40, N-2027 Kjeller, Norway

Hydro Automotive Structures Raufoss, Box 15, 2831 Raufoss, Norway

251
processor tool can be applied to view animations of the time evolution of the thermally induced

deformations. Such illustrations of the process give valuable insight and increased understanding

of the process. This provides a basis for generating rules for a better design of both the fixture

and the welding sequence.

A complete replacement of real welding experiments by simulations in the fabrication of welded

components is not realistic in the foreseen future, but there is no doubt that the trend is

irreversible moving towards a more extensive utilization of computer simulations. The impetus

for this development is the substantial potential for saving both costs and time in the preproduction stage. This has been the motivation for developing the weld simulation model

WELDSIM (Ref 1-4), which provides a mathematical description of the relation between the

main welding parameters and the subsequent distortions and weld properties. The model has

been developed and refined to a stage where it can be applied for realistic simulations of welding

of complex aluminum structures and capture important effects of the geometric design and the

weld parameters, as well as material related variables like alloy composition and base metal

temper condition. Moreover, the last years significant developments on computer hardware has

made it possible to carry out simulations on an ordinary PC within an acceptable amount of time.

The WELDSIM model is today extensively applied in the pre-production phase in seeking

adequate welding procedures in fabrication of welded automotive parts in aluminum. The present

paper illustrates recent developments of the model. The accuracy of the simulations is illustrated

by comparisons between simulation results and measurements, and an example from a case study

on welding of an automotive component is presented at the end of the paper.

Physical welding Virtual welding

I

Yes

Figure 1 : Two different approaches for satisfying geometric tolerances in fabrication of welded

structures.

252
MODEL OUTLINE

WELDSIM consists of a thermal, a microstmcture and a mechanical sub-model that are connected as illustrated in Figure 2, and implemented in a finite element method (FEM) software

framework. The thermal field influences both the microstmcture evolution as well as the

evolution of the thermally induced stresses and strains. At the same time there is a link between

the microstmcture and the mechanical sub-model to account for the important effect of softening

of the heat affected zone due to reversion of hardening particles. This is taken into account by a

separate (intemal state) variable f/fo.f/fo '\s computed by the microstmcture model and transmitted

to the mechanical model as a field variable. There is no "back-coupling" from the mechanical

model to the microstmcture model.

Figure 2: The connection- and transition of data between the different sub-models of WELDSIM.

The Thermal Model

The thermal model is a fully 3D FEM solver with an implicit time integration scheme, where the

specific enthalpy is the basic unknown. In WELDSIM, the heat source is modeled as a traveling

double ellipsoid heat distribution function. In order to obtain realistic temperatures, the heat

distribution function has been calibrated against measurements of the weld pool geometry.

Moreover, the model has a correction algorithm for compensating numerically induced

deviations in the total heat input. If present, the addition of filler wire material is modeled by a continuous activation of the finite elements in the reinforcement regions when they are approached by the heat source. The dimensions of these regions must, however, be predefined.

The Microstmcture Model

Heat-treatable aluminum alloys obtain their main strengthening contribution from the

precipitation of a fine dispersion of small particles from a supersaturated solid solution. These

small particles are not resistant to the high temperatures imposed by the welding, and will partly or fully dissolve at short distances from the weld. This process is usually referred to as reversion,

and is the main reason for the HAZ softening taking place during welding of heat-treatable

aluminum alloys. In order to quantify this effect, the model considers the evolution of the

volume fraction /of the small hardening particles. The state of the precipitate stmcture can then

be described by the single normalized state variable f/fo, where fo refers to the initial volume

fraction of the particles before the welding starts. As shown in Ref l,^^can be calculated as:

253
/o

^

Here, « is a time exponent (<0.5), and t* the time taken for complete particle dissolution at a

given temperature. The corresponding local hardness HV is assumed to vary linearly with f/fo,

i.e.: /J HV-HV.nun (^\

Here /fFm/n corresponds to the hardness in the fully reverted state whereas HVmax represents the

original base material strength for the actual temper condition (e.g. artificially aged (T6) or

naturally aged (T4) material). For alloys within the 6xxx series (i.e. Al-Mg-Si alloys) in the T6

temper conditions, the hardening phase consists of needle-shaped /? "-particles, while in the T4

temper condition, the precipitates are the fully coherent and significantly smaller GP-zones. The

width of the HAZ depends both on the temperature distribution and on the thermal stability of

the hardening phase. Since the GP-zones revert at lower temperatures than the ft" particles, a

wider HAZ is obtained for an alloy in the T4 than in the T6 temper (Ref 4).

A recently developed precipitation model for aluminum alloys within the 6xxx series (Ref 5)

predicts both the density and the size distribution of the hardening precipitates, and correlates

this to the room temperature yield stress through established dislocation theory. This offers the

possibilities of predicting the microstructure throughout the different operation steps and thermal

treatments, including both the pre- and post-weld heat treatment. This more advanced

microstructure model will be implemented in the next version of WELDSIM.

The Mechanical Model

In WELDSIM, the metal is described as an isotropic and elastic-viscoplastic material according

to the following formula:

<y = F(T)G{f/f,,T)[H{<p,f/f, (e^ . (3)

Here the flow stress is expressed as a product of a solely temperature dependent function F, a

function G incorporating the effect of hardening particles, a function H representing the work

hardening and finally the viscoplastic strain rate . Eq. (3) requires elevated temperature data,

which are only to a limited extent given in the literature. Therefore, material characterization of

some relevant alloys and temper conditions has been carried out on a Gleeble' thermal simulator.

Samples were tested at different temperatures and strain rates. The effect of particles on the flow

stress was investigated by performing tests both on samples with- and without an initial

precipitate structure at a given temperature. In order to remove the particles before the testing, a

solution heat treatment was prescribed as part of the pre-defined thermal program. The flow

stress was generally seen to be strongly dependent on the temperature and the volume fraction of

precipitates, and less dependent on the work hardening and strain rate. For alloy or temper

conditions not covered by the Gleeble testing, the F and G functions in Eq. (3) are scaled by

^ Gleeble is a registered trademark of Dynamic Systems Inc.

254
assuming a linear relationship between the stress and the hardness, and by inserting reasonable

values for HVmin and HVmcK in Eq. (2).

The mechanical boundary conditions representing the fixture or clamping is an essential part of

the model. These can be imposed as a distributed force or spring stiffness. The boundary

conditions are allowed to vary with time, and the spring stiffness can be dependent on the

computed displacement. These boundary conditions can also be applied at the interface between

parts of the solution domain, making modeling of gaps or mechanical contact possible. In the

simulations, the interface boundary conditions are different in front of and behind the moving

heat source in order to account for the cohesion, i.e. the ability of the interface to withstand

tensile forces, depending on whether a prescribed threshold temperature is reached.

Applving the Model

CAD drawings are used as a basis for generating appropriate geometries for the FEM meshing.

Commercial pre-processors are applied for generating the mesh and a necessary set of labels for

the different volume or surface domains. These labels are then combined with data sets

representing the thermal, microstructure and mechanical properties of the actual alloy(s), as well

as the thermal and mechanical boundary conditions reflecting the fixture design. Data describing

the welding process and the welding sequence must be specified, as well as parameters

governing the time stepping and the numerical schemes.

Modeling the welding process involves solving time-dependent coupled non-linear problems

with a moving, localized heat source. Such computations require extensive CPU-times.

Generally, much larger time steps (typically a few tenths of a second) are applied for solving the

mechanical problem compared to the time steps of the order of milliseconds for the

thermal/microstructure problem. Output from WELDSIM includes the temperature evolution, the

microstructure and resulting material strength, as well as the residual stresses and the distortions

of the assembly.

COMPARISON BETWEEN MODEL SIMULATIONS AND MEASUREMENTS

A series of welding experiments were carried out at Hydro Automotive Structures Raufoss.

Robotic GMA welding was performed on extruded aluminum tubes, of the alloys 6063 and 6082.

The assembly, corresponding to a comer of a frame, is shown in Figure 3(a). Two welds were

deposited in order to join the two tubes, as illustrated in Figure 3(b). The starting point of the

welds was at the exterior side of the comer. The cross section of the extruded tubes was 40 by 60

mm, while three different values of the wall thickness were used, i.e. 3, 4 and 5mm. The welding

speed was 16.1 mm/s at the straight portions of the welds and 21 mm/s at the curved comers. The

voltage was about 21.5 V and the mean amperage was estimated to be 147 A.

The distortions were registered by 4 extensometers. An example of displacements recorded from

one welding experiments, applying 3mm thick 6082-T6 extrusions, is shown in Figure 4(a).

Compression of the extensometers corresponds to positive displacement values. Corresponding

computed displacements are shown in the right part of the figure. A very good agreement is seen. However, all measurements suffered from noise during welding, due to the AC power supply for

255
the welding arc. When the clamps are released (after 1200s in the experiments, after 500s in

simulations) the displacements develop similarly, but the elastic spring boundary condition

remaining at clamp a (conf Figure 3(a)) cause the computed displacements at extensometer

positions 1 and 2 to become zero.

O Clamping from above

(b)

On ,

40mm

R=10mm

Weld 1

^Off

Weld 2

Figure 3: (a) Top view of the experimental set-up showing locations of clamps and extensometer

positions, (b) The cross-section of the tubes and the trajectory of the weld path.

The measured displacements are affected both by rigid body translation and rotation. In fact,

only one distortional mode can be extracted from the measurements. This corresponds (closely)

to the tilt angle 0 illustrated in Figure 5(a). Also of interest is the bending of the extrusion tube

A, represented by the angle q>. Figure 5(b) shows final computed distortions, magnified by a

factor of 20. The tilt angle is clearly larger than would be expected from an orthogonal

attachment of tube B on a bent tube A. Hence plastic deformation must have been induced at the

weld joint interface due to the welding.

A comparison between the calculated and measured permanent tilt angels is shown in Figure 6.

A very good agreement is found for the 3mm thick tubes of the 6082 T6 alloy. In the case

referred to as "strongly clamped", an extra clamp acting horizontally was applied on tube B. This

clamping is clearly seen to reduce the tilt angle. The deformations resulting from varying the

tube thickness for the 6063 alloy, as shown in Figure 6(b), also reveals a reasonable agreement

between calculated and measured values. These results clearly show that the distortions are reduced as the profile thickness increase due to a corresponding increase in the profile stiffness.

256
(a)

200 400 600 800

Time (s)

1000 1200 1400

(b)

100 200 300

Time (sec)

400 500

Figure 4: (a) Measured and (b) computed displacements during welding and subsequent cooling

of 3mm thick extruded tubes of the alloy 6082-T6.

0

Tube B

(a)

Tube A

(b) Slap

Figure 5: (a) Illustration of the tilt angle 6 and the bending angle (p. (b) Computed final

distortions magnified by a factor of 20.

257
(a)

(b)

0.012

cI 0.008

2,

o" 0.006

O)i 0.004

0.002

0

0.012

« 0.01 c% 0.008

2^ 0.006

o>

ro 0.004

0.002

0

^ Measured

B Computed

Normally clam ped Strong ly clamped

^ Measured

Computed

3mm 4mm 5mm

Figure 6: Comparison of computed and measured tilt angles, (a) Effect of different clamping for

3mm 6082 T6 alloy tubes, (b) Effect of varying wall thickness of 6063 T6 alloy tubes.

The error bars indicate the range of measured values.

SIMULATING THE WELDING OF AUTOMOTIVE PARTS

A detail from an actual windshield frame, made from extruded profiles of the aluminum alloy

AA6082 in 16 temper conditions, has been subject for a series of simulations. Only a minor part

of the frame, containing an upper comer was included in the solution domain, as illustrated

schematically in Figure 7. The actual CAD geometry of this comer contained numerous finer

details, making it unsuitable as basis for meshing with solid brick elements. Instead, a finite

element mesh was constmcted on a simplified geometry. This geometry deviated somewhat from

the actual shape, and no reinforcement regions were included. It was nevertheless considered to

be an adequate basis for the simulations.

Figure 7: Schematically drawn figure illustrating the windshield frame including the solution

domain for the simulations located at the upper right comer.

258
In these simulations, a constant welding speed of 3.8nim/sec and a net heat input of 1250W were

generally assumed. The welding sequence 1 depicted in Figure 8 was applied in most of the

simulations. The welding was completed after about 85 seconds. The clamping was released 60

seconds later, and the simulations were stopped after 400 seconds. The two end boundaries of the

solution domain, shown in Figure 8, correspond approximately to the nearest clamping positions.

The effect of the clamping was simulated by use of linear spring boundary conditions at these

boundaries. Generally, a rather rigid clamping was imposed. A displacement of 0.1mm resulted

in stresses of lOMPa and IMPa in the normal and transversal directions, respectively. An initial

gap between the parts of 0.5mm was assumed. This was achieved by imposing mechanical

boundary conditions entailing an initial offset displacement.

Figure 8: Welding sequence 1 superimposed on the solution domain containing the finite element

mesh. At the end boundaries, indicated by arrows, the boundary conditions represent

the clamping.

In the following the results from the simulations listed in Table 1 will be discussed. Two

alternative welding sequences that have been tested in the simulations are illustrated in Figure 9.

Table 1 : The simulation cases for the windshield frame comer. For the cases B-F the difference

from case A is described.

Case Description

A The reference case with welding sequence 1 B Welding heat source 2mm offset into the header

C Heat input increased by 25%

D Boundary condition stiffness reduced by a factor of 100 at A-pillar cross section

E Welding sequence 2

F Welding sequence 3

Figure 9: (a) Welding sequence 2 applied in case E. (b) Welding sequence 3 applied in case F.

259
In order to simplify the interpretation of the computed displacements, the displacements have to

be transformed into a few quantities representing the particular distortions of interest, i.e. the inplane tilt angle 0 and the out-of-plane torsional distortion angle (j) as illustrated in Figure 10.

The thermally induced strains in the weld joint zone also entail longitudinal contractions of the

extruded profiles. However, due to the size of the windshield frame, the longitudinal contractions

are assumed to have less significance than the angular distortions. The illustrated distortions

appear as the result of imposing zero displacement at the end boundary of the header and

releasing the corresponding end boundary of the A-pillar. On the other hand, if the A-pillar is

kept fixed, the angle (j) represents a rotation of the header. As the header actually has a

pronounced curvature, this rotation would entail a considerable displacement at a distance from

this windshield frame comer. The computed angular distortions are shown in Figure 10. As a

windshield frame has a typical dimension of Im, some results indicate displacements of several

mm. (Angle values in radians multiplied by 1000 gives distortions expressed in mm/m.)

0.014

0.012

0.01 ^ 0.008

c 0.006

.1 0.004

I 0.002

Q

0

-0.002

@ Tilt angle

E] Torsional angle

m. B

Simulation case

Figure 1 1 : Computed values of the distortion angles at tiie end of the simulations.

260
In case B, where the heat input was given an offset of 2mm into the header, the tilt angle is seen

to be about 1 0% larger than in case A. This is associated with the larger computed plastic strains

at the lower side of the header, compressive in the profile's longitudinal direction. The

displacement of the heat source also resulted in a lack of fusion at parts of the weld seam. This is

probably the major cause for the low values of the predicted torsional distortion. The results from

case C show that the distortions are sensitive to the total amount of welding heat input. The

relative increase in the torsional distortion is even larger than the relative increase of heat.

Notable, the more significant compliance of the clamps in case D nearly eliminated the predicted

distortions. This strong effect of the clamping forces therefore represents an interesting

possibility of manipulating the weld distortions, and the underlying mechanism behind this effect

will be explained in the following. In Figure 12 the computed angular distortions as function of

time is shown for cases A, D and F.

0.01

^

0.005

(/) c

(0 X3 0

g

c -0.005

(0 p -0.01

-0.015

0.01

I

0.005

c^ -0.005

COc

o

g -0.01

I-

-0.015

Case A Case F

Case D

100 200 300

Time (sec)

400

Case A

V Case

Case D

0 100 200 300 400

Time (sec)

Figure 12: Computed angular distortions as function of time for case A, D and F.

The lower stiffness of the clamps assumed in case D, compared to case A and case F, allows for

much larger distortions during welding. In particular, after about 5 1 seconds, a rapid evolution of

the tilt angle distortion takes place. This can be associated with the heating/expansion resulting

261
from the onset of weld segment 4, whereas the region close to weld segment 3 undergoes

cooling, as illustrated in Figure 13(a). This compensates for the subsequent distortion caused by

the weld segment 4, involving a transversal weld across the header. Considering the torsional

distortion, it seems important to allow for a large (negative) angle at the time the welding of the

last segment starts. This is the situation depicted in Figure 13(b). The cooling after welding of

this segment will induce considerable thermal contractions at the regions at the right part of this

figure, corresponding to the computed development between 80 and 100 seconds of the torsional

distortion angle seen in Figure 12.

The results from cases E and F illustrate clearly that the welding sequence may have a

pronounced influence on the distortions. Use of welding sequence 2 (case E) resulted in a

significantly reduced tilt angle compared to case A. The bending of the header induced by the

first weld segment is seen to be "absorbed by" the gap between the parts. However, the torsional

angle is predicted to be very large in case E, and therefore an improved welding sequence was

looked for by performing a series of simulations. After a few trials, the welding sequence 3

applied in case F was found to be optimal with respect to both kinds of angular distortions. The

splitting of the first weld into two segments (compared to sequence 2) was essential in order to

obtain a uniform gap distance prior to local fusion along these weld segments. This, in turn,

prevented torsional distortion.

Figure 13: (a) Fringe plot of heating/cooling rate after 51 seconds, (b) Temperatures after 69

seconds (right) from case D. Distortions are magnified by a factor of 10.

In general, it is possible to compensate for the resulting weld distortions by mounting the parts in

the fixture in an offset position, unless the welded parts form a closed loop. The exact magnitude

of the offset positions can be determined from practical welding experiments, or preferably, by

weld simulations provided that the latter are sufficient accurate. Canceling the distortions, by

applying weak clamping or by applying an optimal welding sequence, can be an attractive

alternative. Generally, it is considered as advantageous to apply a welding sequence, which

implies that the distortions at the interface (including generation or closure of gaps) can

compensate for the welding induced deformations of each of the welded parts.

262
SUMMARY

In this article a process model (WELDSIM) for welding of age-hardening aluminum alloys has

been outlined. The model consists of a thermal, a microstructure and a mechanical sub-model

implemented in a FEM framework.

Examples have been shown on verification of the simulation model against distortion

measurements on real welding experiments. Very good agreement was obtained between

computer simulations and welding experiments.

Results from a modeling case study on welding of a windshield frame have been presented. A

given welding sequence resulted in considerable angular distortions when rigid clamping was

assumed. However, by selecting an alternative welding sequence and keep the rigid clamping,

the computed distortions were almost eliminated. Alternatively, the distortions were also

eliminated, by applying the original welding sequence combined with a more compliant

clamping. This case study illustrates how a simulation model like WELDSIM can be a valuable

tool in the pre-production phase in fabrication of welded automotive parts in aluminum.

ACKNOWLEDGEMENTS

The authors wish to acknowledge the financial support from Hydro Automotive Structures and

The Research Council of Norway. Moreover, Mr. Peter B. Jacobsen and Mr Flemming Damborg

at Hydro Automotive Structures T0nder, Denmark is acknowledged for their support regarding

the automotive case study presented in the paper.

REFERENCES

1. Myhr, O.R.; Grong, 0.; Klokkehaug, S.; Fjaer, H.G.; Kluken, A.O. 1993. A Process

Model for Welding of Al-Mg-Si Extrusions - Part I: Precipitate Stability, Science and

Technology of Welding andJoining, Vol 2, (6), 245-253.

2. Myhr, O.R.; Klokkehaug, S.; Fjaer, H.G.; Grong, 0.; Kluken, A.O. 1998. Modelling of

Microstructure Evolution, Residual Stresses and Distortions in 6082-T6 Aluminum Weldments,

Welding Journal Vol 77, (6), 286-292.

3. Myhr, O.R.; Klokkehaug, S.; Fjaer, H.G.; Grong, 0.; Kluken, A.O. 1999. Modelling of

microstructure evolution and residual stresses in processing and welding of 6082 and 7108

aluminium alloys, Proceedings of the 5th International Conference on Trends in Welding

Research, ASM International, 233-238.

4. Myhr, O.R.; Fjaer, H.G.; Klokkehaug, S.; Holm, E.J.; Grong, 0.; Kluken, A.O. 1999.

WELDSIM - An advanced simulation model for aluminium welding. The Ninth International

Conference on Computer Technology in Welding. Detroit 1999, 52-63.

5. Myhr, O.R.; Grong, 0.; Andersen, S.J.; 2001. Modelling of the Age Hardening

Behaviour of Al-Mg-Si Alloys, Acta Materialia. Vol 49, 65-75.

263

A COMPUTATIONAL MODEL FOR HEAT AFFECTED ZONE

OF REACTOR PRESSURE VESSEL STEEL

J.S. Kim*, S.G Lee, J.S. Park and T.E. Jin

ASTRACT

A computational model for the heat affected zone(HAZ) of reactor pressure vessel(RPV) steel is

developed to predict microstructures and material properties. The microstructures of HAZ are

predicted by a combination of the temperature analysis considering multi-pass welding and

PWHT, and the thermodynamics-kinetics models for prior-austenite grain growth, austenite

decomposition and carbide coarsening. Also, the hardness, yielding strength and tensile strength

are estimated by the prediction results for microstructures and the empirical relations. Finally,

these prediction results are compared with experimental ones and show the reasonable agreement.

KEYWORDS

Reactor Pressure Vessel Steel, Circumferential Narrow Gap Weld, Heat Affected Zone,

Temperature Analysis, Microstructure, Mechanical Strength

INTRODUCTION

The metallurgical microstructures of HAZ are changed due to repeated thermal cycle during

multi-pass welding and carbides coarsening during PWHT. Especially, in case of SA508 steel,

which has been widely used for pressure vessel because of good mechanical properties, the

mechanical properties of HAZ may be degraded due to the microstructure change. Therefore, the

some studies(Ref. 1~4) has been performed to more detailedly estimate the microstructures and

material properties for HAZ of SA508 steel. However, these studies used the experimental

approaches, so it has the economic problems to apply the methodologies used in these studies to

various welding processes and geometric shapes.

Accordingly, the purpose of this study is to develop a computational model predicting the

microstructures and material properties for the HAZ of SA508 Gr.3 Cl.l, which has been often

used for RPV The microstructures of HAZ are predicted by a combination of the temperature

analysis considering multi-pass welding and PWHT, and the thermodynamics-kinetics models

for prior-austenite grain growth, austenite decomposition and carbide coarsening. Also, the as- welded hardness, yielding strength and tensile strength are estimated by the prediction results for

microstructures and the empirical relations.

* Welding Integrity in Nuclear Structures Laboratory rwww.wins.re.kr"). Korea Power Engineering Company

Zip Code 449-713, 360-9, Mabuk-ri Kusong-eup Yongin-si, Kyunggi-do, Republic of Korea, kinijs@kopec.co.kr

265
COMPUTATIONAL MODEL

Fig. 1 shows a computational model predicting the microstructures and mechanical properties

for the HAZ of SA508 Gr,3 Cl.l. As shown in Fig. 1, firstly, thermal analysis is performed to

determine the peak temperature and cooling rate in HAZ considering real welding processes

such as multi-pass welding and PWHT. Secondly, the microstructures of HAZ are estimated

considering various metallurgical factors such as prior-austenite grain growth and austenite

decomposition during welding. In additions, the carbide coarsening during PWHT is also

estimated. Finally, the changes of mechanical properties due to microstructure changes are

predicted.

WW: TlumalAMafysti

Modeling

Determine

P€tA Temp. & CocUng RaU

]iiteti>sttaet$tte Ss^imMm

Phase EguOibrium Diagram

Grain Site

TTTand CCTDiagram

Phase Fraction

Carbide Coarsening

according 0 PWHT

I

MeehaUdddf^opeiiles

EsOimmon

Vickers Hardness

Yield and Tensile Strength

Fig. 1 . Computational model to predict microstructures and material properties.

TEMPERATURE ANALSYS

Analysis Model

Fig. 2 shows the analysis model of RPV circumferential narrow gap weld. Base material is

SA 508 Gr.3 Cl.l and filler material is L-TEC 44. Submerged arc welding(SAW) method is

used in the narrow gap weld. Welding is performed with 8 1 passes and 39 passes for inner

and outer part respectively. Table 1 summarizes the chemical compositions of SA 508 Gr.3

Cl.l for RPV. And Table 2 summarizes the welding parameters of this model.

Finite Element Model

The finite element for an analysis model is shovm in Fig. 3. The numbers of elements and

nodes are 830 and 940 respectively. The element property is a 4-node axis-symmetric element

(Ref. 5). By using the lumped model(Ref 6), the finite element model is simplified as the one

with 8 and 5 weld layers for inner and outer weld part respectively. Model change

technique(Ref. 5) is adopted to simulate multi-pass welding process.

266
Fig. 2. Analysis model.

Table 1. Chemical composition of SA508 Gr.3 Cl.l

Blement " " 'C '

' ^\ Mn P s Ni' tt

'

wt.% 0.19 0.08 1.35 0.006 0.002 0.82 0.17 0.51

Table 2. Specifications of welding parameters

, Wjtre Size ^ Curreat „>, , Speed , v.. Inteipasstemp. WW.

4mm 500- 600A 28- 32V 30- 40 cm/min 12rC(min.) 200°C(max.) 615°C/40hr

Fig. 3. Finite element model.

Analysis Results

As shown in Fig. 4, temperature cycle in HAZ is the saw-tooth pattern because of repeated

heating and cooling during multi-pass welding.

1600

1400

1200 H 1000

| 800

I 600 I

-F.L.: 0 mm

-F.L.+ 4.24 mm

F.L.+ 10.46 mm

-F.L.+ 16.82 mm

O.OOE+00 5.00E+02 1.00E+03 1.50E+03 2.00E+03 2.50E+03 3.00E+03 Time (sec)

Fig. 4. Peak temperature change vs. time.

267
MICROSTRUCTURE PREDICTION

Determination of Equilibrium Phase Diagram

Fig. 5 shows the equiUbrium phase diagram of SA508 Gr.3 Cl.l according to temperature

calculated by Thermo-Calc(Ref. 7). As shown in Fig. 5, the thermodynamically stable

carbides are MC, M2C, M7C3, ^-carbide and cementite(M3C). But, it is difficult that the MC

type carbide precipitates because of relatively low temperature. These results show the good

agreement with TEM analysis results that the carbides of M2C and M3C type are observed.

Table 3 presents the equilibrium phase transformation temperatures from ferrite to austenite,

Aei and Aes, determined from Fig. 5. From Table 3, these calculation results have a few

differences in quantities from the previous results that are phase transformation temperatures

during heating.

iE-5-1—' ' "1

'

I ' I

1 1- im GQ0 900 1200

Temperature (Celsius)

Fig. 5. Equilibrium mole fractions of phases vs. temperature for the SA508 Gr.3 Cl.l.

Table 3. Phase transformation temperatures from ferrite to austenite.

AiCC) AaCC) Remark

Thermo-Calc 646 790 Ael/Ae3

Ref 8 680 830 Acl/A;3

Ref.9 701 786 Acl/Ac3

Estimation of Grain Size

Grain size is an important factor to predict the microstructure because grain boundaries

provide nucleation sites and diffusion paths. Therefore, the grain sizes of HAZ have to be

estimated according to distance from fusion line before determination of TTT (Time

Temperature Transformation) and CCT (Continuous Cooling Transformation) diagrams. The

grain sizes are predicted by using the following Svensson's equations (Ref 10):

dl'"^ =dl';^ +K,are-^"'^' (1)

268
a = 2^{7cRT^IQ) (2)

T = {q'lvd)l[27tXpe{T^-T,f (3)

where, is the time-dependent grain size, d^^ is the initial grain size, is an exponent,

Q is the activation energy, is the peak temperature, /I is thermal conductivity, and p

is density.

As shown in Fig. 6, there is no grain growth for the case that peak temperature is 900°C. Also,

the final sizes of prior austenite grain are determined as 30~38|a.m by using the temperature

analysis results.

55

50

45

i. I"(0

.E 35 sO

30

25

20

0 5 10 15 20 25 30 Time (sec)

Fig. 6. Prior austenite grain growth according to peak temperature.

Prediction of CCT Diagram

To predict the CCT diagram of SA508 Gr.3 CI. 1 , TTT diagram is determined by using the

grain growth results and Li's reaction kinetics model(Ref. 11). And then the CCT diagram is

determined by using the TTT diagram and Scheil-Avrami addivity rule(Ref 12, 13). Fig. 7

shows the predicted CCC diagram of SA508 Gr.3 Cl.l.

900

800

700

S 600 iI

500

400

300

200

0.1 1 10 100 1000 10000 100000 Time (tec)

Fig. 7. Predicted CCT diagram of SA508 Gr.3 Cl.l.

1 1

Grain s|r'ze = 8 |

1 1

1 1

1

1

1

1

1

1 1

1 1

1 1 1% TlllllHR^JIIBllW^

"'"

_L 1

1 1

1 1

L

\y

Ferrlte yf r y— 110% Transformation / '

'

1 1 /k

1 1 - -j f VmtnXv-,^-^'-^ —!——1 — —

1

1 / 1

-

'

1

1

T 1

1 1

1 1 "1— i'

1 1

-L 1

1 1

1 1

1

1L

1

1

1 1

1 1 J 1

1 1

1 1

269
Prediction of Precipitation Phases

By superposing the cooUng curves obtained from temperature analysis on the predicted CCT

diagram, the precipitation phases are determined according to cooling rate or distance from

fusion line. As shown in Fig. 8, we can expect that only bainite and martensite will be formed

over HAZ and the volume fraction of martensite will decrease with the increase of distance

from fusion line.

1200

1000

— 800 V

Sf

600 I

E

400

200

0.01 01

1 1 1

1 1 1

1 1 1

1 1 1

1 1 r1 1 1 12-0/^0 (F.L.+2.8mm)

| ^'n.t-.-.T -.X^ - ^ -

r — —1- 1 1

1 1 1 1

1 1 1 1

1 1 1 1 — -1 t t 1

1 1 1 1

1 1 1 1

1 1 1 1

13«c/9e<l (F.L.+O.SmmrV

1 1 i\

~ ~ (Srafri Shff^-f\- 1

III--' ^''^"'^^ f'r ~ i

1

[

1 1 1 1 1 ± 1 J _ .

Mjattenalte

I ! ! I

1 1 1 1 1 1 1

1 1 1 1 1 1 1

1 1 1 1 1 1 1

1 1 1 1 1 1 1

1 1 1 1 1 1 1

1 1 1 1 1 1 1

10 100 1000 10000 100000 1000000 Time (sec)

Fig. 8. Prediction of precipitation phases for SA508 Gr.3 Cl.l.

Estimation of Carbide Coarsening due to PWHT

Carbide coarsening during PWHT, which causes bainite microstructure to degrade hardness

and ductility, is estimated by DICTRA(Ref. 14). Fig. 9 shows the estimation results of M02C

coarsening during PWHT. As shown in Fig. 9, we can expect that the carbide size will

increase by 30% as compared with initial size.

g J 1 1 1 1 1 1 1

5-

1

-

0 H 1 1 1 1 1 1 1 r e 5 IQ 15 35 30 3S

F>WHT Time (hours)

Fig. 9. Carbide coarsening due to PWHT.

270
MATERIAL PROPERTIES PREDICTION

Prediction of Hardness

The hardness of HAZ before PHWT is predicted by using the volume fractions of

precipitation phases and the empirically based formulas (Ref. 15). Fig. 10 shows that the

hardness decreases with the increase of distance from fusion line. Also, comparing with the

experimental results(Ref. 16), the difference from the ones increases with the increase of

distance from fusion line.

500

450

400

350

300

250

200

150

100

IT

• Prediction oAflerPWHT

Before PWHT|KAERI|

AAfterPWHTIKAERI]

Distance from Fusion Line (mm)

Fig. 10. Vickers hardness distribution according to distance from fusion line.

Estimation of Mechanical Strength

Yielding and tensile strength are estimated by the following relations (Ref. 17) between

Vickers hardness and mechanical strengths:

C7 =3.1/f,(0.1)"-80 (4)

o„=^.5H,{\-n){\2AI{\-n)Y -92

« = 0.065(Ar,oo/5oo)'"

(5)

(6)

From Table 4, we can expect that the predicted results will be greater than the experimental

results.

Table 4. Comparison between the predicted and the experimental mechanical strengths.

Location ^800/ soo Prediction (MPa) Experiment (Ref. 18)

^« ^«

FL+0.8mm 417.6 23 sec 923.1 1648.2 1286

FL+2.8mm 325 25 sec 697.8 1265.8 1039

SUMMARY

In this study, a computational model for the HAZ of RPV steel is developed to predict the

microstructures and material properties for HAZ of SA508 Gr.3 Cl.l by using the

temperature analysis, the thermodynamics-kinetics models and the empirical relations. The

271
prediction results by applying this model show the reasonable agreement with the

experimental results.

CONCLUSIONS

Based on the prediction results obtained from this study, the following conclusions can be

made: • Temperature cycle in HAZ is the saw-tooth pattern because of repeated heating and

cooling during multi-pass welding. • There is no grain growth for the case that peak temperature is 900°C and the final sizes of

prior austenite grain are also determined as 30-3 8|im. • Only bainite and martensite are formed over HAZ and the volume fraction of martensite

decreases with the increase of distance from fusion line. • The carbide size increases by 30% during PWHT as compared with initial size. • The as-welded hardness decreases with the increase of distance from fusion line. • The predicted results for mechanical strengths are greater than the experimental results.

REFERENCES

1. Suzuki, K., et al, 2001, Nuclear Engineering and Design. 206, 261-278.

2. Kim, J.K. and Yoon, E.P., 1998, Journal of Nuclear Materials, 257, 303-308.

3. Alberry, P.J. and Lambert, J.A., 1982, The Welding Metallurgy of SA508 Cl.II Heat

Affected Zones, Central Electricity Generating Board, TPRD/M/1223/R82.

4. Kussmaul, K., et al., 1982, Int. Conference on Welding Technology for Energy

Applications, Gatlinburg, Tennessee, May 16-19, 17-69.

5. HKS Inc., 1998, ABACUS User's Manual, Version 5.8.

6. Dong, P. and Brust, F.W., 2000, J. of Pressure Vessel Technology, 122, 329-338.

7. Thermo-Calc AB, 1999, Thermo-Calc User's Manual, Version M.

8. Atkins, M., 1980, Atlas of CCT Diagrams for Engineering Steels, ASM.

9. Hanjung Heavy Industry, 1996, Determination of Material Properties for SA508 Cl.III.

10. Svensson, L.E., 1993, Control of Microstructures & Properties in Steel Arc Welds, CRC

Press.

11. Li, M.V., et al., 1998, Metallurgical and Materials Transactions B, 29B, 661-672.

12. Scheil, E., 1935, Arch. Eisenhuttenwes, 8, 565.

13. Avrami, M., 1939, J . Chem. Phys.. 7, 1103.

14. Thermo-Calc AB, 1998, DICTRA User's Manual, Version 2.0.

15. Maynier, P., et al., 1978, Hardenabilitv Concepts with Applications to Steels, AIME, 163-

176.

16. Kang, S.Y., et al., 1999, J. of Korean Institute of Metals & Materials, 37(4), 423-434.

17. Akselsen, O.M., et al., 1990, Material Science and Technology, 6, 383.

18. Kim, J.H. and Hong, J.H., 2001 . The 2^^ Meeting for Welding Integrity Evaluation.

272
NOVEL COMPUTER-AIDED METHOD

OF WELDING MATERIALS DESIGN

V. Mazurovsky\ M.Zinigrad^, A. Zinigrad^

ABSTRACT

A novel welding materials design method is described. New CAMD (Computer-Aided

Materials Design) software based on the mathematical simulation is proposed.

At present there is no integrated design procedure available to the welding materials

developers. The authors have developed same, based on the following:

1) mathematical modeling of the technological process of welding (taking into

account non-equilibrium crystallization and strengthening-phase-formation

processes in the weld);

2) structural analysis of the problems relating to the design of new welding

materials.

The mathematical models used as a basis for the method developed are described. These

mathematical models enable the following to be performed:

a) predicting the composition, structure and properties of the weld based on the

structural analysis and structuring the tasks defined according to the

requirements of the customer;

b) calculating the composition of welding materials which will ensure obtaining

of the weld metal with required properties.

Such integrated approach allows developing new welding materials according to the

specific requirements of the customer.

NOVEL METHODOLOGY OF THE MATERIALS DESIGN

We understand design as a process of developing tiie package of technical documentation for

the new product manufacturing - starting from the technical specification formulation and

proceeding with its realization.

Design is complex system built as hierarchy and sequence of operations having respective

links, priorities, tasks and intermediate solutions.

The optimal way to realize such a system (while automating the process) calls, for our

opinion, for the following:

1. System approach (with the design process as the object of analysis)

2. Mathematical modeling (allowing automating the process of forming intermediate

solutions and final solution)

SYSTEM APPROACH

System approach is only a means to structure a problem, to establish links and the order of

priorities, to structure data, etc., using structural analysis. System approach as a multi-

' CWC Ltd., Ariel, Israel

^ College of Judea and Samaria, Ariel, Israel

273
purpose method uses abstract notions, which in our (as in any particular) case acquire a

specific practical application:

subject domain- specific fabrication process for a specific customer;

subject domain element - an object or a process (belonging to the subject domain and

having specific features, functions and links with other objects and processes) selected

during the structural analysis. In our application, those will be the following:

- technological process of welding (hardfacing) itself;

- welding equipment;

- tools;

- environment, etc.

- design object - a subject domain element, whose design (development) is an objective of

the task being structured. In our application, these are new welding materials.

A brief review of the major stages of structural analysis of the welding materials design

problems is presented below:

1. Definition of the design stages (stage of the technical specification formulation; stage

of the technical specification reali2;ation).

2. Structuring the stage of the technical specification formulation

2.1. Determining the structure of the design object

2.2. Determining the structure of the subject domain.

2.3. Determining of the input parameters for the design:

2.3. 1 . Characteristics of the design object elements;

2.3.2. Characteristics of the environment,

2.4. Determining the output parameters: characteristics of the design object.

2.5.Establishing functional relationship between the design object and the subject domain

elements (direct and reverse connections).

2.6.Establishing connections between the subject domain and the design tool (expert

system).

2.7.Determining the operational algorithm structure and the subject domain representation

method for the design tool.

3. Structuring the stage of technical specification realization

3.1. Analysis of the technical specification. Formulating the task (specification) for

CAMD;

3.2. Forming design operations and procedures as well as priorities and links;

The design tool (CAMD system) provides the designer with the solution of the task

formulated (in our case: development of the welding material which provides for obtaining

welds with required properties - as defined by the customer).

Flow-chart of the stage of technical specification realization and forming the final solution

using CAMD system is shown on figure 1.

274
Task for CAMD

Determining

weld metal

structure

I

1

Determining

properties and

chemical

composition

I

Formulation of welding consumable

composition

Optimization of the composition

Knowledge

base

Forming the solution

Output

documents

Figure 1: Flow-chart of the stage of technical specification realization

275
Customer

Designer

Data analysis

and formation of

data packages,

rules and

instructions

o ^ c -a §

C es H

2JS

Joint/hardfaced

element

Operating

conditions

including

environment

characteristics

Operating

environment

Cutomer's

requirements

to the

weld/deposit

Results of

the design

object

operation

(weld/deposit

metal)

J

Task foriQulation

Intellectual

interface

Equipment

characteristics

Customer

facilities

Design object

properties

Design object

functions

Design object

(welding

consumable)

CAMD

Testing of

the

weld/deposit

properties

Figure 2: General flow-chart of the design process

276
MATHEMATICAL MODELING

The basis for the technical specification realization according to the approach proposed is

the CAMD system, based on the software package.

This software was developed on the base of the mathematical models of the technological

process of welding and the process of non-equilibrium weld/deposit metal crystallization. The

above models provide for the possibility of calculation of the composition of welding

material, which will ensure obtaining of the weld metal with required composition, structure

and properties.

MODELING PHASE INTERACTION

The method of the mathematical modeling of the phase interaction during welding and

some examples of its implementations are presented, for instance, in [1].

The mathematical model of the processes involved in the physicochemical interaction of the

phases is based on the method for the kinetic analysis of the interaction of multicomponent

metallic and oxide melts previously developed with participation of one of the authors of the

present work [2]. It is used to solve the most complex problems in modeling, viz.,

consideration of the rates of transfer of all the elements through the phase boundaries, as well

as consideration of the mutual influence of all the chemical reactions taking place on these

boundaries. On the basis of this method it is possible to take into account the complex

interactions between all components, i.e. their interactions with each other [2]. At present in

the physicochemical literature there are a lot of data on the thermodynamic and kinetic

parameters of high temperature processes involving different metals, oxides and gases and

physical properties of these phases which are necessary for computation. The missing data is

obtained in the present paper. The authors of the work have the large experience in the field

of experimental obtaining of the physicochemical constants [3 -7].

The method described provided good results in the modeling of the bucket refining of steel

[8, 9]. We developed a general scheme for the mathematical modeling of processes, whose

investigation will be the subject of the present work, and we have compiled the database

needed to create the model (which includes thermodynamic, kinetic, and diffusion constants,

as well as a large body of technological data).

A fairly large amount of experimental data on the kinetics of chemical reactions on a metalslag interface has been obtained. Both general laws and individual steps of processes have

been investigated while taking into account the influence of the temperature, composition,

mixing rate, and other factors.

In most technological processes involved in the production of metals and alloys, the principal

reactions determining the final composition of the products take place on the boundary of the

metal with the slag. They are primarily redox reactions involving alloying elements and

harmful impurities, as well as vaporization, gas-adsorption, and other processes. When the

kinetics are analyzed, special attention is focused on revealing the nature of the individual

rate-limiting steps of the overall heterogeneous reaction.

We note that the analysis of the kinetics and mechanism of individually occurring reactions

does not present any special difficulties at the present time and that, as a rule, its results

faithfully describe the real processes. A kinetic analysis of the interaction of multicomponent

277
metallic and slag melts with consideration of the mutual influence of reactions taking place in

parallel is considerably more complicated.

Let us briefly describe the method for the kinetic analysis of diffusion-controlled reactions

that we previously developed [2]. Thetheoretical basis of the method consists of two

assumptions:

1) under diffusion-controlled conditions the concentration ratio on the phase

boundary for each reaction is close to the equilibrium value;

2) the rate of transfer of the reactants to the phase boundary or away from it is

proportional to the difference between their concentrations in the bulk and on the

boundary of the metallic and oxide melts.

The oxidation of elements in a metallic melt can be represented by the reaction: -[E,] + (FeO)

^-(£,0J + Fe, (1)

m m

In this equation Ej are the elements dissolved in the liquid metal pool (Mn, Si, W, Mo, V,

etc.), and Ei„Om are their oxides in the slag phase. The problem reduces to calculating the

rates of reactions of type (1). We note that the problem of determining the rate of such a

reaction for each element individually does not create special difficulties today. However,

such an approach, i.e., analyzing each reaction individually, does not correspond to the

situation in an industrial welding process, hi the real case the mutual influence of the metal

and slag components, as well as the mutual influence of all the heterogeneous reactions

occurring in this complex system, are observed.

Our approach permits the determination of the rates of reactions of type (1) for all the metal

components with taking into consideration their mutual influence:

Vr = , ^'

, , (2)

Here x is the ratio between the concentration of ferrous oxide (FeO) in the slag and the

concentration of iron in the metal [Fe] at the phase boundary; V^^ and Fj^^^ are the limiting

diffusion rates of the components; [Ei] and (EinOm) are the initial concentrations of the

elements and their oxides in the metal and the slag, respectively; Ki is the equilibrium

constant of the reaction involving [Ei]; and n and m are the stoichiometric coefficients.

Li other words, is the rate of passage of any element from the slag to the metal (or in the

opposite direction), and the further problem reduces to calculating the concentration of that

element as a function of time. After solving this problem, we become able to determine both

the time-variant composition of the liquid metal pool and the fmal composition of the weld

metal. Since the rates of passage of elements through the phase boundary Kg depend

significantly on the temperature, composition, hydrodynamic conditions, and some other

factors, the correct determination of the technological parameters of a welding process would

be of great value.

The concentration of element i in the weld metal in the case, for example, of flux-core arc welding, can be written in the form [1]

278
,^,r • [£, ], • At + F,, • [E, 1„-AT+V,^-M,^-A^-AT-100

LA J =

T

'

where Ap is the area of the interface between the metal and the slag, and nip is the mass of the

weld pool at the time x.

The system of equations (2, 3) along with the equations deriving from the stoichiometry of

the reaction (1), comprises a mathematical model, which describes the interaction between

the phases in real welding (in this case FCAW) process. The model connects the composition

of the weld metal with the composition of the welding consumables thus allowing calculating

the composition of the welding consumable providing for the obtaining of the weld metal

with required composition.

MODELING NON-EQUILIBRIUM CRYSTALLIZATION PROCESS

The model of non-equilibrium weld crystallization process proposed by the authors [10],

constitutes mathematical description of the process of formation of the strengthening phases

and calculation of chrome and nickel equivalents thus connecting the content of the

strengthening phases and structure of the matrix with the composition of the metal (for weld

metals over a broad range of levels of alloying).

The Schaeffler diagram has been modified taking into account the following factors:

- nonequilibrium crystallization;

- the formation of strengthening phases;

- mutual influence of the alloying elements.

The model includes the parametric dependencies connecting the weldability and

mechanical properties of the weld with its structure and composition.

DESIGN PROCEDURE

Based on the structural analysis and mathematical modeling, the authors have developed a

design method comprising a sequence of design stages (with specific instructions and rules

for each stage): from data package formulation to the solution development phase.

Structure of the design process with direct and reverse connections between the stages,

functional dependencies, and procedures of forming of input and output parameters are presented in the flow-chart of the design process (See figure 2).

As seen from the figure 2, the proposed method comprises a close-circuit system with a

comprehensive coverage of reverse connections, thus providing the following:

1. structuring the problem, defined according to the actual application (as defined by the

customer);

2. forming data packages and technical specification

279
3. forming instructions and rules based on the above, presentation thereof as a task

definition for the CAMD system;

4. solving the task in a dialogue mode between the designer (expert) and the CAMD

system;

5. formulating the solution as:

a) formula of the welding material needed to obtain the weld metal required;

b) predicting the composition, structure and properties of the weld;

c) welding/ hardfacing parameters.

CONCLUSIONS

The novel welding materials design method developed constitutes a comprehensive

system allowing for the developing of custom welding consumables.

REFERENCES

1. Detroit, MI. 1999. Computer modeling of metallurgical technologies. Proc. Ninth

International Conference "Computer Technology in Welding". M. Zinigrad, V.

Mazurovsky: 164- 171, NIST Special Publication 949.

2. Boronenkov V.; Shanchurov S.; and Zinigrad M. 1979. Kinetics of the interaction of

multicomponent metal with slay under diffusion conditions. Izvestiva Ac. Nauk

USSR. Metal. 6: 21-s to 27-s.

3. Panphilova L.; Zinigrad M.; and Barmin L. 1978. Effect of surface concentration of

oxygen in Me-S melts on the kinetics of its transfer through a sulfur-oxide melt

interface. Journal Phvs.Chem. : 5, 10, 249 1-s to 2494-s.

4. Flyagin A.; Zinigrad M.; and Barmin L. 1979. Kinetics of ion exchange between an

ironcarbon-aluminium melt and an oxide electrolyte. Electrochem.: , 5, 12, 1858-s to

1861-s.

5. Panphilova L.; Zinigrad M.; and Barmin L. 1 98 1 . Quick stage kinetics of oxygen ion

discharge on the boundary of sulfide melts and liquid oxides. Electrochem.: 17, 9,

1346-s to 1349-s.

6. Zinigrad M.; Phephelov A.; Barmin L.; and Shalimov M. 1986. Kinetics of the

interaction of a boron containing metal melt with an oxide electrolyte. Electrochem.: 22, 1, 74-s to 76-s.

7. Zinigrad M.; Okolzdajev A.; and Flyagin A. 1988. Limiting stage of anodic oxidation

of tungsten at the boundary of metallic and oxide melts. Rasplaw: 2, 3, 46-s to 5 1-s.

8. Boronenkov v.; Zinigrad M.; and Shalimov M. 1983. Mathematical modeling of

metal and slag- processes interaction in a ladle. IZV VUZ. Tcher. metallurg: 1, 36-4.

9. Sendai, Japan. 1992. Simulation of metal and slag interaction for optimization and

development of technological processes, Proc. of the 4^ Molten Slags and Fluxes

International Conference. M. Zinigrad: 125.

10. Ariel, Israel. 2000. Predicting weld structure using modified Schaeffler constitution

diagram. Proc. of the International Conference Mathematical Modeling and

Simulation of Metal Technologies. V. Mazurovsky, A. Zinigrad, and M. Zinigrad:

540-545. College of Judea & Samaria Press.

280
I

Finite Element Analysis of Creep Strength

Mis-matched Dissimilar Welded Joints

It Itit Chen Hui , Chen Zigang

* Department ofMaterials Engineering, Southwest Jiaotong University, Chengdu, China,

610031

**Dept. ofMaterials Science andEngineering, Dalian Railway Institute, Dalian, China, 116028

The finite element analysis (FEA) of mismatched welded joints with a 30°

groove angle was performed to study the mechanical behavior of DMWJs (dissimilar

metal welded joints). It is concluded that the distribution of stress triaxiality in the

DMWJs is uneven, especially near the bond lines. The degree of creep strength

mismatch has remarkable effect on the distribution. The higher the level of mismatch

is, the more uneven the distribution is and the easier for premature failure to occur in

the joint

Key Words: dissimilar metal welded joints, finite element analysis, creep strength

mismatch, stress and strain

281
Introduction

The premature failure in of dissimilar metal welded joints (DMWJs) frequently occurs

during elevated temperature services'-* So far the predominant causes of the failure have not

been clear, although some research workers have reported that the mismatch of creep strengths

between the weld metal and base metals is the predominant cause of the failure^^-'. The

mechanical behavior of DMWJs is not clear, and the factors that accelerate the failure remain

to be determined.

In the investigation, finite analysis was carried out to study the high temperature behavior

of mismatched DMWJs with different creep strengths. The effect of creep strength mismatch

on the distribution of triaxality in the joints is discussed particularly.

1 Materials and Analysis Model

1.1 Materials and creep law

9CrMoVNb, 12Cr2MoWVTiB and 12CrlMoV steels are selected respectively as high

strength metal (H), moderate strength metal (M) and low strength metal (L).

Bailey-Norton Creep law is used in the analysis, which is — — m n e=A(7 -t where:

A, m and n are constants of the material varying with temperature;

e is the effective strain; c7 is the effective stress; e and a are defined by

-.=|V(«-«/+(^-^f+(a-af and c=^^(.-a.f.«.-cf^o-af,

where s^^ and are the principle strains; and is the principle stresses. The mechanical properties and creep constants of test materials at elevated

ten:perature(560°C) are given in Table l^^l

1.2 Parameters and model

In this paper, stress triaxiality Ot is the main factor to be studied, which is defined by the

following formula:

where: is the hydrostatic stress, defined as

and a is the effective stress.

oil is a parameter relative to brittle fracture or rupture with low ductility. Under high

hydrostatic stresses, materials will not be difficult to deform, which may lead to brittle

fracture.

The effective stress is relative to plastic deformation. The higher the effective stress is,

the more a material can deform, which may lead to plastic fracture.

Ot increases with o^ and decreases with o. The greater the ot is, the more likely the

material fractures under low ductility.

282
Table 1 The mechanical properties and creep constants of test materials at 560"C

.

XT

0

Material

Os

(MPa

)

Ob

(MPa

)

(GPa)

A m n

H 9CrlMoVNb 478 568 175.5 3.27 X 10-' 2.17 0.58

M 12Cr2MoWV 318 435 175.3 3.81X10-^2 3.40 0.68

L TiB 183 302 175.1 2.79X10-'^ 6.13 0.78

12CrlMoV

The joints analyzed in the FEA are shown as in Fig 1

.

80

B

C C

Fig.l The joints used in FEA

The base metals in all the joints analyzed were respectively 12CrlMoV(L) and

9CrMoVNb. 12CrlMoV (L), 12Cr2MoWVTiB (M) and 9CrMoVNb were selected as the

weld metal respectively in the low, moderate and high strength matched joints. L, M and H

means low, moderate and high creep strength.

The joints analyzed in FEA operate at the fixed temperature of 560°C with initial tensile

stress of 120 MPa along the transverse direction of the joints (direction of 7 axis).

The FEA is performed under the plane stress state.

2 Analysis Results and Discussion

2.1 The distribution of stress triaxiality in DMWJs

The distribution of stresses in the joint of L-M-H after different creep times is shown as

Fig. 2. In Fig. 2, it can be observed that the distribution of stresses {ay, Ch, at) is even at the

time without creep deformation (/=0 h.). The distribution becomes uneven under creep

condition and the degree of this uneven distribution increases with the extension of the creep

time due to creep deformation. It should also be noted that there is a great fluctuation of the

distribution nearby the interface of 12CrlMoV to 12Cr2MoWVTiB or 12Cr2MoWVTiB to

9CrMoVNb.

2.2 The effect of creep strength on the distribution of stress triaxality

The distribution of at in mismatched joints of steels with different creep strength was

studied after the joints have undergone 1 000 h creep deformation. The distribution of at on

the section A-A, B-B and C-C in L-L-H joint is shown respectively in Fig. 3(a), (b) and (c).

The distribution of at near the interface of L and H is uneven, and the highest value and the

most uneven distribution of stress triaxiality is at the weld toe.

The distribution of at on the section A-A^B-B and C-C of L-H-H joint is shown

respectively as Figure 4(a),(b) and (c). It can be noted that the distribution of at near the

interface of L to H is uneven, and the highest value and the most uneven distribution of stress

283
triaxiality is at the location corresponding to weld root and the location adjacent to the bond

line on the B-B section.

1=1000.

t=iooo.

20 30 40 50 60

Location (mm)

(a)

1=1000.

Location(iiin)

(b)

Location(inm)

Figure 2 The distributions of stress on B-B section in the joint of L-M-H

8

&

25

P

30 40 50 60

Location(mm)

(a)

I 1

S

4if---.

3D X

Location(inm)

(b)

Lx>cation(inti)

(c)

Figure 3 The distribution of at on the section A-A, B-B and C-C in L-L-H joint

76

e

55

, tr

e

95

Location(mm)

(b)

Location(inin)

(c) Location (mm)

(a) Figure 4 The distribution of Ot on the section B-B and C-C in L-H-H joint

The distribution of crt on section ^-yi, B-B and C-C of L-M-H is shown respectively in Fig.

5(a), 5(b) and 5(c). It can be seen that the distributions of at near the interface of L-M and

that of M-H are uneven, but the values are much lower, and the unevenness is not so

significant, as compared with that near the interfaces in L-L-H or L-H-H joint.

at increases with ah and decreases with a. The greater the at is, the more likely the

material fractures under low ductility, which is corresponding to typical creep rupture. Stress

triaxiaUty is the factor related to the creep cavity formation and linking^^'l Ref. [5] presented

that stress triaxality is the dominant factor to accelerate the brittle creep rupture. So the degree

284
of risk of the joint increases with the peak value and the level of uneven distribution of the

stress triaxiality near the inhomogenous interface.

20 30 40 50 60

Location(mm) Location(mm) Location(ran)

(a) (b)

Figure 5The distribution of Ot the section A-A, B-B and C-C of L-M-H joint

As a summary, the potential risk location is closely related to the mismatch of creep

strength. However, the location of the highest stress triaxality and unevenness of stress

triaxality distribution are at the root for an L-H-H joint, but at the toe for an L-L-H joint,

although both locations are close to the L-H interfaces, and are the potential risk locations

where creep cracking easily occurs. The cause of the phenomenon needs further study.

3 Conclusions

(1) The distribution of stress triaxiality in DMWJs is uneven, especially near the

inhomogenous interface. The degree of the uneven distribution increases with the extension of

creep time.

(2) The degree of the uneven distribution increases with the increase in mismatch of creep

strengths on the inhomogenous interface. The value and the uneven distribution of stress

triaxiality near L-H interface is much higher than the ones near L-M and M-H interfaces.

(3) The value and the uneven distribution of stress triaxiality in moderate mismatched joint

is low. The moderate mismatched joints are recommended for application in engineering.

References

1 Kefer D. W., Reuter W. G., Smartt H. B., et al, Needed: verified models to predict the fracture

of weldments. Welding Journal, 1993, Vol.72, No.9, 73-78.

2 Holko K.H., Li C.C., Dissimilar weld failure analysis and development program, International

Conference on Welding Technology for Energy Applications, Gatlinburg, Tennessee, May 16-19,

1982, (?)

3 Ye S.Y., Cong X.Z., Study on creep rupture of dissimilar welded joints, The 7th. Chinese

Welding Annual Assembly, June, 1993, Vol.2, 236-240 (in Chinese).

4 Nath B., Creep rupture and creep crack growth behaviour of transition joints. Int. Conf on

Welding Technology for Energy Application, Gatlingburg, May, 1982, 597-621

5 Shi C.Y., Tian X.T., Chen Z.G., et al., On Mechanical parameter controlling creep brittle

rupture along interface of dissimilar metal welded joints. Transactions of the China Welding

Institute, Vol. 16(4), 1995,12 (in Chinese).

285

Session B3: Modeling III

287

LASER WELD KEYHOLE DYNAMICS: SIMULATION AND EXPERIMENT

M.H. Cho*, D. Farson**, J.Y. Lee*", CD. Yoo****,

ABSTRACT

The dynamics of a keyhole formed by high energy density laser-material interaction is studied by

x-ray videography experiments and by simulation. The molten material lining the sides of the

keyhole periodically collapses due to the surface tension. The collapse of the keyhole due to the

surface tension traps a void at the bottom of the keyhole. This phenomenon causes the instability

of the keyhole during the laser beam welding. As the laser radiation continues to impinge on the

material, the keyhole is subsequently forced open again and then re-collapses at a later time. This

cyclical collapse behavior is verified qualitatively by high-speed x-ray images of laser keyholes.

The first keyhole is produced about 1.75ms after the laser beam impact on the material and then

it takes 0.88ms to collapse again after the first keyhole collapse, which happens at 1.97ms

welding time. Also, the flow of the melt is calculated using the Volume of Fluid technique in

order to compare the results for the simulation of keyhole to the experimental results. The

evaporation recoil pressure, calculated from the thermodynamic relations, is the key driving

input that causes motion of the melt layer. The propagation of the Gaussian laser beam within the

keyhole is calculated using a ray-tracing model. According to the simulation, it takes

approximately 0.2ms between the formation of protrusions and the keyhole collapse. This is the

same as the experimental results. The simulation predicts that the laser-induced keyholes are

unstable. The simulation also predicts the formation of "protrusions" on the keyhole wall due to

non-linear interactions between the melt dynamics and laser power density on the keyhole wall.

INTRODUCTION

The laser welding process is one of the most attractive methods for joining two materials up to

32mm thickness with a single pass. The laser welding process can minimize the distortion of the

materials due to the minimum heat input for fiising the metal and also increase the productivity

due to the high travel speed. ^ The laser is easily automated by computer control, so the precision

of the weld joint is much better than the one used by the conventional electrical arc process.

The keyhole formed by a high energy density laser-material interaction is one of important

aspects in the laser welding process. The keyhole dynamics have been studied for a long time,

but it has been very difficult to observe and predict it. According to previous publications, the

keyhole is not constant but dynamically fluctuating during the laser welding process, so this is

one of the factors to have the welding defects. When the laser beam impinges on the workpiece

with the power densities on the order of 10^ W/cm^, the keyhole is created due to the highdensity energy and is dynamically fluctuating. During the fluctuation of the keyhole, the keyhole

* Graduate Student in Welding Engineering department at the Ohio State University

** Professor in Welding Engineering department at the Ohio State University

*** Graduate Student in Mechanical Engineering department at KAIST in Korea

**** Professor in Mechanical Engineering department at KAIST in Korea

289
is collapsing and reopening periodically due to the continuous beam and the high surface tension

of the material. The flow of melting material has important effects on the keyhole dynamics.

The direct observation of the keyhole is the one of the best ways to trace the flow of the melt.

The keyhole was observed by x-ray videography experiments.^' ^ The charge coupled device

(CCD) camera with a recording rate of 4500 frames per second was used to capture the keyhole

formation because the keyhole is fluctuating rapidly. In this work the simulation of the keyhole

calculated by Volume of Fluid technique is used to predict the formation of the keyhole and the

results are compared to the pictures taken by x-ray videography in order to verify the keyhole

dynamics.

OUTLINE OF STUDY AND METHODS

The dynamics of a keyhole formed by high energy density laser-material interaction was

investigated by x-ray videography experiments and by numerical simulation. In the x-ray

videography experiment, the images of keyhole formation during the laser welding

implementation were captured by a high speed CCD camera. In the simulation, the effect of

multiple reflections in the keyhole was modeled using the ray tracing method, and the free

surface profile, flow velocity and temperature distribution of the keyhole were calculated

numerically using a volume-of-fluid (VOF) technique.

Experimental procedure

The experimental setup for direct observation of the keyhole formation is shown in Figure 1 . The

dynamic behavior of the keyhole formed by the 500W CO2 laser interaction on the edge of the

plain carbon steel plate is observed by a micro-focus x-ray imaging system. (Figure 1) A

Feinfocus® 225 kV X-ray unit with 5 jim (0.0002 in.) focal size was used as an X-ray source. The positioning system was used for the manipulation of the sample. This system has three linear

axes with two-micron resolution and one rotating axis with 0.01° resolution. The magnification

of the image is defined as d/do, so the magnification of the image is larger as the sample is close

to the x-ray source. In this experiment the distance (do) from the x-ray source to the sample was

1.1875 in, and the distance (di) fi-om the x-ray source to the intensifier was 28.1875 in.

Therefore, the magnification of the image came out to be 23.7 times (28.1875 in/1.1875 in). At

this high magnification, a small x-ray source size is needed to minimize image unsharpness.

The CCD camera, EKTAPRO HS Motion Analyzer, was used for taking pictures of the image,

which is the optical image converted from x-ray image by the intensifier. This CCD camera is

located at the end of the intensifier and captured the keyhole image inside intensifier. This CCD

camera was able to record the pictures up to 40500 frames/sec, and the capacity of the memory

can store 1024 full frames. In this experiment, 4500 frames/sec recording rate was used which

was able to capture the images every 0.22ms. This fast recording rate was able to clearly image

the collapse of the keyhole and permit observation of the keyhole dynamics.

Parameters for CO2 laser were 500W power, 20kHz frequency pulse, 50|is period, and 26|a,s

pulse width. The 20kHz pulsed frequency was chosen to be well above the natural frequencies

of the molten keyhole. The laser focus spot size was 0.13mm diameter. The thickness of the

290
plain carbon steel was 1mm. Sample thickness is critical because the attenuation of x-ray through

the thick plate is large so the keyhole cannot be observed well. Samples thinner than 1mm are very difficult to manipulate and weld on the edge of the plate.

A pre-welded sample was used to find the optimum positioning parameters to obtain the desired

image of the keyhole before implementing the real time monitoring of laser welding keyholes.

After the welded sample was set up in the optimum position the welded sample was replaced

with the new sample and then the laser beam hit the edge of the plain carbon steel for about one

second while the CCD camera took a picture every 0.22ms.

Formulation for Numerical Simulation

The simulation was comprised of a fluid/thermal simulation to calculate keyhole formation and a

ray-tracing simulation to calculate propagation of the laser beam within the keyhole. The raytracing method of calculating beam propagation assumes that incident radiation consists of a

number of rays, each having a specified direction and power,'* The absorption of laser power for

a simplified conical shape is increasing compared to the flat surface due to multiple reflections

within the keyhole as shown in Figure2. In this work, the laser beam incident on the work

surface was assumed to consist of parallel rays, the power assigned to each calculated from a

circularly symmetric Gaussian profile having specified 1/e^ radius. The ray tracing proceeded

under the assumptions that the material surfaces were specular, that plume absorption was

negligible and that the beam polarization was random. Consistent with the last assumption, the

reflectivity of the iron surfaces was taken as the intermediate value between available "s" and

"p" reflectivity vs. angle curves. The number of reflections and the accumulated power density

X-ray Intensifier

CO2 laser Microfocus

I I X-ray source

Figure 1: Microradiographic setup for observation of keyhole behavior

291
were calculated at each position on the keyhole wall, and the absorbed power density was given

as the boundary condition to calculate the temperature of the keyhole.

Figure!: Ray tracing in the keyhole cavity

The axisymmetric keyhole profile and flow velocity were calculated numerically using the

Volume of Fluid (VOF) method.^ The governing equations for the VOF method consist of

continuity and momentum and volume fraction equations

Vv = 0 (1)

-V— 1 ^

+ (v • V)v = --VP + vV'v + PgAT (2)

at p

^+ (vV)F = 0 (3)

where v is the velocity vector, P is pressure, p is mass density, v is viscosity, P is thermal

expansion coefficient, g is the gravity vector and F is volume fraction.

The temperature distribution was obtained by solving the energy equation

pCp(^ + v.VT) = kV^T (4)

292
where Cp represents the specific heat and k the thermal conductivity. Note that the cooling effect

of vapor leaving the surface is neglected in this energy equation. The recoil pressure due to

evaporation was taken as 0.54 times the pressure of vapor in equilibrium with the liquid surface

(a common assumption that implies vapor flow velocity is at its maximum possible value). The

equilibrium vapor pressure was calculated from the integral of the Clausius-Clapeyron equation^

= 0.54Po exp(A//, (5)

where Po denotes atmospheric pressure, R the universal gas constant, AHy the latent heat of

vaporization and Tv evaporation temperature, respectively. When the temperature on the keyhole

surface is given, the recoil pressure is calculated and imposed on the free surface as the boundary

condition.

Since the VOF method was explained in details in other works^' ^, the calculation procedure is

briefly summarized as follows: (1) absorbed laser power density on the keyhole wall is estimated

using the ray tracing method, (2) liquid and solid temperatures are calculated by solving the

energy equation, (3) recoil pressure is obtained from the surface temperature and imposed on the

free surface, (4) free surface profile and liquid flow velocities are calculated numerically using

the VOF method, (5) time is incremented and steps (l)-(5) repeated.

SUMMARY

Figure3 shows the real time x-ray images of the keyhole formation during 500W CO2 spot

welding of plain carbon steel. These images show how fast the keyhole is fluctuating and how

often the keyhole collapses during laser welding. The images in Figure3 were selected to

illustrate the keyhole dynamics.

1) 1.75ms 2) (1.75+0.22)ms 3) (1.75+0.44)ms

293
4) (1.75+0.66)ms 5) (1.75+0.88)ms 6) (1.75+1. l)ms

Figures : Keyhole behavior observed by X-ray image system

The first image was taken at 1.75ms after the beam impinged on the material, and then others

were taken every 0.22ms. This is the first keyhole formed during the weld. There is no pore at

the bottom of the keyhole in the first image. The keyhole was collapsed in the second image, and

a pore was trapped at the bottom of the keyhole after 0.22ms since the complete keyhole formed.

This was the first pore seen since the laser beam impinged on the edge of the sample. In the

third image the pore suddenly disappeared because the continuous laser beam broke through the

bridge and reopened the keyhole. The keyhole diameter increased and did not collapse in the

fourth image. The keyhole was still in a stable state.

In the fifth image the protrusion appeared on the wall of the keyhole. Simulation results of

shown later that the protrusion forms because of the collision the flow of the molten metals due

to the recoil pressure acting upward and the surface tension acting downward. The protrusions on

the keyhole walls were obviously growing toward the center of the keyhole and were almost

connected each other. This is the beginning point of the keyhole collapse. Finally, the bridge was

completely built and the pore was trapped at the bottom of the keyhole in the sixth image.

The complete keyhole was found 1.75ms after the laser beam impinged on the plain carbon steel.

After 1.97ms, the first keyhole collapse was observed and then the keyhole reopened by the

continuous laser beam irradiation after 0.22ms. The period of the periodical collapse time is

about 0.88ms.

The surface profile, flow velocities and temperature distributions corresponding to a 500W laser

power are shown in Figure 4. The recoil pressure is significantly higher and keyhole exists at a

time of 10ms. Simulation results between 10 and 13ms show molten metal oscillating upward

and downward along the keyhole wall, a mode which contributes to the keyhole instability.

Upward flow from the bottom of the keyhole, caused by the high recoil pressure at that location,

delivers heat and molten material to the periphery. The surface profile at the periphery becomes a

convex (e.g. at 13ms), increasing the inward pressure due to surface tension. This pressure

induces a downward flow, opposite to that induced by recoil pressure. As the opposing flows

collide at 13.2ms, an inward protrusion is formed near z = 0.1mm. This protrusion becomes

unstable, since the smaller keyhole radius at that location increases the inward radial component

of surface tension pressure. Hence, the protruding liquid is accelerated inward and the keyhole

collapsed shortly after 13.2 ms.

294
The keyhole profile fi"om the simulation at 13.2ms is similar to the keyhole shape from the x-ray

experiment at the fifth image in FigureS. Also, it takes 0.2ms for the keyhole to collapse since

the protrusion is generated in both cases, which are the x-ray videography experiment and the

simulation.

if

iti 01 64 a6 as 00 a a* m az oo ai a* at os on a 04 oii as

r axis (mm) r axis (mm) r axis (mm) r axis (mm)

10ms 12.5ms 13ms 13.2ms

Figure 4: Surface profiles, temperature, and flow pattern for 500W laser

That the cylindrical keyhole geometry is prone to instability of this type is not surprising.

Conceptually, the dynamics of the keyhole configuration are related to that of a submerged gas

jet in a liquid, first analyzed by Rayleigh.^ In that work, it was found that the cylindrical jet was

unstable when the wavelength of a varicose perturbation was greater than its circumference. The

maximum exponent, q, of the rate of increase in the amplitude of a varicose perturbation e^' was

calculated as

q = jo.696^

V P«

where a is the radius of the jet, y is the surface tension, and p is the density of the steel. For the

simulated keyholes, this amounts to the time constant of about 1.8x10"^ s. This growth rate

compares well to the time elapsed from protrusion formation to keyhole collapse in both the

500W keyhole simulations and the direct x-ray keyhole observation experiment of the 500

W

laser weld.

295
CONCLUSION

The direct observation of the keyhole dynamics by the x-ray transmission imaging system

showed the dynamic behavior of keyholes generated with a 500W CO2 laser beam. The keyhole

formation was monitored and instability of the keyhole was observed by the analysis of images.

Additionally the dynamic behavior of the keyhole was simulated and it was compared to the

experimental results. Surface tension caused growth of protrusions on the keyhole wall. This

caused instability of the keyhole and led to the collapse of the keyhole. In both cases the time for

the complete collapse of keyhole when the protrusion appeared was about 0.2ms. This number

shows reasonable correlation between the experiment and the simulation.

REFERENCE

1. Welding Handbook, Eighth Edition, Vol. 2. Miami: American Welding society, 1991:723-

725.

2. Shigeki Fujinaga, Hiroo Takenaka, Tom Narikiyo, Seiji Katayama and Akira Matsunawa.

Direct observation of keyhole behaviour during pulse modulated high-power Nd:YAG laser

irradiation. J. Phys. D:Appl.Phys. 33 492-497.: November 1999.

3. Akira Matsunawa, Jong-Do Kim, Naoki Seto, Masami Mizutani and Seiji Katayama.

Porosity Formation and Solidification Cracking in Laser Welding. Taiwan International

Welding Conference.: September 1998.

4. Milewski J O and Barbe M B 1999 Modeling and analysis of laser melting within a narrow

groove welding Welding J. 78 109-1 15s

5. Hirt C W and Nichols B D 1981 Volume of fluid (VOF) method for the dynamics of free

boundaries J. ofComputational Phys. 39 201-225

6. von Allmen M and Blatter M 1995 Laser-beam interactions with materials 2"^ ed (Springer)

7. Ko S H, Choi S K and Yoo C D 2001 Effects of surface depression on pool convection and

geometry in stationary GTAW Welding J. 80 39-45s

8. Fan H G and Kovacevic R 1999 Keyhole formation and collapse in plasma arc welding J. Phys. D: Appl. Phys. 32 2902-2909

9. Rayleigh J W S 1892 "On the stability of cylindrical fluid surfaces", Phil. Mag. s.5 v.34 177-

180.

296
FINITE ELEMENT MODELLING ON WELDING PROCESSES AND

WELDING PHENOMENA

B. Palotas*

ABSTRACT

The Finite Element Modelling is a wide applicable method in the field of welding as well. The

paper shows application examples of modelling of resistance welding processes, modelling of

cold pressure welding and some possibilities of FEM application.

The results of FEM can be used in the field of Computer Aided Process Planning applied in the

field of welding processes.

The application of Finite Element Modelling helps to understand the welding processes, the

welding phenomena and it is an effective tool of Welding Specialists and Researchers in our time.

KEYWORDS

Welding, Computer Aided Welding, Modelling of Welding, Finite Element Modelling

INTRODUCTION

The Welding Engineer is often faced with the task of developing a welding technology, after he

has first selected the welding procedure in accordance with the prescribed characteristics and the

geometrical requirements of the weld. Then it is necessary to calculate the course of the welding

process, the criteria for optimisation being the weld form, the quality stability and the weldability

of the material under pre-established physical, technical and economical conditions. The physical

processes should be described by natural laws, such as the conservation, transmission and

conversation of energy. The technical and economic conditions, on the other hand, such as time,

output and cost can be influenced. They therefore form the basis for the optimisation problem.

This complicated problem can be solved theoretically or empirically. Less and less time available

for project planning tasks makes it increasingly necessary to avoid empirical test methods and to

search for more effective methods of studying and developing welding technologies.

Automated preparation of the technological data increases the labour productivity of the engineer,

but basically does not change the quality of the design work. This is due to the fact that the

welding parameters are prescribed on the basis of empirical information.

Associate Professor, Department of Materials Science and Engineering, Budapest University of Technology and

Economics, Bertalan L. u. 7. Budapest, H-1 111, Hungary, Tel: +36 (1) 463 1115, e-mail: palotasb@eik.bme.hu

297
The quality of the design work can, however, be improved considerably using the theory of

welding process in tiiie form of certain mathematical models. These models should as far as possible contain conplete information about the process to be calculated. It would be

advantageous to create universal models for the form of the welds and the weld defects. The

software for the mathematical model is only produced once and can be rendered more precise as knowledge of the process to be studied increases (Ref 1.).

MATHEMATICAL MODELLING OF WELDING PHENOMENA

The welding phenomena should be written by differential equations given in the Table 1 . on the

basis of (Ref. 2.). The general solution of these equations can be given by Gauss-Ostrogradsky's

theorem:

^,ds=JdMdv = J||i44^ dx dy dz

dV (1)

where q is a flow through a Z surface ( flux of q ) when its source is written with its divergent

(div) in V volume bordered by Z surface. The equation (1) can be used for analysis of phenomena

occurring in solid, liquid, solid-liquid and gas agent during welding.

Physical process Process parameter Coefficient Flow

Heat conduction Ten5)erature, T Thermal conductivity, X Heat flowing

q = -X grad T

( Fourier's law

)

Diffusion Concentration, C Coefficient of Diffusion,

D

Mass flow

q = - D grad C

( Pick's law

)

Electrical field Potential, U Electrical conductivity, a Current density

j = - a grad U

( Ohm's law

)

Elastic deformation Strain, x Young's modulus, E Stress

a = E grad x

( Hoock's law

)

Viscous flow of

liquid

Flow velocity, v Coefficient of friction, \i Stress of friction

X = - |i, grad V

( Newton's law

)

Table 1. Base equations of welding phenomena

Unfortunately, the analytical solution of equations (Tab.l.) is very difficult and it requires a lot of

neglect and we have to know all the processes occurring during welding. Of course all the

processes are not known that occur during welding and negligence lead to information losses.

298
Due to these facts, we have to use numerical mathematical methods to solve the above-mentioned

equations (Ref. 3.).

One of these numerical methods is the Finite Element Method.

FINITE ELEMENT MODELLING OF WELDING

The processes occurring during welding are named coupled processes, because thermal, electrical,

electro- magnetic, mechanical, metallurgical and surface processes are to be taken into

consideration (Fig. 1.) (Ref. 4.), The heat source heats the material which heat causes thermal

processes in the material with mechanical response and changes the microstructures.

The first publications were about FEM modelling of stress and strain distribution of welded joints

and many articles can be found about this question for example (Ref. 4.), (Ref. 5.). At the time of

first FEM modelling the software and conputer possibility were limited, but in our time many

FEM Software exist and they are applicable in the field of welding successfully (for example,

ANSYS, SYSWELD, MARC).

In the following new results of FEM modelling will be shown, these acts of modelling were

carried out at the Department of Materials Science and Engineering of Technical University of

Budapest (new name is Budapest University of Technology and Economics ).

MODELLING OF PRESSURE WELDING PROCESSES

Pressure welding processes are generally fully mechanized processes, so the human factors do not

effect in the case of these processes. It means that, the Computer Aided Process Planning (CAPP)

is well applicable for these processes. CAPP requires probable equations based on physical laws.

The FEM modelling can give the basis of CAPP.

The FEM modelling of pressure welding processes helps understanding physics of them as well.

The simplest process is the cold pressure welding in the modelling.

Cold Pressure Welding

The cold pressure welding applies only force for jointing elements. When we use this welding

process means we use a cold forming process. Cold forming processes are widely modelled by

FEM. There are many software for FEM modelling of forming, for example MSC MARC

AUTOFORGE3.1.

We used this software for modelling of cold pressure but welding as well. The plastic deformation

processes and the condition of formation of welding joint can also be investigated by this

modelling.

The result of geometrical modelling of cold pressure welding of aluminium rods is given in the

Fig. 2. The computer with a high speed can calculate the main welding parameters as pressure

force and strain. This modelling should be become a theoretical basis of investigation of welded

joint formation. If we can give a mathematical equation for conditions of welding joint formation

then it will be a nice new technical result. We are working on this question nowadays.

299
If we heat the materials during welding then the processes are more complicated, but we could

model resistance welding processes by FEM.

Thermal bound, cond.

Heat source data

Thermodynamical prop.

Chemical composition

Austenitizing condition

Austenite grain size

Latent heat of transf

, Prop. = f(T,t)

I Deformation heat jhermal stress

Component geometry

Finite element mesh

Creep strain

ransf by stress

Transf = f(T,t)

Transformation kinetics

;-e curves

Transf strain

Transf plasticity

Mechanical bound cond.

Loading data

Mechanical properites

Hardness distribution

Figure 1 : Coupled processes at welding analysed by FEM

300
Figure 2. FEM model of cold pressure butt-welding

Another area of welding processes modelling is the FEM modelling of resistance welding

processes at our department.

Resistance Upset Welding

The FEM modelling of resistance upset welding was carried out by MARC/AutoForge 2.2.0

software. The detailed conditions of modelling and results can be seen in Ref 6. The strain

distribution is given in the Fig.3. and the pressure force requirement can be seen in the Fig.4. The

results of modelling can be used for welding parameter planning. The practice proved that the

results as pressure force and length change, were applicable, correct welding parameters.

The FEM modelling of resistance upset welding as pressure forming procedure is a new approach

of welding parameter modelling and it has international interest.

Resistance Spot Welding

The Ref 7. modelled the resistance spot welding, the result of temperature distribution in the

moment of current turned off can be seen in the Fig. 5. The results of Ref 7 , are in accordance

with international literature, the used software was the ANSYS 5.0.

The three-dimensional geometrical model can be seen in Fig. 6.

301
Inc : 35

Tifne ; 4.5679-04

ftngle: O.OOOb+00

a.l98et00

l.O78e*00

9.S83e-01

8.3876-01

5.9'

4.7'

3 .S94e-01

2.396Sr01

i.l9Se-01

Icasel

Total Equivalent Plastic Strain

Figure 3: Strain distribution results at resistance upset welding

Badg 3 Force X (xlOOOO)

01 2

I

1\5

\^

X 10 11X. -13 Xi 4

IS X

^2S

3233 34

Bodlj 3 Cos X

Figure 4: Pressure force requirement at resistance upset welding

302
ANSYS 5.0 A

OCT 28 1995

18t0e:38

PLOT NO. 1 NODAL SOLUTION

STEP-4 SUB -4 TIUE-1.3 TEUP SHN -286.414

SMX -53693

291

346

401

456

Bern 511

566

627 M 839

957 1063

1169

1275 BM 1381

1487

1593

r~i 1687

1 1 1753

1807

1861 SM 1912

1966

2027

Figure 6: A three-dimensional geometrical model of resistance spot welding

303
MODELLING OF HEAT AFFECTED ZONE

The new modelling area is the FEM modelling of heat-affected zone at our Department. Using the

FEM modelling the critical points of heat-affected zone can be found.

Fig. 7. shows the result of temperature distribution in a given case for demonstration that, the

FEM modelling can be applied for calculation of thermal cycles in the heat affected zones. From

thermal cycle we can predict the structure changing in the heat-affected zones, which is the basis

of mechanical properties prediction.

SEQV CAVG)

DHX a.04S388

SHH =525.197

Figure 7: Heat affected zone modelling by FEM

SUMMARY

The Department of Materials Science and Engineering at Budapest University of Technology and

Economics has experiences in the field of Finite Element Modelling of welding processes and

welding phenomena. The paper shows some result of FEM modelling as cold pressure welding,

resistance upset and spot welding and as heat affected zone modelling. The results of modelling

can be used in the Computer Aided Process Planning of welding processes.

CONCLUSION

The Finite Element Modelling is a powerful method for modelling of processes occurring during

welding as well. FEM modelling results of Materials Science and Engineering Department of

BUTE proved that the pressure welding processes could be approached as cold or hot forming

processes. The phenomena occurring in the heat-affected zone of welds can be also modelled by

Finite Element Method, which we are working on presently.

304
ACKNOWLEGEMENT

Students' work in the field of Finite Element Modelling is appreciated. Thanks a lot for work of

Istvan Borhy, Dmitrij Malgin and Balazs Keskeny.

REFERENCES

1. W. Sudnik 1991. Research into fusion welding technologies based on physicalmathematical models. Schweissen und Schneiden: 43 (10): 588-s to 590-s.

2. W. A. Sudnik, W. A. Jerofejeev 1986. Computer aided welding process calculations : 100

Tula: Technical University of Tula.

3. B. Palotas, I. Borhy 1996. Mathematical modelling of welding phenomena. Welding

Technology: 7 (2): 15-s to 23-s.

4. D. Radaj 1992. Heat Effects of Welding. Temperature Field. Residual Stress. Distortion:

348 Berlin Heidelberg: Spinger-Verlag.

5. Z. Dudas, N. Habib 1989. R&D work. Computer Aided Welding.- 261-s to 449-s

Budapest: lEC, TUB.

6. B. Palotas, D. Malgin 1999. Finite Element Modelling of Resistance Upset Welding.

Proc. 9 International Conference on Computer Technology in Welding Eds. T. Siewert,

and C. Pollock: 1 1 1-123. NIST Special Publication 949.

7. I. Borhy 1995. Modelling of resistance spot welding: Dt 3/1994 Budapest: TUB.

(Supervisors: Gy. Krallics, B. Palotas).

305

Session B4: Modeling IV

307

THREE-DIMENSIONAL SIMULATION OF ROBOT PATH AND HEAT TRANSFER OF

A TIG-WELDED PART WITH COMPLEX GEOMETRY

M. Ericsson*, P. Nyl^n**, G. Bolmsjo***

ABSTRACT

The applications of commercial software (OLP) packages for robot simulation, and programming,

iis interactive computer graphics, provide powerful tools for creating welding paths off-line. By

the use of such software, problems of robot reach, accessibility, collision and timing can be

eliminated during the planning stage. This paper describes how such software can be integrated

with a numerical model that predicts temperature-time histories in the solid material. The

objective of this integration is to develop a tool for the engineer where robot trajectories and

process parameters can be optimized on parts with complex geometry. Such a tool would

decrease the number of weld trials, increase productivity and reduce costs. Assumptions and

principles behind the modeling techniques are presented together with experimental evaluation of

the correlation between modeled and measured temperatures.

INTRODUCTION

The metallurgical structure of a metal, which determines its mechanical properties, is a ftinction

of its chemical composition, its initial structure and the thermal effects of the welding process.

Theoretically, if both the thermal events and the response of the material to the thermal process is

known, the resulting changes in microstructure and properties can be predicted. Several papers

have been published concerning numerical modeling of thermal histories, residual stresses, and

distortion (Ref. 1-7). Mainly two-dimensional studies have been performed. Three-dimensional

studies are still restricted to simpler shapes such as plates and pipes.

The use of robots for arc welding started in the early 70 's and is now extensively used in the

MIG/MAG processes. Using robots for TIG (GTAW) welding is however still rare. One of the

reasons is the increased demand for precise programming and control. Programming of welding

robots is usually done manually by the jog teach method. Using this method the robot is off-line,

the part stationary, and the robot arm jogged through the program under reduced power and at

reduced speed, via a joystick. Generating a path by hand in this way can be time consuming. On a

complex geometry, it is virtually impossible for a progranmier to maintain constant gun velocity,

distance from, and orientation to, the part. However, by using computer simulation this problem

can be overcome. Using this method, the programming is moved away from the robot to a

graphical computer system, often referred to as a "off-line programming" system (OLP). The

technology in this area is well established and has been a research area (Ref. 8-11) for some ten

years. Despite these extensive investigations, the two different simulation techniques (numerical

process modeling and OLP) seems only to have been studied separately.

The need for a better simulation tool for arc welding was the starting point for a research program

at the University TroUhattan/Uddevalla in collaboration with Volvo Aero Corporation. The

University Trollhattan/Uddevalla Box 957 S-461 29 Trollhattan Sweden

* Volvo Aero Corporation, S-461 81 Trollhattan, Sweden.

** Department of Production Engineering, Lund Institute of Technology, Box 118, S-221 00 Lund, Sweden

309
objective of this program is to provide temperature -time histories and metallurgical- and

mechanical-properties predictions on robot welded parts. The program is divided into four parts:

1 . to off-line program parts with complex shapes,

2. to numerically predict the shape of the molten pool by the use of Computational Fluid

Dynamics (CFD) techniques,

3. to numerically solve the energy equations in the solid material with sufficient accuracy that

metallurgical predictions can be made, as well as to link the off-line programming model with

this numerical model, and

4. to empirically establish relationships between temperature-time history and metallurgical and

mechanical properties

This paper is concerned with parts 1 and 3 above; namely methods of programming robots offline and of predicting temperature-time histories on parts with complex shapes.

PRINCIPLE OF OFF-LINE PROGRAMMING (OLP)

Several commercial software packages for off-line programming of robots exist (CATIA, IGRIP,

Robcad GRASP etc.). A brief description of the methodology using such systems is given below.

A more detailed description is given in (Ref 8). The methodology of OLP includes the following

steps (Ref 8):

1 . modeling of the work cell,

2. modeling of the work-piece,

3. calibrating the work-cell,

4. adjusting and fme-tuning up and down loading of programs,

5. programming, and

6. test runs and macro programming enhancements

The first step to model the work cell concerns the construction of a geometric and a kinematic

model of the robot, positioner etc. This demands access to design drawing of the cell together

with measurements of critical dimensions in the cell. The workcell model is usually constructed

directly in the OLP system. The IGRIP (Interactive Graphics Robot Instruction Program, Deneb

Robotics) system was used in this study. In the second step a geometrical description (CAD data)

of the part to be welded is generated either in a CAD/CAM software or in the off-line

programming software (OLP). If this model is created in a CAD system the data is imported to

the OLP software either using a neutral interface (for instance IGES) or a direct reader.

The accuracy of the modeled workcell is usually not high and the third step is therefor to make a

calibration by measuring different points in the physical welding cell. This procedure might

include several sub-steps depending on the conplexity of the workcell. In this work a tool

calibration and a calibration of the workpiece were performed. Tool calibration is performed to

determine the tool center point and to determine the orientation of the weld torch. The procedure

used in this study was to have a measuring arrow in a fixed position in the work cell and to move

the robot to this position in different directions. The positions from the real robot cell were then

uploaded to the OLP software and a "best fit" was performed by the system. The calibration of

the workpiece was performed similarly by moving the robot to clearly identified positions on the

workpiece. These positions were recorded and uploaded to the OLP software where the

310
difference between model and measurements was calculated and an adjustment of the model

using least squares fitting was done.

The motion of the robot is then programmed in steps four and five, either in a high level

programming language (for instance GSL, which is the graphical simulation program in IGRIP)

or in a specific robot language (such as RAPID, which is the program language for ABB robots).

A robot trajectory is then defined by a set of coordinate frames specifying locations and gun

orientation. After that, the motion may be simulated to check the results on the computer. High

level languages are then translated and the program finally downloaded to the robot controller

where in the final step, test runs are performed. Figure 1 shows a screen-capture during the

simulation in the OLP system.

Figurel: OLP model.

THE COMPUTATIONAL HEAT TRANSFER MODEL

The primary aim of the numerical finite element model is to predict the temperature evolution

outside the molten zone on a part with complex geometry. Here, the software ICEM CFD,

HEXA, which is a commercial pre-processor for CFD and structural applications, was used to

mesh the part. The commercial FEM program Marc from MSC Software was used in the heat

transfer predictions. User subroutines had to be developed to simulate the moving heat source. A

Gaussian surface distribution was used to model the heat source from the weld. This distribution

was preferred to a volumetric one (Ref. 4) since it reduces the number of parameters (unknown

variables) to be fit and because the plates to be welded were considered thin (<1.5mm) The heat

flux was expressed as (Ref. 4):

rjEIa

q = -e '

where q denotes the heat transferred to the workpiece, E the voltage, I the current, r| the

efficiency factor, ocq the concentration factor, and r the radial distance from the center of the heat

311
source. The distribution was truncated in the radial direction, at a cut off limit of 5% of the

maximal heat input, as proposed by D. Radaj (Ref. 4). Temperature dependent properties as well

as phase change was included in the analysis. The thermal conductivity was increased by a factor

10 when the temperature reached the liquidus temperature to account for connective heat transfer

in the melted zone (Ref. 4). This simplified model was used instead of more advanced CFD

models to simulate the physics in the molten zone, since the latter methods are too

computationally demanding. The part was divided into a non-uniform mesh with higher mesh

densities close to the weld path (where the highest temperature gradients were assumed to occur),

Figure 2. Eight-node brick elements were used. Six elements were defined in the thickness

direction. Grid sensitivity trials were made by successively refining the mesh. The final number

of nodes in the model was 181500.

Figure 2: The non-uniform mesh. Note the higher densities along the weld path

The boundary conditions for the analysis are given in Table 1 . The model had to be restricted to a

subsection of the part to be welded since otherwise to long computational times would have been

required. To compensate for this simplification a "Metal-Metal" boundary condition was

introduced which symbolizes pure heat conduction through these interfaces. Table 1

Table 1 : Boundary conditions in the heat transfer analysis

Type of condition Interface Value

Face film Metal - Metal lOOOE-6

Face film Metal - Air 20E-6

Face film Metal - Gas (Argon as root gas) 200E-6

Flux Metal - Heat source user subroutine ( q above)

The simulations were to time consuming to be run on a single workstation why parallel

computing on a Linux cluster was used. By the use of the parallel computing option in Marc, the

part is subdivided into several domains. For each domain, subsolutions are then calculated in

parallel on different processor, and an iterative procedure assembles the subsolutions to the global

solution. Ten 800Mz processors were used in the present study.

312
INTEGRATION OF THE HEAT TRANSFER AND OFF-LINE PROGRAMMING

MODELS

By the integration of the heat transfer model above with the off-line programming system IGRIP,

a powerful, yet efficient tool for tenperature prediction and optimization may be obtained. To

that end, an interface translating the data; robot coordinates, welding speeds and currents between

the two softwares was developed. This interface calculates a linear motion between each robot

point, which controls the moving heat source in the finite element calculation. The two softwares

(IGRIP and Marc) have to be installed on the same workstation since communication between

different operating systems has not been considered. The overall architecture of the simulation

system is given in Figure 3.

Simulation

program for the

robot motion

Robot CAD/

simulation CAM

Welding

path

Translator

Complete

robot code

IRB

Controller

Gooi^and

optimum weld

Geometry

e.g. IGES

FEA

Optimization

of I and V

I, V for full penetration,

microstructure, HAZ etc.

Figure 3: The overall architecture of the simulation system.

EXPERIMENTAL

TIG (GTAW) welding was performed using an in-house robotised welding cell. The torch used is

from Binzel AB and is linked to a six-axis robot from ABB, IRB 1400. The power source is a TIG

Commander 400 AC/DC from Migatronic AB. Throughout all experiments, thoriated tungsten

electrodes were used. Welding experiments were performed on both plane plates and on a section

of an aerospace part (in the following referred to as the production part), namely a turbine

component from the V2500 engine provided from Volvo Aero Corporation. 1/13 was cut out of

the real part, which originally consisted of an inner, an outer ring and 13 vanes, see Figure 4. The

reasons for the experiments on the plane plates were twofold: to calibrate the heat source

parameters used in the simulations, and to determine the emissivity in the infrared temperature

measurements. Both the plane plates and the production part were made of Greek Ascoloy with a

thickness of 1 .25 mm. To avoid oxidation, argon was used as root gas in all weld trials. The types

of welds performed were bead on plates, and no filler material was used. All plates were tacwelded together before performing experiments so that no gap or misalignments could be

introduced. Two special fixtures were designed, one for the plane plates and one for the

production part. Figure 4.

313
Figure 4: Fixture for the plan plates (left) and fixture with component (right).

To obtain temperature measurements, both thermocouples and high-resolution infi-ared (IR)

emission measurements were used. Six thermocouples were positioned perpendicular to the

welding direction. The first gauge was positioned as close to the melted zone as possible at a

distance of 4mm from the center of the weld. The second and third were positioned 0.5mm

radially from the previous one. The remaining gauges were positioned 1 .0mm radially from the

preview one. The sampling frequency for all thermocouples was 270Hz. The IR-camera used is a

VARIOSCAN High Resolution, from JENOPTIK, Laser, Optik, Systeme Gmbh, which works in

the IR radiation spectrum of 8 - 12)am. The camera was used both in a line scan mode with a

scanning frequency of 270Hz as well as in a full-frame mode with a frequency of IHz. The

analysis of the IR measurements were made using the IRBIS Plus software provided by

JENOPTIK. A comparison between the IR results with the thermocouple was made. Different

techniques to soot the plane plates were evaluated to reduce emissivity -dependency in the IRmeasurements. Finally a method using an acetylene flame was selected. This technique was also

used on the production part.

RESULTS AND DISCUSSION

The results of the robot programs made off-line showed a high accuracy and very little finetuning after calibrations had to be made. The method appears as a powerful tool, particularly in

small batch production such as within the aerospace industry.

The computational time from the parallel calculation was 34hrs for the production part. The

predicted fusion zone was 5.0mm on the top-side and 4.8mm at the root-side which agreed well

with measured widths.

The predicted and IR-measured temperature histories in the point located 4mm and 7mm from the

center of the weld are given in Figure 5. The IR-measurements were performed twice,

corresponding to the captions IRl and IR2 in the Figure 5. There is an excellent agreement

between predictions and measurements for the 4.0mm case both in peak temperature and in the

cooling history. The agreement for the 7.0mm case is not as good as for the 4mm case but still good. An example of the comparison between the thermocouple and the IR-measurements are given in Figure 6. There is a very good agreement, which implies that that the technique to soot

worked well. The reason why data is lacking in the IR-curve is that the camera-system collects

data during a maximum time interval of 20 seconds. This data then have to be written to disk

before a new sampling sequence can be gathered. A more extensive evaluation between

thermocouple and IR measurements is planned.

314
16 20 2S Tim* ()

Figure 5: Predicted and measured temperature-time histories, 4mm (left) and 7mm (right) from

the weld centerline.

O TO e

3

i^'^^^.^ Thermocouple

Figure 6: Comparison between thermocouple and IR measured temperatures.

The overall conclusion from the simulations is that the model predicts the thermal cycle very

well. However, further research is needed until welded structures can be optimized without

experiments. The temperature predictions are naturally dependent on the heat transfer coefficients

(boundary conditions in Table 1). To determine these values, experiments are required since the

heat flow can be convection dependent, specifically if the workpiece and fixturing are small.

Also, the heat source parameters (in the expression for q above) have to be calibrated by

experiments. The on-going work at the laboratory at University Trollhattan/Uddevalla to

numerically model the magnetohydrodynamics of the arc and to predict the shape of the molten

pool by the use of Conputational Fluid Dynamics (CFD) techniques seems as a promising tool to

compute the heat source parameters without experiments. Such a model will establish a direct

relationship between the welding current, speed, voltage and the shape of the molten zone, which

can be used as a boundary condition in the temperature predictions in the solid region. Such

model will also increase the knowledge of the stirring of the weld pool, the weld pool surface

shape and the physics of the arc. Several extensions of the modeling work described in this article are possible. The simulations

can be extended to compute residual stresses, distortion and in the longer term to predict fracture

strength and fatigue life of a structure. To extend the modeling work to include filler wire and

pulsed current would also be valuable.

SUMMARY AND CONCLUSIONS

An engineering method and a simulation tool to define robot trajectories and to predict thermal

histories on parts with complex geometries have been developed. The method was evaluated on a

part with a complex shape where robot weld paths were defined off-line, and automatically

315
downloaded to a FEM-model where transient temperatures were predicted. These predictions

were compared with experimental measurements using both thermocouple and infrared emission

measurements and good agreement was found. The described method provides a promising

means to construct and optimize torch trajectories and process parameters off-line. Using this

system, thermal histories can be predicted on complex shaped parts and thereby resulting changes

in microstructure and mechanical properties be estimated. The models used may after futher

development enable the optimization of welding processes, thus increasing productivity and

reducing the need of weld trials.

ACKNOWLEDGEMENT

The authors wish to acknowledge the guidance in the temperature measurements of Per

Henrikson (Volvo Aero Corporation), and the assistance in the laboratory by Xavier Guterbaum

(University of Trollhattan/Uddevalla) and Borje Nordin (Volvo Aero Corporation). Appreciation

is expressed to Peter Jonsson of Volvo Aero Corporation for providing samples for this research

and to Anita Hansbo of University of Trollhattan/Uddevalla for linguistic revision. The work was

funded by the Foundation for Knowledge and Competence Development and EC Structural

Founds.

REFERENCES

1 . Eagar T. W., Tsai, N. S. 1983. Temperature Fields Produced by Traveling Distributed Heat

Sources. American Welding Societv Journal 62(12) 346-s to 355-s.

2. Gu, M.; Goldak, J.; Hughes, E. 1993. Steady state thermal analysis of welds with filler metal

addition. Canadian Metallurgical Ouarterlv. 32 (1): 49-s to 55-s.

3. Kou, S.; Le Y. 1983. Three-dimensional heat flow and solidification during Autogenous GTA

Welding of Aluminum Plates. Metallurgical Transactions A. 14A: 2245-s to 225 3-s.

4. Radej, D. 1992 Heat Effects of Welding: Berlin: Springer Verlag.

5. Goldak, J.; McDill, M.; Oddy, A.; House, R.; Chi, M.; Bibby, M. 1987. Computational Heat

Transfer for Weld Mechanics. Proc. of Int. Conf on Trends in Welding Research. Advances

in Welding Science and Technologv. Eds S. A. David: 15-20. Metals Park ASM Int.

6. Jonsson, M.; Karlsson, L; Lindgren, L.E. 1985. Deformation and Stresses in Butt Welding of

Large Plates with Special References to the Material Properties, J. of Eng. Mat. And Tech.

107: 265-s to 270-s.

7. Lindgren, L.E.; Karlsson, L. 1988. Deformation and Stresses in welding of Shell Structures.

Int. J. for Numerical Methods in Eng. 25: 635-s to 655-s.

8. Bolmsjo, G.; Olsson, M.; Brink, K. 1997. Off- line programming of GMAW robotic systems - a case study. Int. J. for the Joining of Materials. 9 (3): 86-s to 93-s.

9. Buchal, R.O.; Cheras, D.B.; Sassani, F.; Duncan J.P. 1989. Simulated Off-Line Programming

of Welding Robots. Int. J. of Robotics Research 8 (3): 31-s to 43-s.

10. Bolmsjo, G. 1999. Programming robot welding system using advanced simulation tools. Proc.

of the International Conf on the Joining of Materials JOM-9. 284-291. May 16-19, 1999,

Helsing0r, Denmark.

11. Walter S. 1994. Simulation and Calibration for Off-line Programming of Industrial Robots.

Proc. of Computer Technologv in Welding: Paper 54. Paris 15-16 June.

316
DYNAMIC MODELING OF GTAW FOR RAPID MANUFACTURING

Z. Jandric, I. S. Kmecko, R. Kovacevic'^

ABSTRACT

A new parameter referred to as the geometrical factor was defined to reflect on the geometrical

configuration around the molten pool, and an expression for heat conduction dissipation through

the workpiece was established. The weld bead width, height, depth of penetration, and the weld

bead cross-sectional area, were experimentally determined for different combinations of the

welding currents, workpiece velocity, the filler metal flow rates, and for different geometrical

configurations of the weald bead. The open-loop controller for the gas tungsten arc welding

(GTAW) process based on the developed model was designed and experimentally tested by

controlling the metal layer deposition process on top of a workpiece with complex geometrical

features, such as conformal channels, edges, and comers.

INTRODUCTION

Rapid prototyping (RP) is the most common name given to a host of related technologies that are used to fabricate physical objects directly from CAD data sources. These methods are unique in

that they add and bond material in layers to form objects. Such systems are also generally

known as Freeform Fabrication (FFF), solid freeform fabrication (SFF) and Layered

Manufacturing. The major advantages of the RP technologies are that:

- Objects can be formed with any geometric complexity or intricacy without the need

for an elaborate machine setup or final assembly;

- Rapid prototyping systems reduce the construction of complex objects to a

manageable, straightforward, and relatively fast process.

The materials used in rapid prototyping are limited and dependent on the production method

chosen. Non-metal materials such as plastics, ceramics, waxes, and papers were almost

exclusively used for many years. Parts made of these materials can not be used to test if the

design of the parts meets the mechanical property requirements. Deposition by welding (DBW)

enables the direct production of the metallic parts. The use of welding for creating free-standing

shapes was first used in Germany in the 1960's (Ref. 1). Other work in this area has been

undertaken by Babcock and Wilcox Co. who were working mainly on large components

produced in an austenitic material. Also, work by Rolls Royce has centered on investigating the

technique as a means of reducing the waste levels of expensive high performance alloys that can

occur in conventional processing. Lately, work on 3D welding has also been in progress at the

University of Nottingham, the United Kingdom and Southern Methodist University, Dallas,

Texas.

Research Center for Advanced Manufacturing

Southern Methodist University, 1500 International Pkwy. Suite #100, Richardson, TX 75081

•Corresponding author - tel.: 214-768-4865, Email: kovacevi@engr.smu.edu

317
Gas tungsten arc welding has many properties that makes it very attractive for the DBW such as: the wire feeding rate does not depend on the welding current; it is possible to completely stop the

deposition process while keeping the arc on; the wire can be fed directly to the molten pool to let

the surface tension to evenly spread the liquid metal; and the presence of the previously

deposited bead will not affect the stability of the arc, etc. To make GTAW even more suitable

for DBW, a constant heat input and cooling rate should be maintained during the deposition

process in order to achieve a homogeneous crystallization and transformation structure

throughout the whole workpiece mass, and to obtain the uniform geometry of weld beads across

the deposited layer.

As mentioned, a major advantage of RP is the building of the 3D part with the complex

geometrical features. Depositing layers above the complex geometrical features directly affect

the heat transfer conditions, since the surrounding mass of the material is rapidly changed with

the X-Y coordinates. Though, when the weld bead is deposited near the edges or above the

channels, any excessive heat input will melt the underlying geometry together with a filler metal - the shape of the 3D part will be destroyed. It is not possible to successfully use the same heat

input for depositing the weld bead over the changing volume of substrate. Clearly, heat input is

strongly correlated to the geometry of the part (Ref 2). In order to keep a uniform cooling rate,

and subsequently, the uniform mechanical properties of the welded substrate, the heat input must

be adjusted for each instance according to the mass of the surrounding material. A new

parameter referred to as the geometrical factor (^q) was designed to reflect on the geometrical

configuration around the molten pool. Since the geometry of the built part is known, the

geometrical factor can be calculated in advance and that represents the base for the open-loop

controller.

To verify the correlation between the geometry of the part and the weld-bead width, height,

depth of penetration for different combinations of the welding currents, welding speed, and the

filler metal feeding speed, the number of experiments were designed and performed. An

accurate dynamic model and an expression for the heat conduction dissipation through the

workpiece were established. A number of researchers (Ref 3-8) modeled the welding process

without taking in consideration the geometry of the substrate. In this paper, an expression is

presented that balances the mass and energy flow through the controlled volume set around the

molten pool including the net heat input, filler metal feeding speed, welding speed, and

geometrical factor. The open-loop controller based on this model is experimentally tested, by

controlling the metal layer deposition process on top of the workpiece with complex geometry.

EXPERIMENTAL SET-UP

Figure 1 shows the experimental set-up. A

torch is equipped with a step motor (1) in

order to solve the problem of omnidirectionality, the filler metal has to be fed in

front of the torch. The supply of the filler

metal is provided by the step motor (2). The

PC based data acquisition system controls the

power source, wire-feeding speed, and

orientation of the wire feed mechanism.

To PC

Fig. 1 Experimental Set-up

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Communication of the central controller and the CNC machine is established so that the

movement of the CNC machine table and the orientation of the wire feeding mechanism are synchronized. A high resolution CCD color camera is integrated coaxially with the torch.

EXPERIMENTAL PROCEDURE

To determine the correlation between the weld-bead geometry and the welding current, the

welding speed, and the filler metal feeding speed, for different geometrical configurations, a

number of experiments are performed. Weld bead geometry is defined by the bead width (wb),

the depth of penetration (dw), the height of buildup (bb), and by the cross-sectional area of the

bead (A). Additionally, the cross-section area of the bead can be divided into two areas: the

above-surface (Ab) and below-surface (Aw) areas, where the surface denotes the top plane of the

workpiece before welding. Two sets of experiments are designed. The first set (Fig. 2)

correlates the weld-bead geometry with the welding current and the filler metal feeding speed -

where the surrounding mass of the material varies between 12.5% and 100% of the maximum

possible one. Experiment I is done for nine combinations of the welding current (80, 120 and

160 A) and the filler metal feeding speed (40, 80 and 120 cm/min). The second experiment (Fig.

3) is designed to correlate the weld-bead geometry with the welding speed, the welding current,

and the filler metal feeding speed. Nine experiments are performed too (combinations of 100,

120 and 160 A; 40, 80, 120 cm/sec). In both cases during a single experiment, the filler metal

feeding speed and the current are kept constant. While in the second experiment, the welding

speed was increased.

Fig. 2 Experiment I Fig. 3 Experiment II

EXPERIMENTAL RESULTS

After welding, each sample was cross-sectioned using an abrasive cut-off wheel, polished to a 1-

|j.m finish, and etched in a 2%-Nital solution. The workpiece from the first experiment is cut

through the A-A, B-B, C-C, D-D, E-E and F-F cross-sections (Fig. 2). The cross-sections for the

second experiment were done along the dotted line in Fig. 3. A few images of the cross-sections

from the first experiment for the wire feeding speed of 40 cm/min and for welding current of 80,

120 and 160 A, are shown in the Table 1. The individual cross-sectional areas of the melted

substrate and deposited filler metal were then measured. Figures 4a, 4b, and 4c present the mean

values of the bead widths, depths of the penetration and the height of bead build-ups as the

function of the geometry. Experimental data are approximated with linear functions. It is

interesting to note that the underlying geometry basically has no influence on the height of the

build-up. A comparison of the maximum and minimum values from the Figs. 4a and 4b show

that the impact of the substrate geometry on the DBW can not be neglected. Further, some

319
cross-sections of the second experiment are presented in the Table 5. Although the experimental

data fluctuates, it is easy to conclude from the data presented in the Table 5 that the workpiece

velocity has a great influence on the bead parameters. In the summary, experimental results

showed that the 'geometricalfactor' has to be included in the modeling of the rapid prototyping

process based on the welding.

Table 1. Cross-sections ofthe beadfor the different geometricalfactors

a) bead width b) depth ofpenetration c) bead build-up

Diagrams 4a, b and c - influence ofthe geometry on the bead

Table 5a. Cross-sections ofthe beadfor the second experiment

workpiece

velocity

1.27-10"^[m/s]

3.8M0-lm/s]

6.35-10-lm/s]

100/40

I [A] / Vf [cm/min]

100/80 100/120 120/40

320
MATHEMATICAL MODEL

An accurate dynamic model that provides guidance for sensing and corrective actions is needed

for on-line adaptive control. The model provides a quantitative analysis of the thermal behavior

of the welding process and aids in a fundamental understanding of the influence of the welding

current, the filler metal flow rate, the workpiece velocity, and the geometrical factor. An

expression that will combine the net heat input, the filler

metal feeding speed, the traverse speed, and the heat

conduction rate may be obtained by balancing the mass and

energy flow through the control volume set around the molten

pool as shown in Fig. 5.

Fig. 5 Control volume

Segment A-A of the controlled volume is positioned through

the largest cross-sectional area of the liquid metal in the

molten pool. By balancing the mass flow through the

controlled volume, the maximum flow rate of the liquid metal

may be calculated as the sum of the filler metal mass flow rate, , and the maximum molten

substrate flow rate, . Then, the balance of energy at steady state is:

(1)

where the ho is the specific enthalpy of the filler metal, and the substrate metal entering the

controlled volume, huq is the specific enthalpy of the liquid metal leaving the control volume,

and is the net heat input and the conduction heat flow rate through the substrate. The balance

(1) can be rewritten as: a„=e/+a+4 (2)

The net heat input Q^^ is used for melting filler metal, , for melting the substrate material,

Q^, and for the heat dissipated by conduction, . The heat used for heating and mehing the

filler metal is calculated as:

(3)

where df is the filler-wire diameter, Vf is the filler-wire velocity and \^-hQ=\2-\(f J I for

carbon steel. The heat-flow rate used to heat and melts the substrate material, Q^, is:

(4)

where is the volumetric flow rate of the molten substrate metal. The heat conduction rate Q,^

is calculated as (Ref. 10):

4=2.217-^,G —[K-K)- la

0.9666 (5)

321
where is the geometrical factor, a is the temperature difflisivity, and Wb is the bead width.

The net heat input is used to calculate the welding current, I:

1

2-b,

bA -bo (6)

where bo = 7.952 and bi = 0.02117 are non-dimensional experimentally obtained constants (Ref.

10), and riarc ~ 0.48% is the arc efficiency for GTAW of mild steel.

GEOMETRICAL MODEL

The calculation of the influence of the geometry on the DBW is achieved in the four steps:

1 . creating solid model

2. creating 3D matrix

3. calculation of a geometrical factor

4. generation of the 2D matrix for each layer to be deposited.

Thefirst step is designing the 3D solid model (Fig. 6) by computer aided design (CAD). The 3D

solid part should have all the details - same as a wanted part (Fig. 7), so that the model is as

realistic as possible.

The second step creates the 3D matrix that consists only of the 'ones' and the 'zeros' (Fig. 8).

'Ones' mark the point where the material is supposed to be deposited, and 'zeros' mark the point

where the material should be removed or does not exist. The 3D matrix is determined by its

resolution defined as the number of the points 'n' within the unit length '1'.

Fig. 6 3D Solid model Fig. 7 Built 3Dpart Fig. 8 Part as a matrix of 'ones ' -fdled

circles and 'zeros ' - empty circles

The third step is conputationally most demanding. For each point in a 3D matrix geometrical

factor, ^G, is calculated. The calculation of the geometrical factor is based on the fact that the

heat affected zone, when it is welded on a semi-infinity solid is theoretically half of the sphere

(Ref 8). The size of the radius (R) of the half-sphere is experimentally obtained. The center of

the sphere is the point for which the geometrical factor is to be calculated with the coordinates

Xc, yc, Zc). Then, all the points in the range (xc±R; yc±R; Zc-R) are checked for their distance (d)

from the half-sphere center according to:

322
d = yjixc - xY +{yc-yy+ {zc - n

d<R

(7)

where, x, y and z are the coordinates of an arbitrary point. All the points that satisfy d<R are counted, where counter is denoted by 'Vmax' In addition, all points with value '1' that satisfy

d<R are counted with the counter denoted as'F. Now, the geometrical factor can be defined as: 4 = (8)

where, the geometrical factor is the ratio of the volumes, where 'Vmax' is constant and depends

on the predetermined radius of the half-sphere, and 'V is the amount of the material that exists

in the observed half-sphere. Here, the volume is digitized; and so, the model error is inherited.

But it should be noticed that this is an off-line heat input planning model that allows us to

increase the matrix resolution up to the virtually any value, if it is concluded that the model is not

precise enough.

The fourth step extracts all geometrical factors from the

3D matrix with the same z coordinate in a unique 2D

matrix that will be used when the layer with a z

distance from base-plate is to be deposited. As an

example a few diagrams with the calculated values of

the geometrical factors along the directions A-A, B-B

and C-C (from Fig. 9) are shown in the Figs. 10a, 10b

and 10c, respectively. These diagrams are obtained for

100»/o

80%

60%

40%

20%

Fig. 9 Welding over the channels

^1

a) deposition at A- A

Time Time c) deposition atC-C

Time b) deposition atB-B

Fig. 10 Calculated geometricalfactor

the top layer, where the bottom of the channels is sufficiently remote and doesn't have any

influence on the geometricalfactor. Results of the welding can be observed in the Fig. 11.

Since two separated controllers are involved, one of the

most challenging problems is to synchronize the

welding speed and update the heat-input (current). In

order to coordinate the work of these two controllers, a

new software based on the LabVIEW is developed.

This software communicates through the PC's serial

port with a CNC controller. The CNC controller sends

the signal whenever the workpiece is about to change Fig. 11. Workpiece cross-section

the direction. Instantaneously the software receives the signal (max. delay is 1msec), reads the

323
appropriate values from the 2D matrix,

and calculates the analog output signal

for the data acquisition board, according

to the Eq. 7. Fast software is necessity

since even very small delays can bring

about the overheating and damage the

part. In addition, this software controls

the filler metal feeding speed, the motion

of the torch, and monitors (record) the

welding current and voltage by sensing

through-the-arc. Recorded analog signals

of the current and the voltage are shown

in the Fig. 12. It should be noted that in

the intervals where the calculated

I [A]

120

100

80

60

40

E[V]

Time

Fig. 12 Values ofthe welding current and voltage as afunction ofthe geometry ofthe substrate

geometrical factor has zero values, the real value of the current is not zero. That value presents

the energy needed for melting the filler metal, and it is taken into the account through the

expression for the heat conduction dissipation.

CONCLUSION

A new parameter referred to as the geometrical factor was introduced to reflect on the

geometrical configuration around the molten pool, and an expression for the heat conduction

dissipation through the workpiece is established. The weld bead width, height, depth of

penetration, and weld bead cross-sectional area, are experimentally determined for different

combinations of welding current, workpiece velocity, the filler metal feeding speed, and for

different geometrical configurations of the weld bead. The open-loop controller for GTAW

process, based on an accurate dynamic model is designed and experimentally tested. The

controller shows very robust performance and fast response speed. Future work will include the

development of a closed-loop controller that should enhance the versatility of the demonstrated

open-loop controller.

ACKNOWLEGEMENT

This work was financially supported by THECB, Grant No. 003613-0022001999, NSF, Grants

No. DMI - 9732848 and DMI - 9809198, and the U.S. Department of Education, Grant No.

P200A-80806-98.

REFERENCES

1. Kovacevic, R., and Beardsley, H., (1998). Process Control of 3D Welding as a Droplet-Based Rapid

Prototyping Technique. Proceedings of the P"" Annual Solid Freeform Fabrication Symposium, August 10-12,

Austin, TX, pp. 57-64.

2. Million, K., Datta, R., Zimmermann, H., (1986). Method of manufacture of a design piece by form creating

deposition welding and a design piece manufactured by this method, European Patent Office, Publication

number: 0 172 309 Al.

3. Christensen, N., Davies, V.L. and Gjermundsen, K. (1965). Distribution of Temperatures in Arc Welding.

British Welding Journal. Vol. 7, pp.54-74.

4. Ghent, H.W., Roberts, D.W., Hemance, C.E., Kerr, H.W. and Strong, A.B. (1979). Arc Efficiencies in TIG

Welds, in Arc Physics and Welding Pool Behaviour. The Welding Institute, Cambridge, pp. 17-23.

324
5. Fuerschbach, P.W. and Knorovsky, G.A. (1991). A Study of Melting Efficiency in Plasma Arc and Gas

Tungsten Arc Welding. Welding Journal (Miami, Fl.). Vol.70, No.l 1, pp.287s-297s.

6. Kannaty-Asibu, E. Jr. (1997). Milestone Developments in Welding and Joining Processes. Journal of

Manufacturing Science and Engineering. Vol. 119, pp. 801-810.

7. Rykalin, N.N. (1957). Berechnung der Warmevorgange beim Schweisen. Verlag Technik, Berlin, pp. 68-69, pp. 100-105.

8. Rosental, D. (1946). The Theory of Moving Sources Of Heat And Its Application To Metal Treatments.

Transactions ofASME. Vol. 68, No.l 1, pp.849-866.

9. Radaj, D. (1992). Heat Effects of Welding. Springer-Verlag, Berlin/Heidelberg/New York.

10. Kmecko, S.I., Jandric, Z., Kovacevic, R. (2001). Influence of Geometrical Factor on Heat Transfer Rate During

GTAW RP. Accepted for the International Mechanical Congress, Nov. 11-16, 2001, New York, NY.

325

THREE DIMENSIONAL TRANSIENT HEAT TRANSFER ANALYSIS FOR GTA

WELDING PROCESS

Min Jou

ABSTRACT

It has been well recognized that the geometry of weld pool plays a fundamental role in

determining the mechanical properties of weld joints. The major variables in GTA welding

affeting on the formation of weld pool including welding current, voltage, welding speed, arc

length, and electrode tip angle. Besides these variables, GTA welding is subject to numerous

disturbances and uncertainties within and between welding cycles. These can arise from:

workpiece thickness tolerances, fluctuations in welding current, voltage, welding speed, arc

length, and root opening. Any or all of these complicate automation, reduce weld quality, and

drive up production cost. For this reason, studying interaction and correlation of process

variables, disturbances and uncertainties affecting on the formation of weld pool for GTA

welding remains a major challenge and goal.

In this research, a series of experiment has been conducted to investigate the interaction and

correlation of welding current, voltage, welding speed, and arc length affecting on the formation

of weld pool. The arc length affecting on arc efficiency and heat distribution parameter are also

examined and addressed in this paper.

In addition to experimental study, a three-dimensional finite element model has been

developed to analyze transient heat flow and to predict the formation of weld pool. The

correlation between the parameters including welding current, voltage, welding speed, arc

length, open-loop response and the characteristic geometry of weld pool are established. The 3-D

FEM can calculate not only the transient thermal histories but also the sizes of weld pool in

single-pass arc welding. In order to obtain good weld quality, this model will determine the

effect of arc length to the formation of weld. Furthermore, the effects of welding parameters on

the Gaussian heat source parameters (arc efficiency and heat distribution parameter) are also

studied. The experimental calibration and verification are carried out to verify the numerical

model. Experimental data are consistent and in quantitative agreement with values from FEM

simulations.

KEYWORDS

Weld pool size, Three-dimensional model, Transient heat transfer. Welding process variable,

Process variation. Open-loop response, Arc length

* The author is in the Department of Industrial Education at the National Taiwan Normal University, 162, Section 1, Hoping East Road, Taipei City 106, Taiwan.

327
INTRODUCTION

Gas tungsten arc welding is the most useful welding process used in cases which require

higher weld strength joint such as aerospace, nuclear, large structure and automotive industries.

The weld strength is affected by the geometry of weld and thermal characteristics. In order to

create desired weld strength, the weld cross section geometry (i.e., such as weld bead width,

weld depth, weld front width, and weld heat-affected zone) must be developed accurately (Figure

1).

The key geometric attribute of a major class of welds is the depth of penetration since it is a

direct indicator of weld strength. However, direct measurement of this quantity is quite difficult

since the depth is not directly visible. Several investigator [Refs.1-4] utilized reflection

ultrasound methods to measure the size of weld pool, but this technique is not very accurate and

unapplicable because of the problem of diffusion of the signal and the distortion caused by steep

temperature gradients. The other methods have been studied to indirectly detect the pool shape

based on the concept of pool oscillation [Refs.5-7], but it has been proved by Zacksenhouse and

Hardt [Ref.8] that it is not practical to detect the pool shape in partial penetration using the

concept of pool oscillation. The surface waves can only be correlated to bulk fluid oscillation for

full penetration welds. In a recent report, an effort to study the pool oscillation on a stationary

welding pool was made by Xiao and Ouden [Ref.9]. The case of partial penetration as well as

full penetration was performed in this research but not in a practical welding situations which is a

moving welding pool. Other Studies have been conducted to correlate the depth of penetration to

variables which can be measured during welding including arc voltage sensing[Ref.lO], x-ray

sensing[Ref.ll], video[Ref.l2], acoustic sensing [Ref.l3] and surface radiation

techniques[Ref.l4]. Every methods offers specific advantages, But each of these methods

contain drawbacks such as signal disturbances from high temperatures, contact sensing devices,

sensing performance corrupted by workpiece thickness or preheat changes, and gave no details

on the actual pool shape. These drawbacks have limited their applicability in practical welding

situations. Infrared sensing techniques have been studied as a possible sensor to detect weld

penetration [Ref.15-17]. In order to develop a sensor based on infrared sensing that can be easily

implemented in practical welding applications. The development of a mathematical relationship

among different welding conditions (i.e., varied current, welding speed, material types, electrode

tip angle, . . .etc.), measurable signal, and size of weld pool becomes an important research goal.

The temperature history of the welded components has a significant influence on the

residual stresses, distortion and hence the fatigue behavior of the welded structure. A large

amount of research has been dedicated to the simulation of welding process in the past few

decades. However, since the welding process causes complex mechanical, electric and

metallurgical changes in weldments, the accurate solution of mathematical model, which is

capable of addressing all the issues including material nonlinearity, phase transformation,

magnetodynamic effects, fluid flow in weld pool, etc., is practically intangible. To solve the

problem, the numerical methods, particularly the finite difference methods and the finite element

methods, are widely used for the simulation of welding process and the resultant residual strain

and stress fields. A quick review of literatures reveals that there are primarily two approaches in

the simulation of welding process. One approach is to solve the decoupled heat transfer equation

and Navier-Stokes equations so that the temperature distribution and the details of fluid flow in

328
I

weld pool can be obtained. The typical examples following this approach can be found in

Kovacevic, el al.[Ref 18] and Cao. et al, [Ref 19], where the effects of changing welding

parameters on the characteristic geometry of weld pool is analyzed by using three dimensional

finite difference method for transient analysis. More examples can be found, to name a few, in

Zacharia, et.,[Ref.20,21] and Kim and Na [Ref22]. Another approach for the simulation of the

welding process is to solve the heat transfer equation directly with the consideration of

temperature-dependent material properties and phase transformation. Due to the simplicity, yet

the acceptable accuracy, of this approach, many numerical models have been developed based on

it, especially when the resultant residual strain and stress is of the particular interest. The

examples can be found in Tekriwal and Mazumder [Ref23], Mahin, et al,[Ref24], Brown and

Song [Ref.25], Taljat, et al,[Ref26] and Bonifaz [Ref.27]. Typically the heat flux input from arc

is assumed to follow Gaussian function or double ellipsoidal model[Ref.27].

This research is focused on the critical single-pass bead-on-plate GTA welding process.

Three dimensional finite element model for the simulation of transient heat transfer is adopted in

our analysis in the sense that the simple 2-D model captures less information of the characteristic

sizes of the weld pool than 3-D model does. The accuracy of the 2-D model has also been

questioned by Brown and Song[Ref25] and Bonifaz[Ref27]. The aforementioned second

approach is employed in our finite element model, and the effect of fluid flow in the weld pool is

approximated by the temperature-dependent material properties. The distribution of the moving

heat source generated by the arc is assumed to be 2-dimensional Gaussian function. The

experimental calibration and verification are carried out to verify the numerical model. The

correlation between the parameters of the GTA welding process, including torch speed, electric

current and voltage, and the characteristic geometry of the weld pool are established. The major

parameters which will affect the pool size are: arc length, current, speed, voltage, and vertex

angle of electrode. However, the electrode is installed and cannot be changed during welding in

automatic welding. So in this research we will not study the effects of electrode vertex angle.

EXPERIMENTS

A Hobart TIGWAVETM 350 AC/DC welding system (Figure 2) was employed for the gas

tungsten arc welding in this study. The water-cooled torch was manipulated by a X-Y positional

table. The electrode was made of EWTh-2 with 3.2 mm diameter and 120 (degrees) included tip

angle. The electrode extension from the nozzle (with a diameter of 1 1 mm) was 3 mm. Shielding

gas with a flow rate of 10 liters/min was industrial pure argon (99.99%). Preparation of test

coupons with dimensions of 130 X 130 mm were made of SUS304 (Type 304) austenitic

stainless plates 6mm and 4mm in thickness. Prior to welding, the coupons were sanded with

#200 sand paper and cleaned with acetone. But welds then carried out down the center of the

plate. The samples were mounted on the flat surface of the plateform. During welding, the

coupons were free of restraint. The joint setup was autogenous bead-on-plate welding. Welding

parameters influence the quality of welded joints. Therefore, the determination of these

parameters is an important step in this study. The criteria for selecting the optimum welding

parameters include the external appearance of the weld, fusion geometry and penetration. To

study the effects of welding parameters on the weld geometry, the tested coupons were sectioned

perpendicularly to the welding direction for metallographic examinations. The sectioned samples

329
were mounted, sanded using sand papers, and polished using AI2O3 (0.05 micron) colloidal

solution. Samples were cleaned before etched with a solution with 10 g CuS04 + 50 ml HCl +

50ml water. Samples were observed under an Olympus BX60M microscope to determine the

weld profile.

In order to verify and calibrate the 3-D FEM model, experiments were performed to

investigate the interaction and correlation of process variables and variations affecting on the

formation of weld pool for GTA welding. The experimental results as shown in Figure 3,

indicate that: (1). increasing the welding current increases the weld penetration; (2). increasing

the welding speed decreases the weld penetration. The open-loop response is the key parameter

for development of control system. Therefore, experiments were conducted to realize how the

open-loop response will be affected by the controllable variables which is the welding current

and welding speed. Figure 4 shows that increases input current magnitude speed up open-loop

response. Increasing welding speed will slow down open-loop response as shown in Figure 5.

Figure 6 indicates that welding speed has more effect on open-loop response than welding

current.

Welding process anomalies occurs quite frequently when welding production sheet metal

parts. The most significant effect of process anomalies is that it results from variation of arc

length. The condition arises when the metal parts are not flat, when contours do not match

dies/fixtures or one another precisely, when ripples or wrinkles are present, when tip of tungsten

electrode is melt due to high temperature. In order to study the effect of variation of arc length on

the formation of weld pool, welding on stepped dovm plates was performed (i.e., arc length

increases from 2mm to 4mm) as shovm in Figure 7. Experimental data indicates that increasing

the arc length decreases weld penetration under constant welding speed as shown in Figure 8.

Figure 9 show that increases arc length decreases weld penetration under constant welding

current. This indicates longer arc length will make the arc efficiency lower.

The arc length affecting on weld surface width is also examed. The results presented in

Figure 10 and 11 show that the surface width slightly increases with increasing arc length. The

longer arc length results in greater expansion of the arc. Therefore, the surface width increases

with arc length increased. However, the surface width decreases with arc length increased when

welding at lower current. The reason is that higher heat input (i.e.,higher welding current)

provides sufficient heat for melting, and a wider arc distribution (i.e.,longer arc length) will

result in a wider surface width. A very broad arc distribution (i.e.,longer arc length) at low heat

input (i.e.,lower welding current) will not produce melting, thus results in decreasing surface

width of weld.

MODELING OF PROCESS DYNAMICS

The objectives for modeling of process dynamics were: (1) to develop a 3-D FEM to

calculate not only the transient thermal histories but also the sizes of weld pool in single-pass arc welding; (2) to determine the effect of arc length to the formation of weld; (3) to obtain the effect

of welding parameters to the arc efficiency and heat distribution parameter; and (4) to identify

330
the correlation among controllable variables (i.e., welding current and speed), arc efficiency and

heat distribution parameter.

The weldment is made of SUS304 stainless steel. Temperature-dependent material

properties are used in this study, which bring nonlinearity into the calculation. To account for the

heat transfer due to the fluid flow in weld pool, the thermal conductivity is assumed to be

enhanced linearly from 1 to 6 with temperature change from the solidus temperature (1673K) to

3100K [Ref24]. The liquid/solid phase transformation is approximated by taking into account

the latent heat of fusion over melting range (between 1673K and 1723K). The thermal properties

used in our calculation are summarized in Table 1. The thickness of the weldment under

investigation is 4mm and 6mm. The length of the FE model is selected to be 36mm and the width

is 20mm. The symmetry condition along the arc path is assumed in our model. The fine mesh

with 0.333mm characteristic grid spacing is applied to the weld pool region, while the coarse mesh is used elsewhere in order to reduce the computation costs. The finite element mesh for the

simulation model is depicted in Figure 12.

The overall input of heat flux is calculated as:

where r| is arc efficiency factor, which accounts for the radiation and other losses from the arc to

the environment, E is voltage and I is electric current. The radial heat flux distribution on the top

surface of weldment is approximated to be a Gaussian function in the form of (as shown in

Figure 13);

Qo f-r']

where r is the radial distance with respect to the origin at the center of torch; is the

distribution parameter defined as the radial distance within which 95% of energy is transferred.

Convection and radiation boundary conditions are applied to the weldment to model the heat

loss from the weldment surfaces. Particularly, the forced convection boundary condition is

assumed on the entire top surface right below the arc. The natural convection boundary

conditions are assumed on all the other surfaces. All the convection boundary conditions are modeled by Newton's law of cooling, i.e.

where his the convection coefficient which is assumed to be constant through the welding

process; T is the local temperature and r„ is the ambient temperature. The radiation boundary

condition is applied over the top surface following to the gray body radiation law:

331
INTRODUCTION

Gas tungsten arc welding is the most useful welding process used in cases which require

higher weld strength joint such as aerospace, nuclear, large structure and automotive industries.

The weld strength is affected by the geometry of weld and thermal characteristics. In order to

create desired weld strength, the weld cross section geometry (i.e., such as weld bead width,

weld depth, weld front width, and weld heat-affected zone) must be developed accurately (Figure

1).

The key geometric attribute of a major class of welds is the depth of penetration since it is a

direct indicator of weld strength. However, direct measurement of this quantity is quite difficult

since the depth is not directly visible. Several investigator [Refs.1-4] utilized reflection

ultrasound methods to measure the size of weld pool, but this technique is not very accurate and

unapplicable because of the problem of diffusion of the signal and the distortion caused by steep

temperature gradients. The other methods have been studied to indirectly detect the pool shape

based on the concept of pool oscillation [Refs.5-7], but it has been proved by Zacksenhouse and

Hardt [Ref.8] that it is not practical to detect the pool shape in partial penetration using the

concept of pool oscillation. The surface waves can only be correlated to bulk fluid oscillation for

full penetration welds. In a recent report, an effort to study the pool oscillation on a stationary

welding pool was made by Xiao and Guden [Ref.9]. The case of partial penetration as well as

full penetration was performed in this research but not in a practical welding situations which is a

moving welding pool. Other Studies have been conducted to correlate the depth of penetration to

variables which can be measured during welding including arc voltage sensing[Ref.lO], x-ray

sensing[Ref.ll], video[Ref.l2], acoustic sensing [Ref.l3] and surface radiation

techniques[Ref.l4]. Every methods offers specific advantages. But each of these methods

contain drawbacks such as signal disturbances from high temperatures, contact sensing devices,

sensing performance corrupted by workpiece thickness or preheat changes, and gave no details

on the actual pool shape. These drawbacks have limited their applicability in practical welding

situations. Infrared sensing techniques have been studied as a possible sensor to detect weld

penetration [Ref 15-17]. In order to develop a sensor based on infrared sensing that can be easily

implemented in practical welding applications. The development of a mathematical relationship

among different welding conditions (i.e., varied current, welding speed, material types, electrode

tip angle, . . .etc.), measurable signal, and size of weld pool becomes an important research goal.

The temperature history of the welded components has a significant influence on the

residual stresses, distortion and hence the fatigue behavior of the welded structure. A large

amount of research has been dedicated to the simulation of welding process in the past few

decades. However, since the welding process causes complex mechanical, electric and

metallurgical changes in weldments, the accurate solution of mathematical model, which is

capable of addressing all the issues including material nonlinearity, phase transformation,

magnetodynamic effects, fluid flow in weld pool, etc., is practically intangible. To solve the

problem, the numerical methods, particularly the finite difference methods and the finite element

methods, are widely used for the simulation of welding process and the resultant residual strain

and stress fields. A quick review of literatures reveals that there are primarily two approaches in

the simulation of welding process. One approach is to solve the decoupled heat transfer equation

and Navier-Stokes equations so that the temperature distribution and the details of fluid flow in

328
weld pool can be obtained. The typical examples following this approach can be found in

Kovacevic, el al.[Ref 18] and Cao. et al, [Ref 19], where the effects of changing welding

parameters on the characteristic geometry of weld pool is analyzed by using three dimensional

finite difference method for transient analysis. More examples can be found, to name a few, in

Zacharia, et.,[Ref.20,21] and Kim and Na [Ref22]. Another approach for the simulation of the

welding process is to solve the heat transfer equation directly with the consideration of

temperature-dependent material properties and phase transformation. Due to the simplicity, yet

the acceptable accuracy, of this approach, many numerical models have been developed based on

it, especially when the resultant residual strain and stress is of the particular interest. The

examples can be found in Tekriwal and Mazumder [Ref.23], Mahin, et al,[Ref24], Brown and

Song [Ref25], Taljat, et al,[Ref.26] and Bonifaz [Ref.27]. Typically the heat flux input from arc

is assumed to follow Gaussian function or double ellipsoidal model[Ref27].

This research is focused on the critical single-pass bead-on-plate GTA welding process.

Three dimensional finite element model for the simulation of transient heat transfer is adopted in

our analysis in the sense that the simple 2-D model captures less information of the characteristic

sizes of the weld pool than 3-D model does. The accuracy of the 2-D model has also been

questioned by Brown and Song[Ref.25] and Bonifaz[Ref27]. The aforementioned second

approach is employed in our finite element model, and the effect of fluid flow in the weld pool is

approximated by the temperature-dependent material properties. The distribution of the moving

heat source generated by the arc is assumed to be 2-dimensional Gaussian function. The

experimental calibration and verification are carried out to verify the numerical model. The

correlation between the parameters of the GTA welding process, including torch speed, electric

current and voltage, and the characteristic geometry of the weld pool are established. The major

parameters which will affect the pool size are: arc length, current, speed, voltage, and vertex

angle of electrode. However, the electrode is installed and cannot be changed during welding in

automatic welding. So in this research we will not study the effects of electrode vertex angle.

EXPERIMENTS

A Hobart TIGWAVETM 350 AC/DC welding system (Figure 2) was employed for the gas

tungsten arc welding in this study. The water-cooled torch was manipulated by a X-Y positional

table. The electrode was made of EWTh-2 with 3.2 mm diameter and 120 (degrees) included tip

angle. The electrode extension from the nozzle (with a diameter of 1 1 mm) was 3 mm. Shielding

gas with a flow rate of 10 liters/min was industrial pure argon (99.99%). Preparation of test

coupons with dimensions of 130 X 130 mm were made of SUS304 (Type 304) austenitic

stainless plates 6mm and 4mm in thickness. Prior to welding, the coupons were sanded with

#200 sand paper and cleaned with acetone. But welds then carried out down the center of the

plate. The samples were mounted on the flat surface of the plateform. During welding, the

coupons were free of restraint. The joint setup was autogenous bead-on-plate welding. Welding

parameters influence the quality of welded joints. Therefore, the determination of these

parameters is an important step in this study. The criteria for selecting the optimum welding

parameters include the external appearance of the weld, fusion geometry and penetration. To

study the effects of welding parameters on the weld geometry, the tested coupons were sectioned

perpendicularly to the welding direction for metallographic examinations. The sectioned samples

329
were mounted, sanded using sand papers, and polished using AI2O3 (0.05 micron) colloidal

solution. Samples were cleaned before etched with a solution with 10 g CuS04 + 50 ml HCl +

50ml water. Samples were observed under an Olympus BX60M microscope to determine the

weld profile.

In order to verify and calibrate the 3-D FEM model, experiments were performed to

investigate the interaction and correlation of process variables and variations affecting on the

formation of weld pool for GTA welding. The experimental results as shown in Figure 3,

indicate that: (1). increasing the welding current increases the weld penetration; (2). increasing

the welding speed decreases the weld penetration. The open-loop response is the key parameter

for development of control system. Therefore, experiments were conducted to realize how the

open-loop response will be affected by the controllable variables which is the welding current

and welding speed. Figure 4 shows that increases input current magnitude speed up open-loop

response. Increasing welding speed will slow down open-loop response as shown in Figure 5.

Figure 6 indicates that welding speed has more effect on open-loop response than welding

current.

Welding process anomalies occurs quite frequentiy when welding production sheet metal

parts. The most significant effect of process anomalies is that it results from variation of arc

length. The condition arises when the metal parts are not flat, when contours do not match

dies/fixtures or one another precisely, when ripples or wrinkles are present, when tip of tungsten

electrode is melt due to high temperature. In order to study the effect of variation of arc length on

the formation of weld pool, welding on stepped down plates was performed (i.e., arc length

increases from 2mm to 4mm) as shown in Figure 7. Experimental data indicates that increasing

the arc length decreases weld penetration under constant welding speed as shown in Figure 8.

Figure 9 show that increases arc length decreases weld penetration under constant welding

current. This indicates longer arc length will make the arc efficiency lower.

The arc length affecting on weld surface width is also examed. The results presented in

Figure 10 and 11 show that the surface width slightiy increases with increasing arc length. The

longer arc length results in greater expansion of the arc. Therefore, the surface width increases

with arc length increased. However, the surface width decreases with arc length increased when

welding at lower current. The reason is that higher heat input (i.e.,higher welding current)

provides sufficient heat for melting, and a wider arc distribution (i.e.,longer arc length) will

result in a wider surface width. A very broad arc distribution (i.e.,longer arc length) at low heat

input (i.e.,lower welding current) will not produce melting, thus results in decreasing surface

width of weld.

MODELING OF PROCESS DYNAMICS

The objectives for modeling of process dynamics were: (1) to develop a 3-D FEM to

calculate not only the transient thermal histories but also the sizes of weld pool in single-pass arc welding; (2) to determine the effect of arc length to the formation of weld; (3) to obtain the effect

of welding parameters to the arc efficiency and heat distribution parameter; and (4) to identify

330
the correlation among controllable variables (i.e., welding current and speed), arc efficiency and

heat distribution parameter.

The weldment is made of SUS304 stainless steel. Temperature-dependent material

properties are used in this study, which bring nonlinearity into the calculation. To account for the

heat transfer due to the fluid flow in weld pool, the thermal conductivity is assumed to be

enhanced linearly from 1 to 6 with temperature change from the solidus temperature (1673K) to

3100K [Ref.24]. The liquid/solid phase transformation is approximated by taking into account

the latent heat of fusion over melting range (between 1673K and 1723K). The thermal properties

used in our calculation are summarized in Table 1. The thickness of the weldment under

investigation is 4mm and 6mm. The length of the FE model is selected to be 36mm and the width

is 20mm. The symmetry condition along the arc path is assumed in our model. The fine mesh

with 0.333mm characteristic grid spacing is applied to the weld pool region, while the coarse

mesh is used elsewhere in order to reduce the computation costs. The finite element mesh for the

simulation model is depicted in Figure 12.

The overall input of heat flux is calculated as:

Qo=VEI

where r| is arc efficiency factor, which accounts for the radiation and other losses from the arc to

the environment, E is voltage and / is electric current. The radial heat flux distribution on the top

surface of weldment is approximated to be a Gaussian function in the form of (as shown in

Figure 13);

q = —Vexp —rwhere r is the radial distance with respect to the origin at the center of torch; is the

distribution parameter defined as the radial distance within which 95% of energy is transferred.

Convection and radiation boundary conditions are applied to the weldment to model the heat

loss from the weldment surfaces. Particularly, the forced convection boundary condition is

assumed on the entire top surface right below the arc. The natural convection boundary

conditions are assumed on all the other surfaces. All the convection boundary conditions are modeled by Newton's law of cooling, i.e.

where his the convection coefficient which is assumed to be constant through the welding

process; T is the local temperature and is the ambient temperature. The radiation boundary

condition is applied over the top surface following to the gray body radiation law:

331
where s is the surface emittance and cr is Stephen-Boltzman constant. The evaporation of the

welding pool is assumed to be negligible in this study.

SIMULATION RESULTS AND EXPERIMENTAL VERIFICATION

Figure 14 shows the transient temperature distribution of the weld created from the

developed 3D FEM model under different welding conditions. The numerical simulations are

carried out for two different workpiece thickness (i.e., 4mm and 6mm) with various torch

velocities. The weld penetration and surface width of different workpiece thickness can be

predicted through the developed FEM model under various welding conditions as shown in

Figure 15 to 18.

The heat distribution parameter has significant influences on both the shape and the size

of weld pools. The numerical simulation reveals that the depth of weld pools is more sensitive to

the changing of than the top-surface width and length. With the increase of r^, the top

surface of weld pools tends to evolving from approximately round shape to tear-drop shape. The

curvature of the weld pool boundary in the depth decrease slightly faster at the front end than at

the rear end when Vq increases. The arc efficiency also has significant influences on the size

weld pools since it directly relates to the amount of the net heat input from arc (i.e., T|*E*I).

Again, the depth of weld pools is more sensitive to the changing of the arc efficiency than the

top-surface width and length. The shape of the top surface of weld pools changes slightly with

the arc efficiency, while the shape in the depth is significantly affected. As for the effects of heat

source parameter on the cooling rate, smaller and efficiency will lead to faster cooling

process.

The dependence of heat distribution parameter, , and arc efficiency on travel speed is

shown in Figure 19 and Figure 20. The process exhibits a nearly constant value of heat

distribution parameter with the exception of the higher current where a decrease is noted as increasing the welding speed. The small distribution parameter, which mean the heat source

more concentrated on the surface under the torch, does not mean that the small pool size will be

obtained. The depth of weld pool is very sensitive to the distribution parameter while surface

width is relatively not. Figure 20 shows a nearly constant level of arc efficiency with the

exception of the lower current where a decrease is noted.

Figure 21 and 22 show that the distribution parameter, , increases rapidly with

increasing arc length and increasing current. As the arc length is increased, the number of

electron-ion collisions increases and the arc spreads from the sharply-tipped cathode to the flat

anode. The increased electron-ion scattering of a longer arc results in greater expansion of the arc. However, distribution parameter slightly decreases with increasing arc length at lower welding

current. This shows that lower current will not provide enough heat to melt the workpiece.

The arc length affecting on arc efficiency is also examed. The results presented in Figure 23

and 24 show that the arc efficiency decreases with increasing arc length. This indicates longer

arc length will make the arc efficiency lower. This results are constanct with the experimental

332
results as shown in Figure 8 and 9, where the weld penetration decreases as the arc length

increasing.

CONCLUSIONS

1. The effects of variations in the welding process parameters on the heat distributions in gas

tungsten arc welding have been reported. Arc length is the primary parameter govering the

heat distributions. The results provide a link between the engineering parameters of current,

welding speed to the scientific parameters which consist of the heat distribution parameter.

2. The relative insensitivity of the GTAW arc efficiency to travel speed has been reported.

3. Develop a meaningful between teoretical models and experimental observations.

4. The finite element solution was sensitive to heat distribution. The model results provided a

straight forward approach to understanding the effects of heat distribution.

ACKNOWLEDGEMENTS

The work reported here was partially performed and funded under the support of the National

Science Council of Taiwan under the Grant No. NSC 88-22 12-E-239-004. The author would also

like to thank National Center for High-performance Computing of Taiwan for furnishing

computational systems which made this investigation possible.

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333
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control for the welding process," Optical Engineering, Vol.25, No.6, 1986, pp.799-805.

16. Lukens, W.E., and Morris, R.A., "Infrared temperature sensing of cooling rates for arc

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Vol. 118, 1996,pp.689-596.

19. Cao, Z.N., Zhang, Y.M. and Kovacevic, R., "Numerical Dynamic Analysis of moving GTA

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20. Zacharia, T.,Eraslan, A.H. and Aidun, D.K.," Modeling of non-autogenous welding,"

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23. Tekriwal, P. and Mazumder, J., "Finite element analysis of three-dimensional transient heat

transfer in GMA welding," Welding Journal, Vol.67, No.5, 1988, pp.l50s-156s

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27. Bonifaz, E.A., "Finite element analysis of heat flow in single-pass arc welds," Welding

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335
Front Width

0 1 2 3 4 5 8 time (s)

Figure 4 Effect of welding current on

open-loop response

Figure 2. Experiemntal system ^^8"^^ ^ Effect of welding speed on

open-loop response

current (A) 50 100 welding speed (mnfi/min)

Figure 3 Effect of welding current and speed

on GTA weld penetration (workpiece

thickness=6mm)

cun'ent (A) 50 100 welding speed (mnVmIn)

Figure 6 Correlation of welding current,

speed, and open-loop response (arc

length=2nim, workpiece

thickness=6mm)

336
Torch

Welding Direction

XL.

Figure 7 Welding on varying workpiece thickness

12

11

10

9 f

«

5 6 U

CD

in 4

3

•

2

11,5

Speed = lOOmm/mIn

-B- 7DA

110A

-9- 150A

2 25 3 3.5 arc length (mm)45

Figure 10 EfiFect ofarc length on weld surface

width under constant welding speed

4

3.5

3 •

£2.5

1.5

0.5"—

1,5

speed = lOOmm/min

-B- 70A

110A -©- 150A

2,5 3 3.5 arc length (mm)45

Figure 8 Effect of arc length on GTA weld penetration

under constant welding speed

E

E, to> •D

1,5

0,51—

1,5 2,5 3 3,5 arc length (mm)4.5

Figure 9 Effect of arc length on GTA weld penetration

under constant welding current

6

speed = ISOmm/min

-a- 70A -- 11QA

-e- 15QA

1,5 2 25 3 3,5 arc length (mm)

Figure 1 1 Effect of arc length on weld surface

width under constant welding speed

Table 1 : Thermal properties for SUSS04 stainless

steel

Thermal conductivity

(W/m-K)

Solid (r<1673K)

Liquid (K)

8.116 + 1.618x10

-171.1 + 0.1233T

Density (kg/m"*)

Solid (<=1673K)

Liquid (>1673K)

8000

7200

Specific heat (J/kg-K)

Solid (r< 1673 K)

Liquid (T>1723K)

Change linearly

between 1673K and

1723K

465.4 + 0.13367

788

Latent heat of fusion (J/kg) 2.5x10'

5 Convection coefficient (W/m -K)

Natural convection

Forced convection

10

40

Surface emissivity 0.7

Ambient temperature (K) 300

337
Figure 14 Temperature distributions at 9th second, (red areas stand for

the weld pools) (E= 12.2V, 1=90A, = 0.50

,

^0 =1.05 with speed 125, 150,

1 75 mmlmin respectively)

338
60 100 120 140 160 160 200 220 welding speed (mm/min) Figure 15 Effect of welding speed on the suface

width of weld pool (arc length=2mm,

workpiece thickness=4nim)

(15DA, 133V)

60 80

o tesi data — simuWion results

100 120 140 welding speed (mm/min)160 ISO

Figure 18 Effect of welding speed on the depth of

weld pool (arc length=2imn, workpiece

thickness=6mm)

o test data — simulation results

eo 100 120 140 160 180 welding speed (mm/min)200

Figure 16 Effect of welding speed on the depth of

weld pool (arc length - 2mm, workpiece

thickness=4mm)

E

E— 6

S 4

° 3

140 160 welding speed (nm/min) Figure 19 Effect of welding speed on the

heat distribution parameter (arc

length=2mm, workpiece

thickness=4mm)

60 60 100 120 140 welding speed (mm/min)

160 160

Figure 17 Effect of welding speed on the surface

width of weld pool (arc length=2mm,

workpiece thickness=6mm)

IX 140 160 180 200 welding speed (mm/min)

Figure 20 Effect of welding speed on arc efficiency

(arc length=2mm, workpiece

thickness=4mm)

339
current (A) 50 2 arc length (mm)

Figure 21 Correlation among heat

distribution parameter, current

and arc length under

KM

cun-9nl(A) 75

50 2 arc length (mm)

Figure 22 Correlation among heat

distribution parameter, current

and arc length under

150mm/min welding speed

1X

cun'ent(A) 60 2

arc length (mm)

Figure 24 Correlation among arc

efficiency, current and arc length under 150mm/min

welding speed

Figure 23 Correlation among arc

efficiency, current and arc length

under lOOmm/min welding

speed

340 i5fU.S. GOVERNMENT PRINTING OFFICE:2002-491 -064/65904

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