Nisr

National Institute of Standards and Technology

Technology Administration, U.S. Department of Commerce

NIST Special Publication 1008

Thirteenth International Conference

on Computerization of Welding

T. A. Siewert and W. G. Rippey, Editors
rhe National Institute of Standards and Technology was established in 1988 by Congress to "assist industry in the development of technology . . . needed to improve product quality, to modernize manufacturing processes,

to ensure product reliability . . . and to facilitate rapid commercialization ... of products based on new scientific discoveries." NIST, originally founded as the National Bureau of Standards in 1901, works to strengthen U.S. industry's

competitiveness; advance science and engineering; and improve public health, safety, and the environment. One

of the agency's basic functions is to develop, maintain, and retain custody of the national standards of

measurement, and provide the means and methods for comparing standards used in science, engineering,

manufacturing, commerce, industry, and education with the standards adopted or recognized by the Federal

Government.

As an agency of the U.S. Commerce Department's Technology Administration, NIST conducts basic and

applied research in the physical sciences and engineering, and develops measurement techniques, test methods, standards, and related services. The Institute does generic and precompetitive work on new and

advanced technologies. NIST's research facilities are located at Gaithersburg, MD 20899, and at Boulder, CO 80303.

Major technical operating units and their principal activities are listed below. For more information contact the Publications and Program Inquiries Desk, 301-975-3058.

Office of the Director

• National Quality Program

• International and Academic Affairs

Technology Services

• Standards Services

• Technology Partnerships

• Measurement Services

• Information Services Advanced Technology Program

• Economic Assessment

• Information Technology and Applications

• Chemistry and Life Sciences

• Materials and Manufacturing Technology

• Electronics and Photonics Technology

Manufacturing Extension Partnership

Program

• Regional Programs

• National Programs

• Program Development

Electronics and Electrical Engineering

Laboratory

• Microelectronics

• Law Enforcement Standards

• Electricity

• Semiconductor Electronics

• Radio-Frequency Technology'

• Electromagnetic Technology'

• Optoelectronics'

Materials Science and Engineering

Laboratory

• Intelligent Processing of Materials

• Ceramics

• Materials Reliability'

• Polymers

• Metallurgy

• NIST Center for Neutron Research

Chemical Science and Technology

Laboratory

• Biotechnology

• Physical and Chemical Properties^

• Analytical Chemistry

• Process Measurements

« Surface and Microanalysis Science Physics Laboratory

• Electron and Optical Physics

• Atomic Physics

• Optical Technology

• Ionizing Radiation

• Time and Frequency'

• Quantum Physics'

Manufacturing Engineering

Laboratory

• Precision Engineering

• Automated Production Technology

• Intelligent Systems

• Fabrication Technology

• Manufacturing Systems Integration

Building and Fire Research

Laboratory

• Applied Economics

• Structures

• Building Materials

• Building Environment

• Fire Safety Engineering

• Fire Science Information Technology Laboratory

• Mathematical and Computational Sciences^

• Advanced Network Technologies

• Computer Security

• Information Access and User Interfaces

• High Performance Systems and Services

• Distributed Computing and Information Services

• Software Diagnostics and Conformance Testing

• Statistical Engineering

'At Boulder, CO 80303. ^Some elements at Boulder, CO.
NIST Special Publication 1008

Thirteenth International Conference

on Computer Technology in Welding

Digest of a symposium sponsored by the

National Institute of Standards and Technology

in cooperation with the

American Welding Society and the Welding Institute

June 18,2003

Orlando, Florida

Edited by

T. A. Siewert

W. G. Rippey

i

October 2003

U.S. Department of Commerce

Donald L. Evans, Secretary

Technology Administration

Phillip J. Bond, Under Secretaryfor Technology

National Institute of Standards and Technology

Arden L. Bement, Jr., Director
National Institute of Standards and Technology Special Publication 1008

Natl. Inst. Stand. Technol. Spec. Publ. 1006, 208 pages (October 2003)

CODEN:NSPUE2

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 2003

For sale by the Superintendent of Documents, U.S. Government Printing Office

Internet: bookstore.gpo.gov Phone:(202) 512-1800 Fax:(202) 512-2250

Mail: Stop SSOP, Washington, DC 20402-0001

\
CONTENTS

Modeling—General Topics 1

The Development of an In-process Monitoring Solution for Inertia Friction Welding:

An Investigation into Feature Descriptors 3

D. A. Hartman, Los Alamos National Laboratory, Los Alamos, New Mexico;

V. R. Dave, and M. J. Cola

Numerical Simulation of Resistance Spot Welding Process using FEA Technique 53

C. Srikunwong, School of Miles, Paris, France; T. Dupuy, and Y. Bienvenu

The Finite Element Method of Resistance Spot Welding 65

A. V. Gohil, Shantilal Shah Engineering College, Bhavnagar, India; S. M. Patel

Modeling—Weld Pool and Solidification 73

The Possibilities of the Application of an Expert System in the Manufacturing

of Resistance Spot Welded Railway Vehicle Structures 75

I. Borhy, TUV Rheinland InterCert Ltd., Budapest, Romania; B. Palotas

Numerical Simulation System of Development of Ultra-narrow Gap GMAW Process 83

T. Nakamura, National Institute for Materials Science, Tsukuba, Japan; K. Hiraoka

Dimensional Distortion of T-joints using Aluminum 6063-T52 Extrusion Material

in MIG Welding 91

R. Koganti, Ford Motor Company, Dearborn, Michigan; M. Zaluzec, J. Velez,

C. Karas, A Joaquin, and A. Caliskan

Fractional Factorial Technique to Predict Welding Current in Gas Metal Arc Welding 103

M. Aghakhani, University of Razi Street, Kermanshah, Iran

Weld Data Flow 113

Control of GMA Butt Joint Welding Based on Neural Networks 115

K. H. Christensen, Technical University of Denmark, Lyngby, Denmark; T. S0rensen

Arc Welding Process Signature Visualization and Analysis 125

C. Hsu, Lincoln Electric Company, Cleveland, Ohio

Network Communications for Weld Cell Integration—Status of Standards Development 135

W. G. Rippey, National Institute of Standards and Technology, Gaithersburg, Maryland

Computers and Automation 171

A Computer Program for Choosing Welding Parameters in Spirally Welded Pipe Production 173

K. §irin, Mannesmann Pipe Company, Kocaeli, Turkey; S. Y. §irin and E. Kalu?

Metal Inert Gas (MIG) Welding Process Optimization for Extruded (6063-T52) T-joint Configuration

Using OTC/Diahen Equipment 179

R. Koganti, Ford Motor Company, Dearborn, Michigan; M. Zaluzec, J. Velez, C. Karas,

A. Joaquin, and A. Caliskan, J. Wang

iii

PREFACE

The Thirteenth International Conference on Computer Technology in Welding was

held June 18, 2003 in Orlando, Florida, under the sponsorship of the American

Welding Society, the National Institute of Standards and Technology (NIST), and

The Welding Institute. These proceedings include all the manuscripts that were

submitted (including viewgraphs)—twelve presentations grouped into sessions on modeling (general topics), modeling (weld pool and solidification), weld data flow,

and computers and automation. Additional copies of this NIST Special Publication

XXX are available from the National Technical Information Service (NTIS) or the

Government Printing Office (GPO).

DISCLAIMER

Except where attributed to authors from the National Institute of Standards and

Technology (NIST), the content of individual sections of this volume has neither

been reviewed nor edited by the NIST staff. Therefore, NIST 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 NIST.

V

Modeling—General Topics

1
)
THE DEVELOPMENT OF AN IN-PROCESS MONITORING SOLUTION

FOR INERTIA FRICTION WELDING:

AN INVESTIGATION INTO FEATURE DESCRIPTORS

T).A. Hartman, V.R. Dave, and M.J. Cola^

ABSTRACT

The development of a novel sensing method coupled with a pattern recognition system is presented

as a non-destructive evaluation (NDE) technique for inertia friction welding. The complex nature of

sohd-state welding processes, and, in particular, inertia friction welding, prevents a system from incor- porating a simple model (e.g., upset) to separate acceptable from unacceptable welds when subtle process

variations occur. This work presents the application of an array of non-contact, acoustic emission sensors for determining bond integrity. The sensor data is explored through a variety of feature descriptors

(RMS, energy, attack and decay, and power spectrum) and, in some cases, fused with the machine data

(speed, pressure, and upset) in an attempt to develop a robust, in-situ NDE technique. The results are presented for bar-to-bar inertia friction welding of copper to stainless steel which exhibits only marginal

weldability and, therefore, is ideally suited for validating the capabilities of this new sensing technique.

INTRODUCTION

Although friction welding is a relatively robust process, highly performance-critical or man-rated joints

require additional scrutiny. Examples of such critical friction welds abound in aerospace applications, e.g., shafts, discs, hubs, fan and helicopter blades, and rotors. In such environments, the total number of parts

made is generally low. Therefore, destructive evaluation is a very costly proposition, not to mention that commercially available post-process, non-destructive evaluation (NDE) techniques are not able to detect all of the fault conditions of possible concern. Hence, an in-process approach is invaluable in precision, small

lot manufacturing of mission- or performance-critical hardware.

For large lot manufacturing environments, the total number of parts can reach upward of tens to hundreds per

hour. Environments such as these perform routine destructive evaluation (commonly referred to as "bookending") to ensure that the process is performing under control. Unfortunately, this method of inspection is not always indicative of the integrity of bonds made before (or after) the part that is currently under scrutiny.

Furthermore, once a flaw is detected by destructive evaluation all parts produced previously must be rejected

(or reworked) regardless of quality. This method of quality inspection reduces productivity and increases

'This work was conducted at Los Alamos National Laboratory, which is operated by the University of California for the United

States Department of Energy under Contract Number W-7405-ENG-36.

^D.A. Hartman is a technical staff member in the Manufacturing and Process Science and Technology Group in the Nuclear

Materials Technology Division of Los Alamos National Laboratory, V.R. Dave is the Group Leader for the Manufacturing and

Process Science and Technology Group in the Nuclear Materials Technology Division of Los Alamos National Laboratory, and

M.J. Cola is a Deputy Group Leader for the Weapons Component Technology Group in the Nuclear Materials Technology Division of Los Alamos National Laboratory, P.O. Box 1663, Mail Stop E506, Los Alamos, New Mexico 87545. E-mail: hartman@lanl.gov,

vivek@lanl.gov, cola@lanl.gov.

3
waste and cost while never guaranteeing that each and every bond is adequate. An in-process approach can

alleviate this burden by providing a consistent, thorough, and real-time response to part quality and, thereby,

leaving only suspect parts for further post-process investigation by traditional destructive or non-destructive

methods.

This paper presents the development of an in-process monitoring solution for inertia friction welding (IFRW).

An Experimental Setup Section briefly describes the experimental approach. A Process Description Section

presents the in-process data that was collected. An overview of the four feature descriptors that were used

to explore the process data is presented in the Feature Analysis Section. A Results and Discussion Section

highlights the relevant results of this work. Finally, a Conclusions Section summarizes our findings and

lists the benefits of employing an in-process monitoring solution for large- and small-scale manufacturing

environments.

EXPERIMENTAL SETUP

The focus in this preliminary investigation was to search for and identify features within the acoustic energy

and/or machine data that are indicative of bond quality. Bar-to-bar inertia friction welding of 1-inch di- ameter, oxygen-free, high-conductivity copper bar to 0.5-inch diameter, annealed Type 304L stainless steel was used in this proof-of-concept study. This material combination exhibits only marginal weldability and,

therefore, was ideally suited for validating the capabilities of this new sensing technique. All welding was

conducted using a MTI Model 90B inertia friction welding system. The welding parameter selection was

based upon work by Bell, et al. (Ref. 1) but altered slightly to accommodate differences in available inertial mass.

Prior to welding, all copper specimens were machined and stored for five weeks at ambient temperature and

pressure. In order to remove surface oxidation that might have developed during storage, selected copper

specimens were machined immediately before welding while bathed in isopropyl alcohol. Others were

welded as is. In all cases, the stainless steel was rotated during the weld cycle while the copper remained

fixed. A non-contact array of microphones surrounds the weld joint and collects the rapid release of energy (i.e., sound pressure) due to the mechanical, thermal, and metallurgical phenomenon occurring during the weld

cycle. The acoustic transducers used in this research are off-the-shelf electret condenser microphones that were sampled at 40 kHz per channel. The welding system provided process data (speed, pressure, and upset)

that was sampled at 100 Hz per channel.

A semi-quantitative evaluation of each joint was performed using unguided bend testing. As-welded, full- size specimens were tested. Image analysis techniques were used to determine the percent of bonded area

after fracturing each specimen. A presentation of the experimental setup and the sensor can be found in Ref. 2.

PROCESS DESCRIPTION

The process description for this work was captured with machine process data (speed, pressure, and upset)

and acoustic energy'. Representative plots for speed, pressure, upset, and acoustic energy are illustrated in

' A curious feature within all of the Cu-SS acoustic signatures is the presence of a second burst of acoustic energy soon after the

initial weld burst. This burst also coincides with a sharp drop in the speed to zero. One plausible explanation for this phenomenon

4

A

/
Figure 1 . This data represents the process inputs for an in-process monitoring system, while the process

output (i.e., quality metric) is bond integrity.

Machine Data: Speed Machine Data: Pressure

Tone, [s] Time, [s]

(c) (d)

Figure 1 : Representative plots for machine data and acoustic energy.

FEATURE ANALYSIS

Feature analysis involves implementing signal processing and data analysis techniques that explore and,

potentially, improve upon the "raw" data. The implementation of a specific feature analysis technique is called a feature descriptor. Oftentimes, the raw data is in a format that is not conducive for direct input into a classifier and, therefore, requires manipulation in order to reduce and/or enhance the data for classification purposes.

is that as the weld material cools, its effective rheological behavior rapidly changes, and at a point the material reverts from a viscoplastic behavior to a largely elastic behavior. This change could occur over a very short time, and thereby result in a sudden

"seizing" behavior. When this happens, an impulse load is effectively applied to the joint causing it to ring, and the acoustic sensor

is able to discern this ringing

5
Ideally, the implementation of a feature descriptor makes the decision making process of the classifier trivial. The task of feature analysis is normally a domain-dependent operation. In addition to using the raw data in a normalized format, the following feature descriptors were investigated: root mean square (RMS), energy,

attack and decay, and power spectrum. Each of these will be addressed separately in the following sections.

Typical Acoustic Signature

Typical Acoustic Signature

-1— 1 1 ;

1

1.5 2 Time, [s]

1 S

o

to 5

1 1.5 2 Time, (sj

£ 1000 i

o 500

LU

Acoustic Energy

799,2

1283,4

635.9 639.0

1.5 Time, [2.5

(a) (b)

Acoustic Attack and Decay

1.S 2 Time, [s]

Typical Acoustic Signature

1.5 2

Tirre. [s]

Acoustic Power Spectrum

i.i,LA..i.Jk.........

0 200 400 600 SOO 1000 1200 1400 1600 1800 2000 Frequency, (Hz)

(c) (d)

Figure 2: Four feature descriptors are investigated in this work.

RMS

The root mean square value is one way of describing an AC waveform. A true RMS measurement relates the heating potential of an applied voltage. The RMS gives the effective value of an AC voltage or current

signal by (1) squaring the instantaneous values, (2) calculating the mean, and then (3) taking the square root. The following equation calculates the RMS value of an instantaneous current or voltage signal, f{t):

6

\/
1

/•*+'^ tI. ^W^-

where T = 1/f and / = fundamental frequency. A pure AC signal swings about a zero voltage (or

current) axis, going positive one moment and negative the next. Consequently, its average value over a complete cycle is zero. The RMS value does not average out to zero, however, and is, therefore, essentially the AC equivalent of DC voltage or current. Figure 2(a) illustrates the RMS for a representative acoustic

energy waveform.

Energy

The energy of a signal, f{t), is defined as

RMS{f{t)) =

Eifit))^ y: \f{tf

t= — oo

The energy of a discrete-time signal, x{n), over a finite interval, —A'' < n < A^, is defined as

(2)

N

E{x{n))= Yl I^W' (3)

Figure 2(b) illustrates the energy calculation for four segments of a representative acoustic energy waveform.

Attack and Decay

The attack and decay feature descriptors are commonly applied in audio processing of voice and musical

instruments (Refs. 3, 4). Attack and decay phases are described by fitting a Hnear or non-linear function

to a set of data. Attack descriptors are used for describing regions of increasing sound intensity, while,

conversely, decay descriptors are used for describing regions of decreasing sound intensity. Similar to the energy descriptor, an important consequence of using attack and decay descriptors is in their ability to richly

define a signal in an extremely compressed manner.

In this work, the attack and decay feature descriptors were fit to both machine and acoustic energy data using an exponential function of the form:

fit) = ce^' . (4)

Figure 2(c) illustrates one attack and three decay descriptors for four segments of a representative acoustic

energy waveform.

Power Spectrum

The power spectrum is accomplished using a discrete Fourier transform (DFT). The DFT "transforms" an

ordered sequence of data samples from the time domain into the frequency domain. Spectral information

7
about the signal can be represented explicitly. In certain problem domains, the spectral information can prove

to be extremely revealing, e.g., when attempting to describe physical phenomenon that exhibit periodicity.

A moving-window DFT was performed on the AE data using the fast Fourier transform (FFT) algorithm. A

step size of 250 data points was used to move through the time-domain data without overlap. A representative power spectrum is illustrated in Figure 2(d).

RESULTS AND DISCUSSION

The experimental matrix consisted of 24 welds. Although the experimental matrix was designed with only one variable in mind, i.e., surface preparation of the copper, three different quality welds were generated:

acceptable: bonded area is approximately 100%, conditional: bonded area is less than 100% but greater

than 5%, and unacceptable: bonded area is less than 5%. Table 1 summarizes the bend test results.

Weld Surface Condition Bond Bonded

Number Before Welding Quality Area (%)

1 - 12 Freshly Machined Acceptable 100.0

13 Freshly Machined Conditional 80.0

14 Freshly Machined Conditional 70.0

15 Freshly Machined Conditional 69.0

16 Freshly Machined Conditional 67.0

17 Freshly Machined Conditional 54.0

18 Freshly Machined Conditional 48.0

19 Freshly Machined Conditional 26.0

20-24 Not Machined Unacceptable 0.0

Table 1: Bend test results.

A Probabilistic Neural Network (PNN) was used as the classifier in this work^. The training/testing method

was motivated out of a limited size of the available data set. Commonly referred to as the single holdout

method, the training/testing method holds out one data point from the n-sized data set for testing while the remaining n - 1 data points in the data set are used for training. This procedure is repeated until all of the data points in the data set have been tested independently. Classification accuracy is calculated based on the sum of the correctly classified data points minus the sum of the incorrectly classified data points divided by

n.

Table 2 tabulates some of the more noteworthy process data combinations and feature descriptors that were

investigated. Tests 4 through 7 employed a data-level sensor fusion operation on the normalized machine

data, while tests 11 through 14 and 21 through 23 employed feature-level sensor fusion operations on the machine data and on the acoustic energy and machine data, respectively.

Among the machine data, speed provided the most accurate classification results, which agrees well with

Bell, et al.'s (Ref. 1) findings, i.e., any minor contamination at the bond plane prevents a metallurgical bond

from occurring which is manifested in the speed curve. Furthermore, the decreasing accuracy between

acceptable versus unacceptable and acceptable versus conditional demonstrates the extreme subtleness that bond plane contamination can have on bond quality for this particular material combination. The authors

^For a thorough presentation of Probabihstic Neural Networks see Ref. 5

8
Pattern Classification Results

Accuracy, [% correct]Acceptable

Process Feature Acceptable Acceptable Unacceptable

Test Data Descriptor Unacceptable Conditional Conditional

1. Speed Normalize 82 74 63

2. Pressure Normalize 59 58 42

3. Upset Normalize 59 74 63

4. Speed, Pressure Normalize 76 63 54

5. Speed, Upset Normalize 59 68 58

6. Pressure, Upset Normalize 59 63 54

7. Speed, Pressure, Upset Normalize 59 63 54

8. Speed Attack and Decay 71 63 50

9. Pressure Attack and Decay 65 68 50

10. Upset Attack and Decay 47 68 50

11. Speed, Pressure Attack and Decay 65 68 54

12. Speed, Upset Attack and Decay 47 68 50

13. Pressure, Upset Attack and Decay 59 63 46

14. Speed, Pressure, Upset Attack and Decay 59 63 46

15. Acoustic Energy RMS 71 47 46

16. Acoustic Energy Energy (1 segment) 76 58 54

17. Acoustic Energy Energy (4 segments) 94 31 41

18. Acoustic Energy Attack and Decay 88 63 54

19. Acoustic Energy Attack and Decay, Energy 94 52 50

20. Acoustic Energy Power Spectrum 100 100 54

21. Acoustic Energy, Speed Attack and Decay 82 68 71

22. Acoustic Energy, Pressure Attack and Decay 71 68 63

23. Acoustic Energy, Upset Attack and Decay 72 68 63

Table 2: Pattern classification results for different combinations of process data and feature descriptors.

expect, however, that the classification accuracies (in all three categories) would improve with additional

training patterns.

Perfect classification accuracy was found to occur only during the acoustic energy power spectrum feature descriptor (test 20). Upon further investigation, the authors found a unique characteristic ringing for both an acceptable and conditional part. This ringing occurs during the second burst of acoustic energy (i.e., when speed reaches 0 rpm)^. The existence of this phenomenon is readily discernible in the power spec- trum. Consequently, bond integrity classification of acceptable versus unacceptable and conditional versus

unacceptable'* bonds becomes trivial - which is the ultimate goal of a feature descriptor.

The implementation of an in-process monitoring system does not, however, need to be limited to the use

of a single data combination and feature descriptor. The PNN classifier's computational efficiency and

^This phenomenon is analogous to the mechanism employed in a resonant inspection system: the acoustic response of a part is monitored after being impacted by a hammer with a known and repeatable force. ''The classification results for conditional versus unacceptable is not presented in Table 2.

9
framework (i.e., its ability to not only provide a decision but also estimate the probability and reliability of

a classification) enables it to be implemented on multiple data sets and feature descriptors. The output of

each classifier (decision, probability, and reliability) can then be input into a decision-making module that employs, for example, a voting or "winner takes all" template.

Furthermore, feature descriptors and data combinations that do not yield 100% classification accuracy

should not preclude their implementation in an in-process monitoring solution. In particular, an in-process

monitoring solution can provide additional feedback about the process, such as, fault classification. Although the power spectrum is feature rich in indicating existence of a bad (or good) bond, it fails to reveal

the reason why. In-process data can potentially yield relevant process information, in the form of fault di- agnostics, when an unacceptable bond is made. For IFRW, speed, for example, is an indicator of surface

contamination or joint misalignment. Consequently, the functionality of an in-process monitoring solution can provide both quality indicators and fault diagnostics.

CONCLUSIONS

A bond quality classification system was developed using a novel, non-contact, acoustic emission sensing

technique. Various feature detectors were investigated to correlate the in-process data to bond integrity. The

system provides a (near) real-time response with minimal hardware requirements.

The following benefits can be achieved by using an in-process monitoring solution for both small- and

large-lot manufacturing environments:

• Fast: provides a real-time response immediately after welding is complete,

• Inexpensive: minimal hardware requirements are necessary,

• Robust: is capable of mapping complex or ill-defined multidimensional input/output systems and is tolerant of noisy data,

• Consistent: sensing and interpretation are performed without operator intervention and, therefore, ensures an accurate, repeatable, and reproducible system,

• Thorough: examination is performed for each weld, rather than randomly to ensure joint integrity,

• Efficient: only suspect welds are examined further using an "inspect for cause" methodology, and

• Revealing: in addition to providing quality feedback, an in-process monitoring system can potentially

expose the cause for failure and, hence, provide fault diagnostic capabilities.

References

[1] R. A. Bell, J. C. Lippold, and D. R. Adolphson. Evaluation of copper-stainless steel inertia friction welds. Welding Journal, 63(1 l):325s-332s, November 1984.

[2] D.A. Hartman, M.J. Cola, V.R. Dave, N.G. Dozhier, and R.W. Carpenter. Nondestructive, in-process

inspection of friction welding: An investigation into a new sensing technique. In H.B. Smartt, J.A. Johnson, and S.A. David, editors. Trends in Welding Research, Proceedings of the 6th International

Conference, Callaway Gardens, Georgia, USA, April 15-18, 2002. ASM International.

10
[3] P. Herrera, X. Amatriain, E. BatUe, and X. Serra. Towards instrument segmentation for music content

description: A critical review of instrument classification techniques. In International Symposium on Music Information Retrieval, 2000.

[4] P. Herrera, A. Yeterian, and F. Gouyon. Automatic classification of drum sounds: a comparison of

feature selection methods and classification techniques. In International Conference on Music and

Artificial Intelligence, Edinburgh, United Kingdom, 2002.

[5] D.F. Specht. Probabilistic neural networks. Neural Networks, 3:109-118, 1990.

11
Ui

if) = .. o O c ^ O C 0 ^ O VQ. 3 C .S:> W C UL

<0 CO

Q c = c © . o

1^1

0) Q o5 s:

O ? ^ o -2 o o IS 2 £ o > I" Q>

(0 o

IS

Q Z cc

G)OO0

I-3 CO

Q.

O

O ^ O CO co

t: 2^

50

0) § « 5)

(0o o

iffcoo

15co

IS

CO o

CO "5

(0

O

12
(0

(0ooo

CDo

CDo 0

0)ccoE>co

G)co

COc

(0Eo

D)g 2 . 3 = (0

(0 (/)

CD

C

CDSo

"cB .ooI CDO

c:CD

CD

.Ooo

<0S

CO

CDs

CD

CL

«+^O

CDS

:3

COoo

.o

CD

::3 I

CDO

CO

CD Q

CDO

CDo

c:

9?

CD

CO

CD5

CJ

c:o

.2 .CO -.S $5 CO

CD .0

CD "5

CD c:

CD

CO

CO

COO

CD

-c:

c: .0

.CO5

.CD

7^

O

Q)

(0

.CO

CDS

CDo

CD

-QS

:3

CO

c:oo

.g

•2

.£

c:oo&

.0

CD

CDo

CO

CD Q

CDo

CDo

a>

CD

CO

CD

:35

c: .0

13
coocoE

13wE

JQO o ^ coE

(/)

(/) ooo

Liacoc

(/)

IS

.E <o ooo 3oo

0)

(0c

J2

re Q o

(0

>-cco

I—<

cco

CO<

0"=

C3•*-> O3C

(0
0o3COoCoEoEo c

00

CO

lU<

I-

(Du u if> 0)

Q.EX

LU

OE

0^ o 2 o

Q. (/) .£ « o £

(0oa

(03o a3o

1^ CO 3 (0

<i> 9 I E O (0 > o

GQ < .. I- OQ O 111 o

Q.E

GQ

QQ E

(0

CO

Q.

CO

CO

0)OOac

MBcE

COc3

jOo00 coo

CO0

CO0

COEo0O3O

CO

to0X

COc

CO 0 ^0c

Lil E

(0 X<dooo

h-c0oc

.2o

CO

(A0)0OO

•ucC

15
.1/
Uicoa3C

17
Eo 2iOo oE

(/> (f> ooO (0

i. o

.E c

MO ? =

Q.O O)

0)>

0)

0)

O

<5 S > 9 <o

r" < o o *^

(0C

to

18
0)

O)

(0c

0)

(0

(0Eo

+-»

(/) oo< cE3CE<

to

0)

0)

enoc

(03oo

OS

CMa32o ^ ^ a m o " E ocooco c3o

(0oEo

(0o

"5)

d)c

0)

0)£ o

SS

O 3 "

0) .E

0, « o o ^ E o S • a a

0)

+-» Sto ^

CS (0 o o

C^

(D ^O s <^ 0

(f> 2

Q. 1 ^"^^ O 0 3 O 0 o o h

C

(0

19


0)>ooOcE0

Q.X

UJ Eo

a! oX

0)

CO

(0I 3o oE

3? o

i3c

"5) i = c o

G)CE _ p ° §

75o

5)oE

(0c>0 co

13Xo

D)6Ecoooo

CO

(0cE -d

II >oo

JQOc ^ o

(/) ooo

>^

CD

O 5 ^

**" (0 C O = 1 % « > c

!E c o o <o ^

-5

(/) oo oo

(5

V.O

(0E ^ o ^

iJ .E

22
o

(0o

Q.a<coE

0)

£LX

LLI E0

to

CO ^ ll

CD

— 0 CD E o

CO

05 a.c0E

Q.

LU

CO

-I—' CO E 0O

00

inE

Q.

OO

Q.

COD)

"DCo0

CNJ

O CM

Gi

ID

O in oo o

CO

*—0

0E

CO D0co

C/) 0co2o N

CO0

(/) co

Q.

(/) 0oc00 0>O0

COco

Q.

(/) 0 NXO 00

C/)

(/) COI c

>. 0 o m O) ±i ^ C c/) > CO • 0

u-o

COCO

CO Q 0 -a

J- CO D) "D C 0

CO E i

CO <^ • • CN

CO

COOcOoI

U-O

CO CD

COdo «- CO

,>S0

h- CO c

(0EI<d coCoO o

_ooco

0)

23

C/)

0)o

Q.O

reoc

(0o

0)

52>c<

(0oo

(0o

Q. >

IS

(0

(00o

is !5 c

(03I E

(0

llO "O

0) o

S.-Q £ o 5 o = s

0) 0

CD P O 3 (!) W £

0) ^ 3 O

(0 o J E ^ X o 2 — Q. = "2 E S o O

O

-C 0) X > o o ^ o

T5 ^ O O 0 O

(0

O

0)

:Q

—m 0 W 0 o ti o O 3 (0

OJ

O t

.E £ S 2 ^ O "+3 O

0) o =»

(/) -o E

.52 8

<D .E ? ® o o

<<> /It 0) £ E t o % Ko

(/) D)

<D .Eo ^ < o C

(0

25
u<ao o

J230

to0C0

DQ 5osoeoU

;^s

C/5

-t-> Ohoo

-ao

03

CO o

00 o 00

03Oo03COoU

03

fiO

fioU

03

fiO• I-H *-> • I ^3

fiOU

03

fiO

fioU

03

fiO> • 1—(

fioU

03

fiO• 1-H 4-> • 1—I

fioU

fi • ^

-fiO

03

-fi

fio

03

-fion

fio

OSB

00

<U

fi • -H -fio

03

-fi

T3

fio

03

-fi

en

$-1

fi • -H

-fio

03

-fi

tin

^3

1)

fio

03

-fi

tin 2"a)

(NI 00

Ohoo

03

fi

a>

fi • r-< -fio

03O

_ I

26
0)

0)

13oI

0)

0)

(0

(0mS 4-' S E

^«o ^ n E

0)O 0)

Sa H 0

(0

0)

"OcoEXoa

Q)OO

C8

0)

(5 o

0)c1 = X c o ic oo cpoo oc1oE

(00

(0^ .E — -o =o 1> 5 o

il

"O (I) s s

(0 (0

a. 0 0 a o a < o o 0

Q.

O

Q. ^

8^" £ 0 O >^ r </) 0 OE

CO £ 0 a 2 -o 0 0 ^ oaX0

(0 200

(0 I

13 0 0 o o O (0 D (0 c

(0E

t:

(0 <d

>»

D)OOcoDC

(0

0>uc

.2o

CO

(/) Q)OO

27

0)co

15

"O

(0

0)o2

Q.I c0

(0o

^1

IIS o

-45 CO C o

0) S 2 o • o O ^ £< o <

OhHC

01

29



33
/
35
'A/

/
39

0)

(0

iSO 0 c

(0 > c O E o

•M . (SEE 1 flj fl) £ w 2 m >

(/)

O (0 3 JS 0 (/) o — .2 ^ = CO

0)

c^ < o c

(0EX<d

>*ooocoHc

CO

0)oc

0)o

(0

(A

(AOoooc

(0c < o cc re O

(0

41
c

(0

/
(0o .S

0)E

Q.Eo

0)c23Oco0>o

(0oo

iSCm>< com*v

Q,Cc

*w

0)cooO

O)c o

(0 o

(0 0) oc o

"5)

0) o

cs

0) an E o o • om

15co1c

(0c

'5

00

O 3 —

O) d)

0.2 6 2 • a

OS c o 5 ^ (/>

CD %

1^Q. —

(/) <5 2 o ^ O JZ > 2 o >

(1) ^ CO o *t <0

=3 E

CO «/) .= o c .E

.2 E c <o —

(0 o <o

(0

Q.

0) 2 CO

(0

O E 5

"I^

0)

OS £ co o >o ^ 3 +^ O c OE

iS o3C

(0

43
0 00

1!

(/) 1 o

CO

.s « «

(0 c

0) (0 2 -o

<0

(1) #^5

il oE o3C

(0

44
ooo

(0oc

mmm o

ISo

'35

(0

iSo i 1 I as 1 1 < Ua W So s ^ o

fa.

GO

00No

a, on

ON

IT)No

CO

CO

On

inN

p3O

N

CdOu

1/3

00

00

ONN

c3O

0)

(UOh

00

ON

inN

C3O

Oh

in

in

(U

ON

inNO Er

(0 <d>o

_ooco

h-cooc

Q)o

CO

in

(0ouoccc

45
i .2

s1

u o

t2

^ I

a

u e

u

o

s »-

U

o

H

o o

00

o

Q

<^

o

<

Ph

C/5

00

IT)

O

Q

o

0)

aion

Oh

ON

OIT)

00

OSo

Q

o

Oh

O

oo oo

IT)

o

Q

o

Ph

(L) Oh

c;/5

O

Q

o

COOh

Oh

00

On

o

Q

o

<

D

Oh

P

0)

C/l CO

Oh

'sio

Q

o

<

COCO 4_,

i-i CO Oh Ph

D

a

< o

o^

(0

0^=

46
9i

C3

03O - 1

C3&S

fi i .2 u fi u o u fi o s ^

1^

QoH

in o

oo

in oo

00

oo o

60

CO

(DO cw

on

CiO 0)

bO oQo oQo

OS

t)0 o

OhO

Oh <

in 00 On

O o

re3C

(0

47
9i

a 1 I - 1 < U

O

g .2

4^

ii 1o

1^Q

C3oH

00

00oo

03

-a

0)

on

(N

00oOo

03 <

(N

(N

00

(N

C3oQo

03

CO

(N Qe c

(0£

(0 <d

>»oooc

s:o0HDCmooc0o

(0

0)ooc

(0

U)c ^ < o

1" o

CD< 3c

(0

48
c

49
co3Ocoo oo

Q)

Q)

Q)c c

<0oo

5) s

(0

Q)C

is <o o .52

<^ S • Q)

Co

.Soo©

CO

.12 .2 ?o ^5 o 2

JTJ

O

O

O

Q) 2

ispC

Is

Q)

Q)

O

l-i T S OE

(0 c

(0E

CO X<d

>»

Uiooco

I-cooc

Q>O

(0

a>ooc

(0O)co3C

z:

O

50 /
oE

0)

0)ooo

(0ocoic

0)Eo

Q.E3 230

(/) 0O^ E

CO

CO

(Dos

CDO

"5

c:

:3

CD

.§e

.COo

CO 5

CO

CDo

c:o 1 1 £

COo

CD

.c:CO

CD

CO

.2

cl

<D 5

CO

ID

CO

c:

c:

COo

CO S

COo cc ^ "53 1 8

CO c

CO

COo

CO Q ^

51
0) H—

0

Q.Eoo

D)c0

030-f—

0EE0

ifi co

Q.0E

05

OS

C/) 0g>oi_

CL

i2

OJ

c/)

</) 0o0c0

05

C/) -I—< c0300

05

05

05C

.9

-t—

D

052

Q) 3

Q. -»—

O

Q. . —C C/)

05 o ^ o 0 o ^ o ED0C

M—0DI oDCO 0 = -7= OX0 coEE8 O 05

goE -B Io0

05

Q.

05

0)o o

T3 ^

05

<0 CO ^ E §1• C/3 o

'•4-» c0£ E O (f) 0 0

8-1O £ 2 ^ 0

-D0 "U E 05

v§ _0 0 JD

CO "to O (1) >- 05 ^ 0 B ^ O — 05 DC

05

C

05

C i-3 CO CO

<^ 0

CO ^ « 0

<0 SI oDc

05c

050

03 50O

050£D0E0

Q.

COc

03E c

05 O) X 0

UJ

•c

:3 o So 0

COc0 E

''0

B CO CO

05 ^ o c ^

05 c E

05

O

D) Q. = TO ^0 ^CD ^ CO 0cE

05X0

05 CO

050OC

CO0

CO ;g0 c00 O 0 0 -C

CO _cf O CO — o >.

1-8.^ c 0 oC i

52
NUMERICAL SIMULATION OF RESISTANCE SPOT WELDING

PROCESS USING FEA TECHNIQUE

C. Srikunwong ' ,T. Dupuy ,and Y. Bienvenu

ABSTRACT

2-D axisymmetric finite element models incorporating electrical-thermal and thermalmechanical coupling procedures were developed for resistance spot welding (RSW) process

simulation. A commercial finite element code, namely SYSWELD®, was utilized for these

simulation purposes. The coupling procedures can provide a more realistic and efficient computational approach accounting for the variation of contact size; particularly for the

application of curved-face electrodes producing a spot weld. The temperature dependency

characteristics and properties of both sheets and electrodes were also taken into account

throughout the study. The welding schedules based on practical aspects of similar two- as well as three-sheet assemblies were considered for the entire of process. Not only the

utilization of pulsed direct current but also that of pulsed alternating current was utilized in order to efficiently achieve the industrial protocol. The experimental study was centered on

nugget formation. The validation for the nugget development was determined in the case of

pulsed direct current welding.

The impact of pulsed alternating current welding combining supplementary post-heating

pulses on the nugget size as well as on the thermal history was investigated. It was concluded

that both heating and cooling rates depend strongly on the position of weld. The results of

electrical-thermal analysis were discussed in view of the thermal history during welding, with

particular regard for different types of welding current used.

INTRODUCTION

Resistance spot welding (RSW) is widely utilized as a joining technique for automobile

structure due to flexibility, robustness and high-speed of process combining with very high

quality joints at very low cost. Not only heavy gauge two-sheet assemblies are joined by this technique but also stack-up sheet assemblies can often be encountered in the application. In some cases of heavy gauge two-sheet joining, the use of a common continuous current signal

is sometimes not efficient to construct the desired weldability lobe. The pulsed welding

approach then becomes an other choice to achieve this purpose. The pulsed welding current based schedule is sometimes recommended for heavy gauge and stack-up assembly cases

associated with some welding signal modification in order to improve weldability and

mechanical properties of spot weld. The pulse current used can be medium frequency direct or alternating current pulse. Other adapted current signal such as down sloping, quenching or post-heating can be also introduced to a required welding current signal. These modifications

become a common convenient technique for the improvement of weld mechanicalmetallurgical properties in high strength steel joining (Ref 1). The use of pulsed welding has

many advantages in heavy gauge sheet joining including the stability of nugget development

characteristics and the reduction of electrode wear.

* CRDM, Sollac Atlantique, ARCELOR Group, BP 2508, 59381, Dunkirk, France * Ecole des Mines de Paris, BP 87, Evry Cedex, 91003, France

53
The recent development in finite element analysis for RSW numerical simulation is well

documented in the literature (Refs. 2 and 3) showing that there is a significant change in the contact radii between electrode-to-sheet and sheet-to-sheet interfaces during welding stage.

This has significative impacts on thermal history, nugget formation and thermal stresses in the

assembly. Therefore, it is vital to implement a coupling procedure between electrical-thermal

and thermal-mechanical modules in order to capture this physical interaction and produce a more realistic predictive model.

RSW Computational Procedure

Squeezing Stage

Mechanical Analysis Stresses/Deformation Contact Area Variation

Start of Welding

Electrical-Thermal

Analysis Thermal history Size & Form of

nugget

Current distribution

Electrical filed

Thermal-Mechanical

Analysis

Stresses Deformations

Elec. Displacement

Contact pressure

End ofHolding

Electrical-Thermal Post-treatment Thermal history

Size, Form,

penetration of nugget Heat affected zone (HAZ)

Thermal-Mechanical

Post-treatment

Stresses Deformation

Elec. displacement

Figure 1: Schematic illustration of computational procedure

The aim of this study is to obtain a better understanding of the influence of process

parameters for heavy gauge sheet joining with the use of pulsed welding current. The features

of the coupling procedures can be described by loop sequential computational procedures of

the nodal temperatures transferred from the electrical-thermal analysis to the thermomechanical analysis in order to compute the thermal stresses and assembly distortions. On the

other hand, the stress distributions associated with assembly deformation are then transferred

back to the next electro-thermal computation step in order to up-date the variation of the contact size and pressure. These successive sequential loops are cumulated until the end of

RSW process. The computational procedure employed in this study is illustrated in fig. 1

.

/

54
FORMULATION FOR MODELING

Structure Modeling

A representative assembly of electrode and sheet utilized for analysis as shown in fig. 2

illustrates a half axisymmetric finite element model for electrode and sheet assembly, which is considered for both electrical-thermal and thermal-mechanical analyses. 2-D axisymmetric

models of two- and three-sheet joining incorporated with the curved face electrode of 6mm

and 8mm.-diameter are constructed. Both electrical-thermal and mechanical contact elements

are specially treated at electrode-to-sheet and sheet-to-sheet interfaces.

(a) (b)

Electrode Th6 (Ola. Baa) Elecircile THB (Dig. Baa)

(c) (d)

Figure 2: Illustration of structure mesh models used in analysis

Figure 2a: Curved-face electrode of 6mm-dia; Figure 2b: Curved-face electrode of 8mm-dia

Figure 2c: Three-sheet assembly mesh model; Figure 2d: Two-sheet assembly mesh model

Electrical-Thermal Modeling

RSW process is a resistance welding process governed by Joule heating effect with a concentration of the heat generation at the interface between two solid bodies in contact while

passing the current. This heat further propagates into these bodies by conduction heat transfer mode associated with the imposed thermal boundary conditions. Electrical-thermal governing

system equations are presented in (1) and (2):

p\—c^/v(;i.gradr) - gradV.cr.gradV - g = 0 (1) ot

fi?/v(c7.gradV) = 0 (2)

Where r,vare the temperature and the scalar electrical potential, respectively. p,Aand

(7 represent the density, the thermal conductivity and the electrical conductivity of the

medium. The temperature dependency characteristics can be taken into account in these

equations. H is the enthalpy also with a temperature dependency. The full coupling between

electrical and thermal phenomena can be governed by the term gradV.cr.gradV in the heat

equation. The modeled alternating current signal used in the analysis is shown in fig. 3a.

55
^ .^^aVWEE iFgl MEjg I !tt ^'fi'LA^^f^^l^J

1 . .w

tl TM-

4. —

i|'t

/

•-] , r 1 ' . 1

1

'

' .' I

(a) Pulsed welding current: I[t] (b) Welding force: F[t]

Figure 3: Modeled welding signals used in analysis

Thermal-Mechanical Modeling

The electrode force as illustrated in fig. 3b is modeled from the welding force signal. The

mechanical boundary conditions (Ref 4) are the electrode force applied at the top surface of

the upper electrode by assuming a uniform pressure distribution across the annular end and

the vertical nodal displacement of annular end of the lower electrode, which is constrained

similar manner to that of practical weld. The elasto-plastic Von-Mises criterion without

deformation rate dependency is defined for sheet characteristics. The non-linearity due to temperature dependency of sheet properties and contact characteristics including transient

computational approach are considered for this study. The three governing equations, namely,

the compatibility condition, the constitutive relation, and the equilibrium equation in cylindrical co-ordinate are discussed elsewhere (Ref 5).

EXPERIMENTAL PROCEDURE

Two sheet grades of ARCELOR, a Transformation Induced Plasticity (TRIP) grade and a non-coated drawing quality Low Carbon Steel (LCS) sheet are utilized in this study.

Properties of both sheets are given in Refs 6 and 7. The metallurgical examination is conducted only for the low carbon steel sheet joining.

Table 1: Welding schedules uti ized in the studv

Welding conditions Current: [kA/Cycles]

Elec. dia. Configuration Force

(mm)

1) 8 2sheets:(LCS) 11.2-DC/4(6+2)

2mm-thick 400 daN

2) 6 2sheets:(LCS) 8.97-DC/4(6+2)

2mm-thick 400 daN

3) 8 3sheets:(LCS) 10.6-DC/4(6+2)

2mm-thick 450 daN

4) 6 2sheets:(TRIP) 7.80-AC/3(7+2)+6.5xl7

1 .46mm-thick 500 daN

56
Welding schedules in table 1 indicate the utilization of pulsed welding current. In the case of

TRIP steel sheet joining with electrode face diameter of 6mm., alternating current with a magnitude of 7,80 kA is applied for 3 pulses, each pulse has 7 cycles of welding plus 2 cycles

of current shut-off. Furthermore, the post-heating current is then applied for 1 7 cycles with a magnitude of 6.5 kA. The aim of post-heating current application is to achieve the good

quality of residual metallurgical phases and minimize the weld fracture of HSS sheet joining.

The as-received sheets are cut to 50x50-mm coupons. Electrode conditioning prior to welding

is performed for 50 welding points with bare sheet. The trial welding tests are then conducted

in order to determine the expulsion limit. These trial welding conditions are based on the

French Industrial Standard (Ref.8), which is considered as welding schedule guideline. The

welding schedules, just below the expulsion limit, are used for three welding coupons and for each pulse in order to examine the formation of nugget relating to configurations. The no- expulsion welding of each pulse can be verified from the force and the displacement signal

monitoring on the LABVIEW® window. The effective current magnitude is obtained from the

MIYASHI® current signal recorder. Nugget development kinetics can be further examined by

sectioning the spot after each interrupted pulse. The polished axial sections of spot welded

samples are etched with picric acid to determine the fiision line or the nugget contour. This

etchant is suitable for the examination of the fusion zone of low carbon steel spot welds.

Quantitative macro-photographic measurements are made for the nugget size.

EXPERIMENTAL RESULTS AND DISCUSSION

Influence of Process Characteristics on Nugget Formation

Nugget development kinetics for two and three LCS sheet joining of 2mm-thick at the end of

each pulse is shown in fig. 4. As expected, both the height and the diameter of the nugget

increase at the end of the first two pulses. During the third and the fourth pulse, the nugget

expands more in diameter than in height. The indentation on the sheet surfaces and the sheet

separation can also be observed. The influence of elecfrode face diameter on nugget formation

is demonstrated by comparing case 1 and 2. It is revealed that the increase of electrode

diameter face leads to the increase in magnitude of welding current by around 2.2kA if the

electrode face diameter of 8 mm is used instead of 6 mm. This is due to the enlargement of

contact size reducing the concentration of current flux at faying surface. The utilization of

smaller electrode face diameter results in remarkable indentation onto sheet surfaces at the

end of welding. Concerning the nugget formation kinetics in the case of two-sheet joining, the occurrence of nugget at faying surface is already observed at the end of the first pulse. In the case 3, instead of initiating at center of three-sheet assembly, the hot zone originates in superior and inferior regions at the end of the first pulse but the nugget does not start forming

yet. For the latter pulses, nugget penetration and development also show a trend similar to that of two-sheet assembly case. The dissymmetry in the upper and the lower nugget diameters can be found before the saturation of nugget diameter at the end of the fourth pulse. However,

both symmetrical or dissymmetrical nugget development can be observed for three-sheet

joining case. The sheet edge separation between faying surfaces is slightly different. The

decrease in current magnitude for the three-sheet assembly comparing to that of two-sheet

joining can be explained by the increase of bulk electrical resistance with the increase in number of sheets.

57
Figure 4: Illustration of nugget formation of two- and three-sheet assembly at the end of

each pulse (Number of pulse is indicated on the macro-photograph and see also table 1 for the case study)

COMPUTATIONAL RESULTS AND DISCUSSION

Influence of Process Characteristics on Thermal History

For two-sheet joining case, the temperature history at different positions demonstrates the same dynamic response to the type of welding current used. The drop in temperature during

current shut-off can be obviously observed on temperature evolution and markedly seen for the positions located in the nugget region as illustrated in fig.Sa. The instantaneous significant

increase in heating rate is found during the first pulse, particularly at the beginning. In contrast to the heating rate of weld center for two-sheet joining, there is no significant change

in heating rate during the first two pulses for three-sheet joining as shown in fig 5b. An

insignificant variation is seen for weld center thermal history during the current shut-off

between the first and the second pulses.

For both two- and three-sheet joining cases, There is no variation in thermal history for the

positions located far away from the nugget and the HAZ, i.e. r=8mm., during the weld stage.

Unfortunately for the sheet joining with RSW technique, it is not easy to attain the same value

of the maximum temperature in order to compare the thermal histories. This is due to the

difference in the inherent welding parameters and the configuration used.

58

)
2500

2000

f 1500

I

S, 1000

E

(U ^ 500

0

2500 _ 2000

uo^ 1500

D

« 1000

E

u^ 500

0

0 20 40 60 80 100

Time [Cycles]

Figure 5: Influence of process characteristics on thermal history considering at the

upper limit of weldability lobe

Figure 5a: Influence of electrode face diameter resulting in welding current adaptation

and consequently on thermal history

Figure 5b: Thermal history in two- and three-sheet joining cases

Influence of Post-heating Current on Weld Geometries

The nugget geometries and sizes at the end of each pulse in the case of TRIP steel joining are

illustrated in fig. 6. It is obvious that the nugget develops until the end of pulsed welding. The

peak temperature at the weld center is found at the end of the last or the third pulse. After

that, there is no significant evolution of heating rate during the post-heating stage. It is shown

that there is no further development in nugget size during the application of post-heating

current and this is contrast to metallurgical phase evolution in the HAZ during this supplementary stage. Let us examine a node located inside the nugget and near the fusion

line, i.e. node at y=l, 168mm as shown in fig. 7b. The maximum temperature of this node is about 1535°C at the end of the welding process. The temperature drop can be also observed during the current shut-off. The thermal history of

this node increases again during the post-heating stage but with a lower heating rate than that experienced in the assembly during welding. The maximum temperature of this node reaches

^^) "-"*~Case 1 @weld center Case2@weld center ^^^^Casel@r=4mm Case2@r=4mm

' Casel(3)r=8mm — - - —Case2@r=8mm

20 40 60 80 100

Time[Cycles]

59
about 1354°C at the end of post-heating stage. This reveals that the simulated nugget size at the end of post-heating will be smaller than that obtained at the end of welding. It is worth

noting that the appearance of maximum nugget height and diameter is an irreversible

phenomenon and only takes place at the end of welding. Therefore, this discussion can be

supported by the occurrence of maximum nugget diameter at the end of welding stage with

the examination of thermal history. Fig. 6d shows the simulation result of the smaller size of

nugget diameter at the end of post-heating stage than that predicted at the end of welding as

illustrated in fig. 6c.

. . " ISOVALEURS (c) , Mm - 17.122'

@End of First Pulse @End of Third Pulse

/r_\ ' ISOVALEURS (b)

. IRcr. GlobBll

Min - 1 7 , I 29:

((\\ ISOVALEURS ; V Temps I .BQ

[RoF. Global]

Mln - 17-.23B,

J —

,

MtiK

-

lBBa.:|

@End of Second Pulse @End of Post-Heating

Figure 6: Predicted nugget development kinetics using post-heating current: case4

It is obvious that more important heating rates can be found for the nodes located along the

axis than for the nodes located along the radius. However, the temperature history considered

at the position located outside and near the HAZ zone, at the position (r = 4,50mm) as shown

in fig.7, is increased even during the current shut-off For the positions located sufficiently far away from the weld center (r=5.10 mm), the drop in their thermal histories cannot be

observed and the temperature increases continuously during current shut-off and post-heating

stage.

The simulation results reveal that the number of pulses and the magnitude of post-heating

current should be prudently selected while practically examining the mechanical and

metallurgical properties of weld.

60
2500 ^ 2000

o

^ 1500

1 1000 ^ 500

0

0 5 10 15 20 25 30 35 40 45 50

Time [Cycles]

2500 ^ 2000

uof 1500

I

a 1000

H

o^ 500

0

0 5 10 15 20 25 30 35 40 45 50

Time [Cycles]

Figure 7: Influence of post-heating current on nugget size at the end of welding: case4

Figure 7a: Average thermal history at different radial position

Figure 7b: Average thermal history at different axial position

Residual Stresses in Assembly

The slide-line mechanical contact element without friction is defined at sheet-to-sheet and

electrode-to-sheet interfaces throughout the computation. This contact condition however may

not be a very realistic approach for the appearance of nugget at faying surface. In fact, when

the nugget starts forming, the faying surface is joined by the molten mold. Therefore, the contact condition associating with the occurrence of nugget should be the sticking contact

condition. The novel mechanical contact approach is under development with the

modification of mechanical boundary conditions at the different stage of process. The residual stresses for three-sheet joining case are shown in fig.8.

61
Predicted radial residual stress Predicted axial residual stress

Figure 8: Predicted residual stresses in assembly at the end of process: case 3

EXPERIMENTAL VALIDATION

For three-sheet joining case, the validation is carried out for the quantitative measurement of

nugget diameter appearing at the interfaces between the upper-to-middle and the middle-tolower sheets, namely Dupper@exp and Diower@exp respectively.

D.o

E

32

8,07 8

7,92 _L22 J 7,9

7,2 6,08

'3,2

'5,3

0 2 4 6 8 10 Nugget diameter [mm]

^D@predict ^Dupper@exp ODlower@exp

Figure 9: Validation of nugget diameter for three-sheet joining: case 3

Note that the nugget diameters increase significantly during the first two pulses and saturate

for the latter stages. Both predicted and measured nugget growth kinetics exhibit similar trend

for long welding time. The validation shows a quantitative agreement in nugget diameters at the end of welding, which are of 8mm corresponding to the diameter of electrode face used.

However, it is found that there is a discrepancy between the measured and the predicted

nugget diameter size, particularly at the end of the second pulse. The measured nugget

diameters for the upper-to-middle and the middle-to- lower interfaces are around 5.30 and

6.08mm, respectively. While the simulated nugget diameter sizes are found in an order of

3.2mm and show the effect of planar symmetry as depicted in fig. 9. This discrepancy may be

due to the inappropriate electro-thermal contact values at faying surface, particularly for the temperature extending from ambient temperature to 200-400°C. It is understood that the

/

62
faying surface contact resistance diminishes rapidly with temperature (Ref 9) and plays a

significant role on the nugget development. These electrical contact resistances also depend

strongly on the surface condition of sheet, the welding force and the temperature.

CONCLUSIONS

A finite element analysis based predictive model incorporating an electrical-thermal and

thermal-mechanical coupling procedure was applied to study the heavy gauge sheet joining by

the RSW technique. This model provides a better understanding of the effects of welding

parameters on the nugget development kinetics and on the thermal characteristics in the

assembly with the use of pulsed welding current schedules.

The main conclusions of this study are:

1) It is experimentally found that the use of the larger electrode diameter leads to an increase

in the magnitude of welding current due to a better distribution of current flux and reduces the

indentation of electrode face onto the sheets. Concerning three-sheet welding configuration, it well demonstrates the significance of the total bulk electrical resistance of sheet at elevated

temperature with the decrease in welding current magnitude. The nugget development

exhibits similar trend for long welding time.

2) The simulation results show the difference in thermal history experienced in the assembly

between two- and three-sheet joining while respecting the upper limit in the weldability lobe

before the occurrence of expulsion. The drop in thermal history due to current shut-off can be

seen on the temperature evolution for both cases and more markedly in the two-sheet joining

case. The thermal history depends strongly on the position in the assembly. For position

located sufficiently far away from the nugget and the HAZ regions, there is no impact of

current shut-off on the thermal history.

3) It is demonstrated that the appropriate selection of the magnitude and the number of postheating pulses has no effect on the final nugget size obtained at the end of welding. However,

the temperature history is slightly increased during the post-heating stage for every position in the assembly.

4) The validation result shows a good agreement for the final nugget size at the end of

welding in the case of three-sheet joining. But the discrepancy in nugget diameter size

development can be observed during the first two pulses before the saturation of nugget. This

may be due to the inappropriate values of electrical contact resistances introduced at faying

surface. Electrical contact resistance determination will be further conducted in order to evaluate the contact characteristics.

ACKNOWLEDGEMENTS

The authors would like to express their grateful acknowledgement to Messieurs G. RIGAUT,

and J. CLAEYS, the director of the Metallurgical Development and Research Center and the

head of resistance welding section, respectively, for their input during the -paper preparation.

Thanks also to J.P. DOUILLY, who performed all spot weld experiments in this study. The

authors wish to thank C. DJENKAL and D. MALEWICZ for their educative advice during

metallurgical preparation and investigation. The partial financial support of Association

Nationale pour la Recherche et la Technologie (CIFRE educational program) is grateftilly

acknowledged.

63
REFERENCES

1. Peterson, W. May 15-17 2002. Methods to minimize the occurrence of interfacial fractures in HSS spot welds. Sheet metal welding conference X. Stering Heights, MI.

2. Khan, J. A.; Xu, L.; and Chao, Y.J. 1999. Prediction of nugget development during

resistance spot welding using coupled thermal-electrical-mechanical model. Science and

technology of welding and joining. Vol. 4, No.4: 201 to 207.

3. Sun, X.; and Dong, P. Aug. 2000. Analysis of aluminum resistance spot welding

processes using coupled finite element procedures. Welding research supplement: 215-s to 221-s.

4. Srikunwong, C; Dupuy, T.; and Bienvenu, Y. Nov., 2001. A decoupled electrical- thermal and mechanical model for resistance spot welding. 15"^ annual conference of

mechanical engineering network of Thailand. Bangkok, Thailand. Vol.2: 76 to 84.

5. Murakawa, H.; Kimura, F.; and Ueda, Y. 1997. Weldability analysis of spot welding on aluminium using FEM. Mathematical modeling of weld phenomena 3 : 944 to 966.

6. Thieblemont, E. 1992. Modelisation du soudage par points . Ph.D. dissertation.

LTnstitut National Polytechnique de Lorraine, (in French)

7. Bobadilla, M.; Niederleander, M.; Nuss, C; Perrin, G.; and Selaries, J. Juin 1994.

Donnees thermiques pour differentes families d'acier. Rapport interne IRSID, Groupe Usinor.

(in French)

8. Caracterisation de la soudabilite par resistance par point de produits plats revetus ou

non, Dec. 1994, Normalisation franpaise A87-001 . (in French)

9. Vogler, M.; and Sheppard, S. June 1993. Electrical contact resistance under high loads

and elevated temperatures. Welding research supplement: 231-s to 238-s.

)

64
THE FINITE ELEMENT MODELING OF RESISTANCE SPOT WELDING

A. V. Gohil*, S. M. Patel

*

ABSTRACT

Resistance spot welding process is the most significant joining process in the automobile

industry due to its high speed and suitability for automation. One of the recent demands in the

automobile industry is to reduce the vehicle weight so as to improve the fuel efficiency and to meet this requirement, aluminium alloys is being considered as an important alternative for autobody material. The vehicle corrosion problem can also be squarely dealt with. However, unlike

resistance spot welding of steel, joining of aluminium through the same process has met with

serious difficulties, because of faster deterioration of electrodes. High electrical and thermal

conductivity, high shrinkage during solidification and the presence of natural oxide coating are some of the features that make spot welding process of aluminium alloys markedly different. At

a very high temperature aluminium chemically reacts with copper alloy (electrode material).

Subsequently random chipping-off of material from the electrode faces takes place and it results

in electrode wear. Further, the electrode life reduces drastically when spot welding aluminium

alloys. In terms of weldability, this is an extreme negative point since weldability for automobile

use is greatly dictated by electrode tip life.

Since the process is very fast, important information, such as transient distribution of current

density and temperature, are difficult to obtain from the experimental methods. Thus, it is aimed

at the present work to develop an integrated computer simulation model for analyzing resistance

spot welding process of aluminum alloys by finite element method. Several calculations have

been carried out for different welding current, weld time and electrode force and for different

surface conditions of aluminium sheets. Non-linear, temperature-dependent, thermo-physical

material properties have been considered. It is interestingly observed that in most cases the

nugget diameter is formed well within 0.02~0.04sec and further flow of welding current simply

increases the electrode face heating. Also, the initial surface condition influences the nugget

formation phenomenon to a great extent. Various other conclusions have been arrived at as a part

of this study.

INTRODUCTION

Resistance spot welding process is the most significant joining process in the automobile

industry due to its high speed and suitability for automation and thus, any new development of

this welding process is closely influenced by the demand of this industry. The need to reduce

vehicle weight, improve fuel economy, and reduce exhaust emission has led to increased use of

light weight materials such as aluminium alloys. However, many technical issues have to be

solved before the use of aluminium becomes commonplace in high-volume production. Unlike

" Department of Production Engineering, Shantilal Shah Engineering College, Bhavnagar, India

# Department of Production Engineering, Shantilal Shah Engineering College, Bhavnagar, India

65
resistance spot welding of steel, joining of aluminium through the same process has met with

serious difficulties. Aluminium is a very good electrical conductor with a bulk resistivity one

third that of steel. Joule heating is proportional to resistance for a given current. It is understandable that a significant increase in welding current will be required to join aluminium,

compared to an equivalent gauge of steel sheet. Further, aluminium has a high thermal

conductivity and the localized heat generated by the welding current will be will be conducted

away rapidly. It is therefore necessary to use short weld times. Aluminium alloys posses a surface oxide layer that varies depending on the prior thermal and mechanical processing. The

oxide has a high resistivity band high melting point, around three times greater than pure

aluminium. Therefore, spot welding of aluminium alloys has become an important research area

in the last couple of years both in the academic as well as industrial research laboratories.

The electrical resistance spot welding process for joining two materials at their common

interface is a complicated interaction of electrical, thermal, mechanical, metallurgical and surface

phenomena. In this process, electrodes press against two or more steel sheet and high amperage

current is passed through the sheet-electrode system. Because of the electrical contact resistance,

heat will be generated at electrode / work piece interfaces and faying surface. The heat at the

faying face melts the work pieces to form a nugget. To prevent melting at the electrode / work

piece interface, water is circulated in the cooling chamber of the electrode.

The current carrying zone in the sheet is determined by the region over which electrodes touches

the sheet and this, in turn depends on the electrode force and consequent plastic flow at sheetelectrode interface. The complete phenomenon is thus, an electro - thermal problem which is also

influenced by plastic flow in the sheet. Coupled with this are various types of nonlinearities

present in the system. For example, thermal conductivity and bulk electrical resistivity vary with

the temperature. Besides, the interface resistance along sheet - sheet interface and sheet -

electrode interface varies with various parameters in a very uncertain manner. Hence, a finite

element code for simulating the spot welding process which includes all those features

mentioned above is developed in the present work for modeling resistance spot welding process

of aluminium alloys.

PREVIOUS INVISTIGATIONS

Since the physics of the process is so complicated, it is quite understandable that very little was

published in the open literature on the finite element modeling which covers these many aspects.

In 1984, Nied [3] had reported a two-dimensional simulation model for analysing resistance spot

welding process of uncoated steel using commercial FEM package ANSYS. A coupled thermoelectrical and thermo-mechanical analysis has been tried. However, the contact resistance along

the sheet-to-sheet and sheet-to-electrode interfaces was neglected. Gould [4] reported a one- dimensional numerical model to calculate weld nugget development during spot welding of

uncoated steel. However, the model being one-dimensional, failed to account for the radial heat

loss into the surrounding sheet. Cho [5] had reported a two-dimensional, finite difference method

based heat transfer model for resistance spot welding process. It has been concluded from the

publications cited above that the resistance spot weldability of aluminium alloys is not yet fully

explored although there is now tremendous demand of the these materials to be used in the

automobile industry. The purpose of this present work is to develop finite element based

66
numerical model which also consists a nonlinear thermo - mechanical coupling to provide a more realistic simulation of the resistance spot welding process of aluminium alloys.

FINITE ELEMENT MODEL

Geometric Modeling

Considering a typical arrangement for resistance spot welding of two pieces of aluminium sheets,

the geometric representation of two identical electrodes simplifies the geometry of two -

dimensional axisymmetric model. Fig. 1 shows the finite element mesh structure used for the

modeling purpose. The mesh structure consists of 358 nodes and 297 elements. The element

mesh size at the end of the electrode and for the work piece is sufficiently refined to account for

thermal gradients in that region. A coarser mesh is considered in the upper region of the

electrode where the gradients are shallower because of heat conduction to the water - cooling

channel. Only one quadrant of the complete geometry has been analyzed considering the axial

symmetry of the sheet-electrode system in spot welding process.

Heat Transfer Analysis

Heat transfer in resistance spot welding process involves convective heat transfer as well as heat

conduction in bulk of the sheet-electrode system. The transient heat flow in resistance spot

welding process has been modeled as a case of axisymmetric heat conduction problem.

LA

r dr

rK

V dr J

1 d

+

r dz

rK + (2 = 5C

di

(1)

where s, c and K are density, specific heat and thermal conductivity respectively. All the material

properties are considered to be temperature dependent. The term Q refers to the rate of internal

heat generation per unit volume.

Electrical Field Analysis

The current density distribution in the sheet-electrode geometry (in two-dimensional cylindrical

coordinate system) can be represented by the following relationship,

1 d

p d r r d z p d z = 0 (2)

where, p is the electrical resistivity and J is the current density vector. The electrical resistivity is considered temperature dependent in the present work.

Internal Heat Generation

The internal heat generation at every point in the sheet-electrode geometry is calculated by the

following relationship,

Q = P (3)

67
Internal Heat Generation

The finite element discretisation of the complete sheet-electrode geometry has been carried out

using four-noded ring type isoparamtric, solid element with rectangular cross-section. Within an

element, temperature (T) can be expressed as,

{T}=[N, N,]{t] (4)

where, Nt, .... Ni are the shape functions (based on nodal co-ordinates of the element) of the

element. The transient heat conduction equation (eq.l) is first discretised and the discretised

equation can be stated as, mm + [s]^ + {F} = 0 (5)

where [H] is the thermal conductivity matrix, [S] is the thermal capacity matrix and {F} is the

load vector due to internal heat generation. Equation (6) is further discretised in time domain

following Galerkin's Principle. The solution of electrical analysis represents the elemental

current density distribution throughout the sheet-electrode geometry. These results are then used

to calculate the internal heat generation in each element and subsequently the heat transfer

equation is solved to obtain the nodal temperature distribution in the complete geometry. The

total weld time has been divided into a number of small time steps. Within each time step, the

electrical field analysis is carried out first to obtain the elemental current density and the heat

transfer analysis is done next considering the internal heat generation.

RESULTS AND DISCUSSIONS

A Simulation model has been developed and extensive numerical calculations were carried out to find out the nugget diameter, penetration, etc. for resistance spot welding of aluminium alloy

sheets using the FEM Software ANSYS. Fig. 1 shows the geometry used for the modeling

purpose. Only one quadrant of the complete geometry has been analyzed considering the axial

symmetry of the sheet-electrode system in spot welding process.

The sheet thickness used for the present analysis is 0.8mm and the temperature dependent

material properties for aluminium and copper electrode are shown in Fig.2 - 5. The sample

temperature distributions in the sheet-electrode system for welding current of 45 KA at different

instant of times are shown in Fig.6. It can be observed that the temperature isotherms are more

concentrated along the faying surface and the maximum temperature occurs along the sheet-tosheet contact zone only. The time histories of the highest temperature experienced by the sheetelectrode system at two different welding currents (35 KA and 45 KA) are plotted in Fig.7. The

development of nugget diameter with time for two different weld currents (40 KA & 45 KA) for

the same material is shown by the Fig.-8. It is observed that the nugget development process is complete within 0.06 sec and with further increase in time no more radial growth occurs. It has

been observed in Fig.9 that within 0.02 sec, the maximum temperature generated is above the

liquidus temperature of the sheet and with further increase in time there is no more rise in

A

/

68
maximum temperature. This occurs presumably as the faying surface resistance decreases and

heat generation becomes more heavily dependent on bulk resistivity and further due to the higher

conductivity of aluminium alloy heat dissipation becomes more with longer weld time. It has

been shown by the Fig.9 that the current density is not uniform throughout the sheet-electrode

geometry and hence hosts the importance of the electrical field analysis in case of resistance spot

welding process.

CONCLUSION

A comprehensive simulation model using FEM for the analysis of resistance spot welding

process has been developed. It has been observed that the finite element modeling of the

resistance spot welding process can provide good simulation, if the model includes the electrothermal mechanical interaction and good temperature - dependent material properties. The

results presented herein indicate that there is another alternative: the use of a realistic analytic

model. This finite element model so developed can calculate most of the resistance spot welding

responses in terms of nugget diameter, depth of penetration, the extent of heat affected zone, rate of heating and cooling, electrode face heating etc. Finally this FEM model will certainly help in

optimizing process parameters combinations in any industrial application of resistance spot

welding process. The authors are also intending to perform real time experiments so as to validate the theoretical results with in-house experimental data since such data are scarcely

available in the literature.

REFERENCES

1. Johnson, K. I. 1976. Aluminium in vehicle bodies. Metal Construction & British Welding

Journal 8(9): 392 to 395.

2. Ostgard, E. 1980. Spot welding of aluminium as delivered, Metal Construction 12 (2): 78 to 86.

3. Nied, H. A. 1984. The finite element modeling of the resistance spot welding process.

Welding Journal 63(4): 123 to 132.

4. Gould, J. E. 1987. An examination of nugget development during spot welding using both

experimental and analytical techniques. Welding Journal 66(1): 1 to 10

5. Cho, H. S. 1989. Study of the thermal behavior in resistance spot welding. Welding Journal

68(6): 236 to 244.

6. Brown, D. J. 1995. Computer simulation of resistance spot welding in aluminium. Welding

Journal 74(10): 339 to 344

7. Brown, D. J. 1995. Computer simulation of resistance spot welding in aluminium. Welding

Journal 74(12): 417 to 422.

8. Murakawa, H.; Kimura, H.; and Ueda, Y. 1995. Weldability analysis of spot welding on aluminium using FEM. Transaction of JWRI. 24: 101 to 111.

9. De. A.; Dom. L.; and Gohil. A. V. 2001. Numerical Modeling of Resistance spot welding of

aluminium alloys. Proc. International Conf. on Advances in Welding and Cutting

Technologv. Eds. The Indian Institute of Welding.

69
800

TerTperature(Deg. celcius)

Fig. 2 Temperature dependent resistivity

of copper

Fig. 1 . Finite Element Model for equal

sheet thickness

Fig. 3 Temperature dependent resistivity

heat of aluminium

u

E

E

>

*3U

3

•DC

Oo

0.5

0.375

0.25

0.125 H

0

1 2 3 4 5 6 7 8 Temperature (Deg.Celcius)

Fig. 4 Temperature dependent thermal

conductivity of copper

Fig. 5 Temperature dependent thermal

conductivity of aluminium

70
WW mii

Fig. 6(ii) Temperature Distribution as Resistive heating progress (after 0.8 sec)

71
c

^ 1200 n

Fig. 7 Maximum temperature generated Fig. 8 Nugget development at different

at different instant of time (sec) instant of time (sec.)

'^After0.04 Sec.)

Fig. 9 Current Density Distribution as Resistive heating progress

( For sheet thickness 0.8 mm and current I = 45 kA)

72

\/
Weld Pool and Solidification

73

THE POSSIBILITIES OF THE APPLICATION OF AN EXPERT SYSTEM IN THE

MANUFACTURING OF RESISTANCE SPOT WELDED RAILWAY VEHICLE

STRUCTURES

I. Borhy*, B. Palotas*

ABSTRACT

The satisfaction of heightened quality requirements for railway carriage bodies as complex

engineering structures are only possible by way of a comprehensive controlling of the entire

design and manufacturing process and the close cooperation of all experts taking part in the

process. In the design phase several different points of view need to be taken into consideration

and weighed in order to satisfy the various demands for the vehicle structure, which often even

contradict each other. In view of the complexity of the task to be solved, the development of an

expert system to be used in the area of resistance spot welding procedures is fully justified. In our paper we are discussing the topical questions of the development of expert systems assisting

the design and manufacturing of spot welded vehicle structures with optimum characteristics.

KEYWORDS

Case-based expert system. Resistance spot welding, Railway vehicles. Weld quality

requirements. Finite Element Model, Genetic Algorithm

INTRODUCTION

The market value (success) of a product is basically determined by how competitive it is. Competitiveness, in turn, is primarily a function of the costs associated with the product

throughout its entire life cycle, i.e. from its design and manufacturing, through its operation and

maintenance to decommissioning. In the design and manufacturing phase, several different

points of view need to be taken into consideration and weighed in order to satisfy the customer's

various demands and expectations for the product, which often even contradict each other. The

achievement of an optimal result taking into account these various different requirements is a

difficult task, since in addition to the demands of strength, reliability, aesthetic considerations,

etc., attention must also be paid so that the product can be manufactured and inspected to specifications. An indispensable means of attaining these objectives is a close cooperation

between the design engineer, the technologist and the experts carrying out the inspections both in

the phase of product design and the manufacturing technology design. At the same time,

* Expert, TUV Rheinland InterCert Ltd., Paulay Ede u. 52., Budapest H-1061,

iborhy @hu.tuv.com

* Associate Professor, Budapest University of Technology and Economics, Department of

Mechanical Technology and Material Science, Bertalan Lajos u. 7., Budapest H-1111,

palotasb@eik.bme.hu

75
competitiveness also presupposes a reduction of the time devoted to the design phase. In view of

the complexity of the task at hand, the short time frames available, and the increased

expectations towards the experts participating in the project (thorough theoretical knowledge

combined with extensive practical experience), the use of expert systems in the design process

for the achievement of the objectives set forth is well justified.

The Department of Mechanical Technology and Material Science of the Budapest University of

Technology and Economics (BMGE) has been conducting significant research projects for over a decade in the area of computer-aided design of welding processes [1, 2, 3, 4]. Connecting to this

research, our project aims to promote the development of an expert system to be used in the area of resistance spot welding procedures. I this paper we intend to discuss some of the most recent

results of research carried out in this field.

RESISTANCE SPOT WELDING PROCEDURES

IN THE MANUFACTURING OF RAILROAD STRUCTURES

The chassises and bodies of both road and railway vehicles are built on the basis of the principle

lightweight construction [5]. Vehicle structures demand designs that are rigid enough and can

withstand fatigue loading, but at the same time have the smallest weight possible. The most

common manufacturing method for such vehicle structures is resistance spot welding, because of

the various advantages (efficiency, the availability of mechanization, uniform weld quality, etc.).

The questions of inspections related to the design and manufacturing of these vehicle structures

is a very current issue these days.

This is primarily the case because this industrial sector is characterized by large series, where the

questions of economic efficiency come to the foreground to a greater extent. The above

objectives can only be attained by increasing the quality and reliability of the vehicle structures,

while at the same time reducing the design, manufacturing, operation and maintenance costs. Competitive market conditions, therefore, demand a constant development of the materials used,

the design, the manufacturing and the control of production, as well as the application of the

most advanced design and manufacturing methods. Without such development success on the

market - maintaining and strengthening the market position by gaining further customers - cannot be achieved. With regard to the fact that vehicle manufacturing industry is one of the

driving forces of global economy, continuous development is justified.

This is secondly the high quality standards set for the vehicles and various components can only

be ensured by controlling the entire manufacturing process from design and the selection of

materials to manufacturing and subsequent checking. This justifies the creation of strict regulations regarding the design, manufacturing and checking of vehicle structures, which

includes the increasing of the reliability of welded joints. The increasing demands for product

safety also justify continuous development.

On the basis of the above it is reasonable to place the customer's expectations in the focus, to analyze and quantify them (i.e. convert them into index numbers), thereby endeavoring to design

a structure with the most optimal parameters. In other words, the satisfaction of the customer's

expectations may be best ensured by solving an optimization task, which places quality in the

focus. Such a quality-centered optimization task justifies the development of expert systems for use in resistance spot welding procedures. The application of an expert system can provide,

among other things, the following benefits:

/

76
• help achieve the strict quality (reliability) requirements set for vehicle structures;

• facilitate the control of the entire design and manufacturing process (from design and

the selection of materials to manufacturing and subsequent checking); and

• increase the reliability of welded joints.

SCIENTIFIC PRELIMINARIES

The literature of methods, optimization procedures and expert systems used in the design of

vehicle structures in very extensive [6, 7, 8]. It cannot be our objective in the confines of the

present paper to describe in detail the theoretical background of optimization procedures and

expert systems, and so we will only briefly refer to some of the preliminaries.

The ultimate objective of several decades of research is intelligent problem solving. An expert

system is a computer-based problem solving system that provides assistance in the analysis and

solving of highly complicated and complex problems by evoking the problem solving procedures

used by human experts [9]. The intelligence of the system is best manifested in its ability to adapt to new circumstances, which is closely related to the use of experiences acquired earlier

[10].

The problem solving method of expert systems is basically determined by the form in which

knowledge is represented. Case-based design implements a system whereby the elements of

earlier designs are re-used in the design process. A complex design problem can thus be solved in

shorter time by appropriately modifying the results of a similar, but already solved problem.

Keeping in mind the complexity of the topic, in our paper we wish to discuss some topical

questions of the development of an expert system assisting in the design and manufacturing of

spot welded vehicle structures. The practical applicability of the development results are demonstrated through the example of updating of vehicle structures in the plant of Bombardier

Transportation in Dunakeszi, Hungary. We intend to demonstrate how the experiences gathered

in the design and manufacturing were utilized in the development of the expert system. We

intend to show how the special welding technological requirements are used in the design and

manufacturing of the side walls. We intend to show what measures had to be taken and what

questions had to be answered in order for these requirements to be taken into consideration as early as the design process and what preparations need to be taken during the design of the

technology and the actual production, so that the welded joints would satisfy the quality

requirements set forth.

DESIGN REQUIREMENTS

Bombardier Transportation was commissioned to build, rebuild and/or modernize various types

of railway passenger wagons in its Dunakeszi plant. Among other projects, the updating of 136

pieces of Bhv-type suburban passenger wagons at the orders of the Hungarian State Railways

(MAV) and 79 pieces of second-class passenger wagons at the orders of the Greek Railway

Company (OSE) can be highlighted (Figures 1. and 2.). The objectives of these projects included

the improvement of the quality of rail transportation, the increase of passenger comfort, and the

raising of the technical quality of the aged vehicle fleet.

77
Figure 1.: Modernized, Bhv-type, suburban Figure 2.: A second-class passenger wagon

railroad passenger railway carriage the orders of the Greek Railway Company

In view of the loads on the structure, the geometric configuration of the vehicle, the material

quality and typical thickness of the side plates and the framework, as well as the minimum

expected lifetime, we strove to find the optimum structure satisfying the customer's requirements

in the design of the spot welded side walls. For this purpose, we have:

• analyzed and quantified the customer's requirements, and defined the limit values for

each parameter (Table 1.); • prepared a finite-element model of the closed frame;

• examined the stresses and deformations arising in the closed frame as a result of the

loads;

• performed examinations in order to define optimum weld distribution (geometric

position and weld distance).

No. Requirement Index number Limit value Index number In optimal case:

1. Strength adequacy of spot welds

Load on spot welds

[kN]

Shear/breaking

strength characteristic

of spot welds [kNl

decreases

2. Aesthetic requirement:

deformation for entire

vehicle structure

Extent of deformation

[mml

Maximum deformation

allowed by standard

decreases

3. Production cycle time Number of spot welds decreases

4. Extent of weld's utilization Utilization factor 100% increases

Table 1.: Customer's requirements and quality indexes

78
THE FINITE ELEMENT MODEL

The finite element analyses were carried out with the application of the MSC.Nastran/Patran v. 70.5 software package. The loads that the vehicle structure is exposed to were determined in

accordance with the customer's requirements, on the basis of the MSZ EN 12663:2000 standard

and the provisions of UIC 566. In the course of the finite element modeling, we examined the

stresses and deformations resulting from normal operation and extraordinary loads as defined by

the standard (Figure 3.). A detailed review of the examinations performed is contained in [11,

12].

Figure 3.: Stresses and distortions in the carriage body

Figure 4. shows the finite element model of the spot welded joints of the side wall windows and

comer frames. On the basis of earlier operation experiences we can state that the lifetime of the

vehicle structure is significantly determined by the behavior of the environment of the side wall

windows and comer frames under stress loads. Deterioration processes starting from the

environment of the side wall windows and corner frames have a fundamental effect on the

lifetime of the vehicle stmcture; therefore, special attention had to be paid to the design of

appropriate connections in this area. The forces affecting the individual welded joints with a given density of welds is shown in Figure 5.

79
Figure 4.: The finite element model of the

side wall windows and comer frames with

resistance spot welds

9161 Figure 5.: The loads on the spot welds

The weld quality requirements and the testing instructions were determined with the results of

the finite element modeling in mind. The results derived from the modeling were checked in the

series of measures taken on a completed carriage body made possible by the Institute of

Development and Experiments of MAV Rt. Upon the evaluation the examinations we could

conclude that the spot welded joints made with the standard characteristics satisfy the expected

loads affecting the carriage body.

THE CASE-BASED DESIGN SYSTEM

The experiences gained in the design problems were utilized in the development of the expert

system assisting in the design and manufacturing of spot welded vehicle structures with optimum

characteristics. The focus of our research was the various methods of knowledge representation

and inference that can be used in the expert systems. Figure 6. shows the diagram of a case-based

expert system developed for the designing of a spot welded vehicle side wall.

-A

/

80
User:

1.Defining optimization parameters on the basis of requirements for the

structure.

2.Rxing limit values for the

optimization parameters.

Geometrical model

Definition of parametric features

(indexing)

Searching

the case-base

Selection of previous case 11

Optimization using

genetic algorithm

A Case-base

Evaluating the results A

IT

Entry of results into the case-base

jr~y (teaching process)

Optimized structural model

(solution of problem)

Figure 6: Block diagram of a case-based design system

In the development of the system, we were looking for solutions to the following problems:

• A parametric system was developed for the definition of the characteristic features of

unique individual cases, i.e. for the coding of the cases;

• For the determination of similarities between individual cases, examinations were

carried out in order to introduce index numbers for similarities; • Studies were made for the modification (adaptation) of a similar design selected from

the case-base. A genetic algorithm was used for the performance of the optimization

process.

The results of the study provide sufficient basis for the achievement of the objectives set forth in

the introduction, and they also facilitate the further development of the model and its additional

industrial application. The experiences gained in the course of the development of the case-based

expert system also point out the directions of future research, since in the case of a weld made

with given parameters it becomes possible to define deformations and residual stresses. These

two characteristics are extremely important from the point of view of the production railway

vehicle parts, because the former affects the usability of the part, while the latter reflects the

effect of the given part on the whole structure; and furthermore.

81
SUMMARY

The paper discusses the possibilities of using expert systems in the design and manufacturing of

railway vehicle structures with resistance spot welds. After the demonstration of the importance

of the topic, the necessity of developing and improving expert systems that can be used in the area of resistance welding procedures is emphasized. The application of welding technology

requirements to design and manufacturing is shown through a concrete example. We have

presented an outline and the characteristic features of a case-based expert system developed for

the design of spot welded vehicle side walls, and references have been made to a possible

method of implementing knowledge representation and inference methods.

REFERENCES

1. . Brenner, A.; Palotas, B. 1989. Computer Aided Welding. BME Memoktovabbkepzo

Intezet. Budapest.

2. Borhy, I. 1999. Actual Considerations on Modelling of Resistance Welding Procedures.

Proceedings of microCAD '99 International Computer Science Conference, Section H

(Mechanical Technologies), p.: 25-30. University of Miskolc.

3. Palotas, B. 2001. Finite Element Modeling on Welding Processes and Welding

Phenomena. Proceedings of ll"^ International Conference on Computer Technology in

Welding, p.: 297-305. Colombus (Ohio).

4. Palotas, B. 2002. Modelling of Pressure Welding Processes by Methods of Mechanical

Forming. Proceedings of 3^^ Conference on Mechanical Engineering, p.: 229-233.

Springer-Verlag. Budapest.

5. Sostarics, Gy. 1991. Railway Vehicles. Tankonyvkiado. Budapest.

6. Pahl, G; Beitz, W. 1993. Konstruktionslehre (Methoden und Anwendung). SpringerVerlag. Berlin.

7. . Jarmai, K.; Ivanyi, M. 2001. Gazdasagos femszerkezetek analizise es meretezese. Miiegyetem kiado. Budapest.

8. Radaj, D. 2001. Integrated Finite Element Analysis of Welding Residual Stress and

Distortion. Proceeding of 6^ International Seminar on the Numerical Analysis of

Weldabilitv. p.: 469-487. Graz University of Technology.

9. Ignizio, J. P. 1991. Introduction to Expert Svstems . McGraw-Hill. New-York.

10. Savory, S. E. 1990. Expert Svstems for the Professional . Ellis Hoewood. New-York.

11. Borhy, I.; Schwartz, I. 2002. Practical application of FEM in the technology design of

resistance spot welding in railway production. Proceeding of 3*^^ Conference on

Mechanical Engineering, p.: 479-484. Springer Verlag. Budapest.

12. Borhy, I.; Kovacs, L. 2003. Application of FEM in the production of welded railway

vehicle parts. Proceeding of International Conference on Metal Structures . University of

Miskolc.

82
NUMERICAL SIMULATION SYSTEM OF DEVELOPMENT OF

ULTRA-NARROW GAP GMAW PROCESS

T.Nakamura* , K.Hiraoka*

ABSTRACT

An Ultra Narrow Gap GMA welding (UNGW) process with less than 5mm gap width has been

developed in which the arc occurring at wire tip is forcibly oscillated over the groove wall in the

thickness direction. To control the wire tip position, the low-frequency pulse current is used.

An UNGW process simulation system which is based on analysis of non-steady state wire

melting under low-frequency pulse arc welding conditions has been proposed to obtain defect

free joints and find out adaptive welding conditions. In this simulation system, the equivalent

electric circuit of arc welding and many factors on welding phenomena are conceded such as the

digging action by arc force, motion of molten pool, equivalent heat input in wire tip, super heat

of droplets, arc current-voltage characteristics and so on. This simulation system makes it possible to find out appropriate welding conditions for UNGW

which is characterized by many pulse parameters that correlate in a complex manner.

KEYWORDS

GMA welding, Ultra-narrow gap, Numerical simulation. Non-steady state, and adaptive welding

condition

INTRODUCTION

New type steel with various properties have recently been developed, and using these materials,

high quality and efficient welding methods that maintain the properties of the materials is expected (Ref. 1). To reduce deterioration of joint properties by welding heat and welding

deformation, small heat input welding method is effective, however high efficient welding is difficult for this welding process.

Narrow gap welding has been developed as a highly efficient and small heat input welding

method (Ref. 2). The smaller groove width is more effective to small heat input welding and

higher efficiency welding, however good welding is impossible with a groove width less than 5

mm. Because the arc is so unstable that the arc pole irregularly traverses up and down the groove

wall, and welding defects such as lack of fusion are formed. To obtain good joint by prevention

of unstable arc, mechanical weaving method in the direction of the groove width is used (Ref.

2). However, since the welding speed is limited if mechanical weaving is used, the high welding

efficiency, which is the advantage of narrow gap welding, cannot be sufficiently obtained.

We developed ultra-narrow gap welding (UNGW), in which stable and high efficiency welding is possible in the groove with a width narrowed until mechanical weaving in the direction of the

groove width becomes unnecessary(Ref. 3,4).The welding of the I-type joint with a groove

width less than 5 mm is achieved by oscillating the arc pole over the groove wall in the thickness

direcdon using low frequency pulse current less than 10 Hz.

National Institute for Materials Science,! -2- l,Sengen,Tsukuba,Ibaraki,JAPAN

83
To obtain both melting of the root area and concave surface beads, the choice of the amplitude

and position of oscillation over the groove wall in the thickness direction is important. To obtain

the appropriate conditions for oscillation in UNGW, it is necessary to determine many control

parameters such as the pulse peak current, pulse base current, pulse peak duration, pulse base

duration, frequency of pulse current and wire feed rate. Furthermore, arc behavior in the groove

strongly affect the molten pool behavior rand the oscillation of the wire tip over the groove wall

in the thickness direction. However, it will be impractical to determine appropriate parameters by

experiments, because a tremendously large number of experiments are necessary. Therefore,

GMA welding process simulation is useful to investigate optimum welding conditions and

numerical models for GMA welding has been studied (Ref. 5-8). In this study, we propose a numerical simulation system using a model of ultra-narrow gap GMA

welding. In the model of ultra-narrow gap GMA welding, non-steady wire melting behavior,

molten pool and arc behavior in the groove and GMA welding electric circuit were considered.

The appropriate conditions of UNGW with many parameters were studied using the numerical

simulation system.

NUMERICAL SIMULATION MODEL FOR UNGW

Non-Steady wire melting model for UNGW process

As the arc generating point (wire tip) is oscillated over the groove wall in the thickness direction

by using low frequency pulse current less than 10 Hz, the wire melting behavior is in

non-steady state (Ref. 9,10). Numerical simulation of non-steady state of wire melting

behavior is performed in the equivalent electric circuit of GMA welding shown in Fig. 1

.

The temperature distribution of wire is expressed the following one-dimensional heat

conduction equation on the fixed coordinate system.

dU_

dt + V, dU_

dx C y dX^ C y

f-1 (1),

where U{X, t) is the temperature(K) at time /(s)

and position X (mm), and A,, y, C, ps and 5" are the thermal conductivity of the wire (J/mm s K),

density(g/mm3), specific heat (J/g K), electrical

resistivity(Qmm),and cross-sectional area of

wire(mm2), respectively. The extended wire is divided shown in Fig. 2. The equation (1) is converted into the moving coordinate system

expressed as Z=Z-v/xr , following equation is obtained.

dt~ C y 'dZ' Cy (2),

Wre feed rate

Wre feeder

Circuit inductance

Circuit resistance ^DDDMA^

Power source

Eo- Extension L

Groove width

Figure 1 : Equivalent electric circuit in

UNGW system

where T(Z, t) is temperature(K) at time t (s) and position Z (mm). The temperature-dependence

is considered in X, y and C.

84
A new element is added in front of the contact tip every At(s), and the origin of the moving

coordinate system is set in front of a new element. The relationship between At and AZ is expressed as AZ=VfXAt.

The increment time of heat conduction is defined as ^(s), and the calculation is performed Kn

=At /St times during At until a new element is added. To obtain a stable convergent solution, it is necessary that ^(s) and AZ satisfy the ^

^ following equation (Ref. 11).

A„„ St

To*'^\ Add one mesh every A t <1

(3)

Wre feed rate : \/, The experimental ranges of this study are

as follows :v/= 100 -250 mm/s; At =10-3

-10-4 s-AZ = 0.01-0.25 mm ; St =10-7-

10-8 s (Ref. 9,10).

Arc heat input qa=lx0 (Ref. 12) is provided to the wire tip element, where /

is current (A) and 0 is equivalent

anode melting potential (V). Correlations

between 0 and superheat temperature of

droplets AT (K) expressed as function of / are applied (Ref. 9,10). Assuming that

500K in CO2 welding of 1.2mm wire, 0 is expressed as the following equation .

1

1 To'

1 t;

Contact tip A Z X A t

;Arc

<pqa

Figure 2: One dimensional non-steady heat

conduction model

AT is

0 = 6.8-h 0.0016x7 (4)

Elements which become Tm +AT are immediately removed from the wire tip as a droplet , where

Tm is melting temperature (K).

Arc length in groove

It is assumed that the arc occurs at the minimal geometric distance between the wire tip and

groove wall, which is Wg/2 (mm), or the minimal geometric distance between the wire tip and

the surface of the molten pool just under the wire, which is Lbottom (mm). The arc length La (mm)

is Wg/2 at Wg/2 < Lbottom , and La = Lbottom at Wg/2 > '-'bottom •

Molten metal behavior in groove

The molten pool depression by arc force is discussed. We assumed that the surface of molten

pool just under the wire tip is depressed to the position of hydrostatic potential of molten

metal Hg (mm) which is balanced with arc force F. In this study, arc force is related to hydrostatic potential of molten metal (HPMM), and the following experimental equation is used (Ref. 13). . , w ^ //^ =9.7x10"' X/' -1.3x10"'' X/'

(5)

The molten metal can not response because of the viscosity and surface tension of molten

metal, when the arc force changed by the low frequency pulse current. Therefore, transient

responses of molten metal are discussed. The depression depth Y (mm) is defined as the

distance between the surface of weld metal in the groove and the surface of the molten pool just

under the wire. The temporal changes of Y is expressed by a dashpot with the viscous damping

85
coefficient c, spring force witli the spring constant k and external force F shown in Fig. 3 (Ref.

14). As the viscosity of molten metal suppresses its movement as the resistance, the viscosity

works as same as the dashpot when external force is added. Since the HPMM increases

corresponding to the concavity of molten metal, the force

balancing with the HPMM is assumed to be in proportion

to the concavity (displacement), and replaced by spring

force with the spring constant k. The relation of

displacement Y of (A) shown in Fig. 3 is expressed

following equation.

1

, -I--—

dt

(6)

When the arc occurs between the wire tip and the surface

of the molten pool, we assumed that arc force /acts on

the surface of the molten pool just under the wire tip

independent of wire tip

position, and this arc force is F, and the HPMM is Hg. In equation (6), the time

constant c/k is regarded as

T{s), The arc force is replaced by the HPMM, the

following equation is obtained.

dY

When arc occurs between

the wire tip and the groove

wall, we assumed that

the arc force acting on the

molten pool is regarded as /

=aF, and the HPMM is oHg

(a, constant).The following

equation is obtained.

Figure 3: Equivalent mechanical

system

(a),(b),(g) (c),(d),

(e),{f)

Spa 0 Figure 4: Measured results of motion of molten pool surface

just under wire tip

u dY ^ cxH^ =T—+ Y

' dt

(8)

To estimate t and a, the

behavior of the molten pool

just under the wire in the narrow groove is observed by

a high-speed video camera

(Fig. 4). The time r,p>0(s) of

swelling of the surface of the

molten pool from the groove

bottom is 0.1 7-0. 19s (Fig. 4 (c)

(a) {Spjmax

Figure 5: Determination of r value and a

f Nalue

value

86
(f)), and the maximal swelling of the surface of the

molten pool {Sp}nuix (mm) was 1.7-2.1 mm.

Combinations of a and r are determined so that

{Sp}^ is included in the hatched area in Fig. 5(a).

For example, combinations of r<0.05 at os=O.S,

0.08<T<0.14 at ot=0.6 and 0.28<Tat a=OA are agreed

with measurements. The combination of a and T

determined from Fig. 5(b) in the same manner is T<0.16 at 0.8><x>0.4. To satisfy the measurement of

both {Sp}max and tsp> 0 simultaneously, or is about

0.6, and the r is about 0.1.

SIMULATION SYSTEM FOR UNGW

Procedure of numerical simulation system

Figure 6 shows flowchart of numerical simulation on

the behavior of the melting wire tip and molten pool.

In part A, input of welding conditions, welding speed

v(mm/s),wire diameter <i(mm), wire feed rate Vf (mm/s), distance between contact tip and groove

bottom Ljfmm), groove gap width Wcimm) and

setting of the pulse conditions are performed. Initial conditions, current /o(A), extension Lo(mm) and wire

temperature To (K) are inputted.

In part B, the one-dimensional non-steady heat

conduction FDM analysis of wire extension is performed. AZ, At, St and K„ are set. The 0is

determined from equation (4). The arc heat 1x0 is added at the tip element. The calculations of the

heat conduction are performed Kn times during At. Wire tip elements with temperature higher than Tm +

AT(K) are deleted as droplet. The extension

Lt+At(mm), temperature 7]'""^', melting rate Vni(mm/s)

and voltage drop of extension Vl (V) are determined

at the time t+At. In part C, the molten pool position just under wire tip

is determined. The position of the molten pool

surface just under wire tip is determined from the

hydrostatic potential of molten metal Hg and

depression depth Y. In part D, the arc length and arc voltage Va(V) are determined. The distance between the molten pool

surface just under wire tip and the wire tip and the

( start )

/ Input welding conditions /

Input pulse conditions /

Input initial conditions

I Input mesh size / g /

T

A

Increment time / V:=f i/v.

I Increment time for heat conciiction 5n

Iteration number K^=f it/ St

f Of ^ correlation 5

Delete the mesh B

k<K.

Add one mesh

in front of contact tip

t = t+0 t

I T"*f *

Voltage drop of wire extension l/^

Hydrostatic potential of molten metal balanced by arc force Hg(l

)

Calculation of position of depressed

pool surface V

Hg=yi¥dV7trfay

stance between wire tip and

depressed pool surface L^^^^

D

Figure6: Flowchart of simulation

system for UNGW

87
minimal distance between the wire tip and groove wall are compared, and the shorter one is regarded as the arc length La. The arc voltage is determined from the following properties of CO2

arc(Ref. 15). = 21. 0 + 0.011 •/ + (2.6 + 0.0052 •/)xL^ (9),

where wire diameter is 1.2mm . In part E, current It+At at t+At (s) is determined by following equation.

It + At — It Eq- Ks (10), It - L(;-XAt

where Eo and Ks are the non-load voltage (V) of the constant voltage power source and the slope

of the external characteristics (V/A), respectively. The inductance and resistance of the circuit are 0.3 mH and 0.025 Q, respectively.

Numerical simulation and the experimental

results in UNGW

Simulation results of I-type joints with a groove

width of 5 mm are compared with experimental

results. The position of the wire tip in the groove are examined by a high speed video camera system

(2250 flames/s). The current and voltage at moments corresponding to the images obtained by

this camera system are indicated in Fig. 7 using thin

lines. Numerical simulations are performed under

the same welding conditions. Current, voltage, wire

tip position and pool surface position are indicated

in Fig. 7 using thick lines.

Qualitatively, simulation results of current wave

patterns, voltage wave patterns and the wire tip

position are agreed with the experimental results. The lowest position of the molten pool surface by

the simulation was 2.1 mm, and the penetration

depth at root was 3.0 mm. These simulated values

agreed with the results of experiments. These

results indicate that the wire tip position in the

ultra-narrow groove, current, voltage and the

molten pool position can be simulated.

Figure 7: Comparison between simulation

and experimental results

itp=0.07s,tb=0.2Ss,

Vp=45Y,Vb=39V

v/= 1 7 1 mm/s,v=7.5mm/s,

SEARCH FOR APPROPRIATE PULSE CONDITONS OF UNGW

Oscillation ranges and position of the wire tip are simulated to obtain appropriate welding

conditions. Criterions to melt root area and to form the concave bead surface in the

ultra-narrow gap are determined as follows

(1) To melt the root area, the lower limit Zmin of the wire tip oscillation should be set below the

root surface (Z^,,, < 0).

88

/
(2) To obtain the concave bead surface, the upper Umit Zmax of the wire tip oscillation should be

set at top of the weld metal (throat thickness) Hj (Z„^ =Ht).

The no load voltage Vp during the peak period is searched under the following pulse condition no loaf voltage of during the base period Vb = 37 V, the pulse frequency is 2.8 Hz,

pulse peak duration tp : pulse base duration = 1 : 4. The simulation results of Zmin , Z^axand Ht are shown in Fig. 8. Criterions ,which is Zmin < 0, Zmax —Ht are satisfied in the hatched area. The ultra-narrow gap GMA welding with a groove width of 5 mm is performed at = 44 V

and Vp = 41V. Figure 9 shows the simulated

results of Zmin and Zmax and cross section of

experimental joins. As the penetration shape is thin and long at Vp = 44V, the arc heat is distributed effectively in the upper and lower

groove areas. Zmin locates near the root face and

Zmax locates near the bead surface, these results

indicate that criterions are satisfied by this

appropriate pulse condition. The position of

- 250 ^

of

Peak volage ii no bad volage V (V ) Figure 8: Effect of setting peak voltage Vp on wire melting behavior

Wre Vyre

z=o

the maximal penetration width is center

penetration shape at Vp= 41V. As Zmax

locates lower that in Fig. 9(b), it is considered that wire tip is oscillated

around the groove bottom.

These results obtained in the above

simulation are the example of the

appropriate conditions. There are many

appropriate conditions among

combinations of other pulse conditions.

The GMA welding process numerical

simulation is useful because quantitative

evaluation is possible to examine the

range of appropriate conditions for UNGW,

in which many welding condition

parameters and factors are related. Appropriate welding conditions can be efficiently obtained

according to changes in the pulse frequency, the relative ratio of the peak to base periods and

wire feed rat by this simulation system.

(a) Vp=41V (b) Vp=44V

Figure 9: Experimental results under welding

conditions shown in Figure 8

CONCLUSION

(1)We developed a GMA welding process numerical simulation system considered non-steady

wire melting behavior, the temporal changes of molten pool depression by arc force,

characteristics of the arc current-voltage, equivalent arc heat input at the wire tip and the welding

electric source circuit.

(2) In ultra-narrow gap GMA welding with a groove width of 5 mm, the oscillation behavior of

the wire tip induced by low frequency pulse current was measured, and it was indicated from the

89
measured results that numerical simulation was possible.

(3) It was indicated that welding conditions to obtain melting in the ultra-narrow groove bottom

and formation of the concave bead surface could be searched using this numerical simulation

system, and the usefulness of this system was demonstrated by ultra-narrow gap GMA welding.

This simulation system makes it possible to find out adaptive welding conditions for UNGW

which is characterized by many pulse parameters that correlate in a complex manner.

REFERENCES

1. Shiga,C. 2000. Systematic approach to solution of welding problems in STX21 project:

aiming for remarkable advances in welded joints. Science and Technology of Welding and

Joining 5(6): 356-364.

2. Malin,V.Y. 1987.Monograph on narrow -gap welding technology. WRC Bulletin 323

3. Nakamura,T. and Hiraoka,K. 2001. Ultranarrow GMAW Process with newly developed wire

melting control system. Science and Technologv of Welding and Joining 60(2):355-362.

4. Nakamura,T. and Hiraoka,K. 1999. Development of Ultra-Narrow Gap Welding Process by

Numerical Simulation. Proc. 9th Int. Conf. on Computer technology in welding, Eds.

T.Siewert and C.Pollock:20 1-21 O.American Welding Society.

5. Bingul,Z.;Cook,G.E. and Strauss,A.M. 2001.Dynamic model for electrode melting rate in

gas metal arc welding process. Science and Technology of Welding and Joining 6(1): 41-50.

6. Yamamoto,T.;Ohji,T.;Miyasaka,Y. and Tsuji,Y. 2002.Mathematical modeling of metal active

gas arc welding. Science and Technologv of Welding and Joining 7(4): 260-264.

7. Xiaolin,Y.;Ping,S.;Shengsun,H. and Zhen,L. 2002.A numerical model of wire melting rate in

CO2 gas-shielded welding. Materials and Design 23(8): 501-504.

8. Trasov,N,M.;Gorlov,A,K. and Lashko,S.N. 2002.Numerical modeling of the process of

formation of a molten metal drop at the tip of a consumable electrode. The Paton Welding

Journal. 6: 21-24.

9. Nakamura,T. and Hiraoka,K. 2002. Wire Melting Behavior by Non-Steady Heat Conduction

Numerical Analysis in Gas Metal Arc Welding. Ouarterlv Journal of the Japan Welding

Societv 20,(1): 53-62 (in Japanease).

10. Nakamura,T. and Hiraoka,K. 2001. Wire Melting Behavior by Unsteady Heat Conduction

Numerical Analysis in GMAWelding. HW Doc.2 12- 1004-01

11. Katayama,K.; Hattori,K.; Okada,M.; and Kotake,I. 1972. Numerical analyses of unsteady

thermal conduction for depending on temperature of material properties. Tran. JSME.

38(307): 574-580. (in Japanese)

12. Lancaster,J. E. ,Ed. 1984.The Physics of Welding: Oxford: Pergamon Press.

13. Nakamura,T. and Hiraoka,K. 2001.Charactersistcs of Bead Formation Phenomena in

Narrower Gap Welding and Proposed of New Welding Process at Ultra-Narrow Gap Joint,

Ouarterlv Journal of the Japan Welding Societv 19,(1): 44-53 (in Japanease).

14. Maruo,H.;Hirata,Y;Kusano,T and Okano,I. 1985. Measurement of weld Pool Oscillation in

Pulse TIG Welding, Ouarterlv Journal of the Japan Welding Societv 3,(1): 457-464 (in

Japanease).

15. Nakamura,T. and Hiraoka,K. 2002. GMA Welding Process with Periodically Controlling

Shielding Gas Composition, Ouarterlv Journal of the Japan Welding Societv 20,(1): 237-245

(in Japanease).

90
DIMENSIONAL DISTORTION OF T-JOINTS USING ALUMINUM 6063-T52

EXTRUSION MATERIAL IN MIG WELDING

R. Koganti, Zaluzec. M, Velez. J, Karas. C, Joaquin. A, and A. Caliskan

ABSTRACT

The development of manufacturing processes for joining and assembling of lightweight

aluminum vehicles requires detailed process capability studies as well as dimensional

variation analysis studies to ensure process controls are in place. These manufacturing

processes not only have to provide cycle time viability but also need to maintain or surpass

product safety and quality. T-Nodes joint designs are an integral of aluminum architectures

based on hybrid designs, i.e those fabricated from mixed aluminum products consisting of

castings, stampings and extrusions. The purpose of this study was to find optimum

parameters for minimum distortion for the welding of 6063-T52 T-Nodes. The welding

factors considered were locators (4-way and 2-way pins verses net surfaces), the welding

equiment process factors (power input, pulse frequency, gas flow rate, torch angle and arc

intensity), the use of simultaneous welding, and welding sequence order. A partial factorial

design of experiment (DOE) was conducted to understand the effects of these factors on T- node joint distortions. A total of 14 points were considered for dimensional distortion

measurements. Results showed power (heat) input is the only statiscally significant factor on

joint distortion. Locators type as well as welding sequence and simultaneous welding also had

a measurable affect on part deviation during welding.

KEYWORDS

Metal Inert Gas Welding, Aluminum, Extrusion, Power Input, Pulse Frequency, Torch Angle,

Gas Flow, Penetration, T-Nodes, Distortions.

INTRODUCTION

Ford Motor Company is investigating various lightweight materials for high mileage

performance and also safety of occupants during collision. Aluminum is one of the chosen

materials for the structural development work for high mileage as well as crashworthiness.

Aluminum structures have many advantages in automotive applications due to its lightweight

and energy management performance in crash applications. Various automotive

manufacturers are currently using aluminum in structural applications. The Plymouth

Prowler, Audi A8, Audi A2, Ferrari's 360 Modena, Panoz's Esperante, Ford's - Th!nk

Neighbor, and Honda's NSX and Insight, are just a few of aluminum intensive vehicles

currently in production.

Mfg. And Vehicle Design Research and Advanced Engineering Laboratory, Ford Research

and Advanced Engineering, Ford Motor Company, Dearborn, Michigan, 48124.

Although unitized steel body architectures represent the most common body structure,

aluminum architectures can come in a variety of designs including unitized body, space frame

and hybrid body architectures. Stamped and extruded (straight and hydroformed) components

91
are finding more applications in unitized body architectures as automotive engineers find cost saving associated with applying these types of aluminum products. Hybrid architectures

consist of a combination of aluminum products including stampings, extrusions (straight and

hydroformed) and castings. Joining and assembling these components is often challenging

task since most of the automotive and joining infrastructure is based on spot welded steel assemblies. In the case of aluminum, a multitude of joining methods can be applied to join

extrusions, stampings, and castings. Commonly used techniques include riveting. Metal Inert Gas (MIG) welding, laser welding and adhesive bonding. In addition, hybrid laser/MIG

welding, magnetic pulse welding, and friction stir welding are future enabling technologies in aluminum joining processes for hybrid architectures. The Ford's Scientific Research

Laboratory (SRL) is currently investigating the joining and assembly of hybrid aluminum

body architectures using MIG welding as the primary joining process. One of the chosen

architecture for development purposes consists of stampings, castings and straight and bent

extrusions as shown in Figure 1 and the joint configurations are shown in Figure 2. One of the

main attributes in any body architecture is dimensional management of Body-In-White (BIW)

assemblies. Prior to weld the full structure, it was necessary to understand the weld process

effects on a lab scale level and ultimately optimum process factors can be selected for

minimum structural distortions. This study is mainly focused on a T-Node joint (shown in Figure 3) for economical purposes and ultimately the information was used for the final front

end assembly.

Cast Shock Towet ^^^^^

Ext uded

Ra Jiatof -

Support Rail

Longiudinal

Figure 1 : Hybrid aluminum front end assembly

92
Figure 3: T-Node configuration

93
WELDING DEVELOPMENT WORK

In order to understand the weld parameters influence on joint properties, initially several

experiments were conducted to optimize the MIG welding parameters on single lap joint

properties. In MIG welding besides the joint strength, the other important attribute is distortion of the aluminum structures due to the welding.

Initial screening experiments were conducted on lap coupons to identify the welding

equipment process factor ranges with an acceptable weld quality (weld strength and weld

penetration). The welding equipment used for the coupons as well as T-Nodes experiments is an OTC/Daihen CPD-350 welding system and DR-4000 pulse power supply (Figure 4).

Based on screening experiments, the ranges for voltage, current, torch speed, gas flow rate, pulse frequency were selected. Since, torch speed, voltage, current and wire feed rate are inter-dependent, all these factors are lumped as one factor i.e., power input (also called as heat input). Moreover, with the Daihen equipment, wire feed rate can't be changed due to the

fact that voltage and current set the wire feed rate automatically. Initial studies were focused on optimization of welding parameters for various alloy combinations (5754, 6063 and A356)

for joint strength. From the single lap coupon DOE study, it was concluded that power input

and gas flow rate are statistically significant on lap shear load.

This study was developed to identify the influence of locators strategy, equipment parameters,

number of welding robots, and welding sequence on joints dimensional variation in the

welding of aluminum 6063-T52 T-Nodes (Figure 3).

Figure 4: OTC/Diahen welding equipment

94
EXPERIMENT

The objective of the experiment was to understand the welding parameters influence on TNode dimensional distortions. The welding equipment process factors selected for this DOE

were power input (torch speed, voltage current, and wire feed as one factor), pulse frequency,

gas flow rate, torch angle, and arc intensity. The extreme levels of power input, pulse

frequency, gas flow, torch angle, and arc intensity are shown in Table 1. Argon gas

(@99.9%), commonly used for aluminum MIG welding [1], was used as the shielding gas. The filler wire selected was a 1 .2 mm diameter Al-4047 filler wire. Other factors maintained

constant throughout the experiment were lead push angle (20°), weld length (75mm), and

wire stick out (15mm).

These experiments were conducted for the following strategies for the minimum distortions

of the T-Nodes:

i) MIG welding factors on dimensional distortions due to locator strategy

a. Both vertical and horizontal extrusions are constrained on net pads

b. Both vertical and horizontal extrusions are constrained using 4 way and 2 way

locator pins

ii) Synchronous welding (1 robot verses 2 robots)

iii) Weld sequences (normal versus reverse order) using one robot

A partial factorial DOE was conducted consisting of eight experimental runs as shown in the

experimental matrix in Table 2. A total of eighty T-Nodes (10 samples of each experimental

run) were welded and fourteen point locations (Figure 5 - Points 2-15) measured on each one

three times: (1) after- welding clamped, (2) after-welding undamped, and finally (3) at freestanding. The last forty of these eighty T-Nodes were also measured before welding to understand the variability of components as well as the fixture. Forty T-Nodes were

positioned for welding with pins and the other forty with surface pads. Five additional TNodes were welded at low input settings for simultaneous welding analysis and another five

at high input settings for welding sequence (normal versus reverse) analysis.

Table 1 . DOE-T2 process factors matrix

Factor Low Level High Level Unit

Torch Speed 635 1270 mm/min

Corresponding Voltage/Current 21.5/150 25.3/220 Volts/Amperes

Pulse Frequency 10 30 Hz

Gas flow rate 0.71 1.27 CubicMt/hr

Torch Angle 45 50 Degrees

Arc Intensity Normal Soft N/A

95
Table 2. Experimental matrix

Kun ff Power Input Pulse

ncc|ueiicy

Gas Flow

r\.aie

Torch Angle Arc Intensity

11

7 7 7 7 7 9 / 7 7 7 7

J 7 7 7 7 4 J 7 7 7 .7 5 -1 1 -7 7 -1 6 -1 1 7 -7 1 7 1 -1 -7 7 1 8 1 -1 7 -7 -1 Note: -1 and 1 are low and high levels of the factor settings as referred in tab e 1.

DATA RESULTS

A total of ninety T-Nodes (for all the strategies mentioned above) were welded for

dimensional variation analysis. The first forty samples were measured at three different

conditions (after-welding clamped, after-welding undamped and at freestanding) at fourteen

selected measurement point locations. Thereafter, the remaining fifty included one additional

measurement before welding for parts and fixture induced variation accountability.

A comparison of "before weld" measurements and their respective nominal showed that the

deviation induced by the combination of the parts dimensional variation and the fixture

variation was not significant (Figure 6). Additionally, there was no significant difference

between the clamping condition, undamped condition, and freestanding condition

measurements (Figure 7). Therefore freestanding was compared to nominal for dimensional

variation study purposes.

96
T-Ncxie Devtatfons Befor« to Nominal

T-Node PoaHlon

i Before to Nominal

Figure 6. Before weld to nominal variation (mm)

0.8

0.6

0.4

-0.4

-0.6

-0.8

T-Node Deviations - Clamping Effect on T-Node Position

1

-t' -'

'

i

j

! i 1

\\

\ 1

I Pinned (Free Standing

to Nominal)

I Surface Pads (Free Standing to Nominal)

Surface Pads (Free Standing to Before Weld)

T-^4ode Position

Figure 7. After weld clamped, undamped and freestanding averages and standard

deviations of measurements (mm)

97
Power input was the only input value that showed significance on distortion. Typical

assumptions on welding power input and its effect on distortion is that the more heat input

during the welding process, the more distortion will occur thus more measured deviation.

Although high and low settings of power input referred to 5.57kW and 3.23kW respectively, the

high setting of power input induced less heat distortion than the low setting because of its higher

travel speed (Figure 8). In this study the interrelation between the torch speed and power at high

and low settings are 50 ipm and 5.57 kw (at high setting), 25 ipm and 3.23 kW (at low setting).

The influence of the other process factors on the dimensional variation of the T-Nodes during

welding remained less of a significant factor.

Influence of Locator Strategy on Dimensional Deviations

Power input shows a significant effect on when the parts are constrained by 4-way and 2-wau

locator pins. High power input exhibited lower distortion and vice versa. The reason for

power input contributes distortion is the horizontal and vertical members are free to slide

(against the friction between the net pads and components) due to coefficient of thermal

expansion. Higher power input (higher torch speed) resulted in lower distortion (Figure 8); this is favorable for reducing the cycle times in high production volumes.

Experiment results indicated that dimensional deviation was higher when extruded parts are located by 4-way and 2-way pins (movement restraints) compared to surface pads (Figures 9

and 10). In addition, the standard deviation of the measurements was significantly higher for

those T-Nodes positioned by pin locators (Figure 10). The fact that the heat of fusion welding

caused aluminum expansion within the aluminum extrusion and shrinkage around the weld

joint, followed by a slow cool accounts for the variations in measurement variation when the

part is restricted by pin locators.

Power input Pulse frequency Gas flow rate Torcfi Angle Arc Intensity

~i 1——I

1——I

1——I1——I r

Figure 8. Welding equipment input values effect on T-Node deviations (mm) for surface pad locators

98
Figure 9. Welding equipment input values effect on T-Node deviations (mm) for pin locators (4-way and 2-way locators)

T-Node Deviations - Clamping Bfect on T-Node Position

T-Node Position

B Pinned (Free Standing

to Nominal)

I Surface Pads (Free Standing to Nominal)

Figure 10. Pinned surface vs. surface pads clamping averages and

standard deviations of measurements (mm)

99
Single Versus Simultaneous Welding Effects on Dimensional Deviations

Single and simultaneous (dual torch) welding averages and standard deviations of the

measurements were comparable for most of their sample points; however, one of the

measurement points (point #7) showed a very significant difference between single and

simultaneous welding. The dimensional deviations in single vs. simultaneous welding are shown in figure 1 1

.

Single Versus Simultaneous Walding - Sequence 1

1 li 1 iT^"' '"j'l" 1 M i"f^ iTi'^" ^^^^

1

r

! * E

L _

' ^.

Singb

Sirujltaneoifi

T-Node Poiillon

Figure 11. Single vs. simultaneous welding averages and standard

deviations of measurements (mm)

Welding Order Effects on Dimensional Deviations

Welding order was reversed (using only one robot) in order to study the effects of changing

the welding direction and sequence order to look for significant effects. On the other hand,

the direction of the deviation kept unchanged and the standard deviations of the

measurements remained similar (Figure 12).

100
Normal Versus Reverse Order - Sequence 4

B Normal Order Reverse Order

-1.000 ^ !

T-Node Position

Figure 12. Normal vs. reversed order averages and standard deviations of measurements (mm)

SUMMARY AND CONCLUSIONS

• The purpose of this study was to identify the influence of clamping devices,

equipment parameters, number of welding robots, and sequence factors on 6063-T52

T-Node joint distortions.

• Surface pads resulted to provide a significantly lower part deviation and lower

standard deviation than pin locators. • Besides decreasing cycle time, high power input settings for surface pads locator

strategy contributed to a lower part deviation than low power input settings. • Welding order influenced magnitude of deviation, yet the direction of the deviation

remained unchanged for the two weld orders studied. • Dimensional distortion was more likely to occur in the direction opposite to part

restraints (pins or surface pads).

RECOMMENDATIONS

Further smdy should concentrate on clamp pressure and clamping area effects on T-Node

deviation, different combinations of simultaneous sequences and several additional welding

order combinations (24 possible combinations).

101
REFERENCES

1. Juvinall, Robert; Marshek, Kurt. Fundamentals of machine component design. USA:

John Wiley & Sons, Inc., 1991, Second Edition

2. Douglas C. Montgomery. Design and analysis of experiments. USA: John Wiely &

Sons, Inc.

3. Ashby, Michael; Jones, David. Engineering Materials 2 - An introduction to microstructures, processing and design, U.K.: Pergamon Press, 1986, First Edition.

102
Fractional Factorial Technique to Predict Welding Current

in Gas Metal Arc Welding

Masood Aghakhani

Abstract

During research on Gas Metal Arc Welding (GMAW) of aluminum, it was observed that

the welding current was affected by any change in wire feed rate, arc voltage, nozzle-to-plate

distance, torch angle, and welding speed. To determine accurately the welding current for any set of these parameters, a mathematical model has been presented. The model was developed using a two-level fractional factorial design and its adequacy was tested by the analysis of variance

technique. The estimated and observed values of the welding current have been shown on a

scatter diagram and the interaction effects of different parameters involved have been presented

in graphical forms. These results can be utilized for determining accurately the heat input into the

workpiece from which reliable predictions can be made about the mechanical and the

metallurgical properties of welded joints.

Keywords: Fractional Factorial Technique; Welding Current; Gas Metal Arc Welding; and

Aluminum.

Introduction

Gas Metal Arc (GMA) also known as Metal Inert Gas (MIG) is the most often used

welding process for welding aluminum and it's alloys (Ref. 1). In MIG welding, the wire

electrode used is thin (0.5-1.6 mm diameter) and consequently the wire feed speed is high. To

achieve self-adjustment of arc at high-speed rates, it is essential to use power sources with flat or nearly flat V-I (volt-ampere) characteristics. For welding with such a power source, the wire feed

rate has been given by Halmoy (Ref. 2) by the following equation:

W = cd + Ma Where

W = wire feed rate

I = welding current

1 = electrode stick-out a = area of cross section of the wire

a = constant of proportionality for anode or cathode heating

P = constant of proportionality for electrical resistance heating

Masood Aghakhani, Assistant Professor in Production Engineering (Welding Technology),

College of Engineering, University of Razi, Kermanshah-67149, Iran. E-Mail: aghakhani @razi.ac.ir & aghakhanim@vahoo.com

103
It is reported that for aluminum a = 0.75 mm/A-sec and (3 is negligible (Ref. 3), thus

reducing Halmoy's equation for GMAW of aluminum to: W = al

or, in other words, the welding current (I) is directly proportional to the wire feed rate (W).

However, during prolonged investigations on GMAW of aluminum, it was observed that the

welding current (I) was not only affected by the changes made in wire feed rate (W), but arc voltage (V), nozzle-to-plate distance (N), torch angle (0), and welding speed (S) also affected the

welding current accordingly. Welding current affects the metal transfer (pinch effect a / ^ ) and

the amount of heat input into the weld pool and consequently it affects the bead shape as well as the metallurgical and mechanical properties. To avoid any serious effects on the analysis of

results of erroneous assumption that the welding current is only proportional directly to wire feed

rate, it was found imperative to determine accurately the welding current as affected by different

welding parameters.

This paper reports the development of a mathematical model, based on practical

observations, made during gas metal arc welding of aluminum to estimate accurately the welding

current as affected by welding parameters.

Plan of Investigation

To achieve the desired aim, the planning for investigations were carried out in the

following steps:

1. Selection of a mathematical model;

2. Design of experiments;

3. Selection of useful limits of the welding parameters viz.; wire feed rate (W), arc voltage (V), nozzle-to-plate distance (N), torch angle (0) and welding speed (S);

4. Developing a design matrix;

5. Conducting experiments as per designed matrix;

6. Estimation of the coefficients of the equation;

8. Checking the adequacy of the model;

9. Testing the significance of the regression coefficients and arriving at the final form of

the mathematical model;

10. Presenting the estimated and observed data on a scatter diagram;

11. Presenting the significant interactions between different parameters in graphical form;

12. Analysis of results; and

13. Conclusions.

Selection of the Mathematical Model

It was observed from the preliminary experiments that the welding current (I) was

affected by wire feed rate, arc voltage, nozzle-to-plate distance, torch angle, and welding speed

within the limits employed for the study. Under these conditions, the response function could be

expressed as

I = f{W,V,N,0,S)

104

/
Assuming a linear relationship and taking into account all possible two-factor interactions, it could be written as:

I = b^+b,W+ b^V +b,N + b^O + b,S+ b^WV + b,WN + b^W0 + b,WS + b.^VN

+ b,yd + b.^vs + b,,NO + b.^NS + b,,es

Where / is the estimated welding current and Z?, ^b^ , ,^,5 are the coefficients of the polynomial

to be determined.

Design of Experiments

The design of experiment, that is, the decision to use different values of different

parameters for conducting a particular experiment was based on the fractional factorial design,

which is a standard statistical tool to investigate the effects of a number of parameters on the

required response. In addition, interactions between two or more parameters can be evaluated,

which is not possible with the conventional experimental approach since in that approach all parameters other than being studied are held constant. For calculating the main and interaction effects of five variables W, V, N, 0, S at two

levels, a half fractional factorial design was selected. This design gave

2'^"' = 16 weld runs to fit an equation and was sufficient for the evaluation of coefficients.

Selection of Useful Limits of Welding Parameters

On the basis of preliminary experiments conducted using 1.6 mm diameter wire with

argon shielding, it was found that for proper bead configurations the wire feed rate, arc voltage,

nozzle-to-plate distance, torch angle, and welding speed were required to be kept between 6.1

m/min and 7.6 m/min, 24 V to 29 V, 15 mm to 20 mm, 80 deg to 100 deg and 25 cm/min to 40

cm/min respectively. These limits were adhered to throughout the investigations reported here.

For the ease of recording and processing of the observed data, upper and lower levels of

welding parameters were coded as + 1 and -1, respectively by using the following relationship:

^ , , Natural Value - Average Value

Coded Value =

Variation Interval

Two levels of welding parameters, their levels, units, and coded values are given in Table 1

.

105
Table 1-Welding parameters and their limits.

Parameter Unit Symbol Coded Value Coded Value

Low (-1) High (+1)

Wire Feed Rate m/mm ITw7 6.1 7.6

Arc Voltage volts V 24.0 29.0

Nozzle-to-Plate mm N 15.0 20.0

Distance

Torch Angle deg 0 80.0 100.0

Welding Speed cm/min S 25.0 40.0

Design Matrix

The design matrix evolved to conduct sixteen experiments is given in Table 2 and is based on the method suggested elsewhere (Refs. 4, 5).

Table 2- 2^"'Design Matrix

s.# W V N e s (-WVN0)

1 + + + +

2 + + + +

3 + + +

4 + +

' ":.5 + + +

6 + +

7 + +

8 + +

9 + + + +

10 + +

11 + +

12 + +

13 + +

14 + +

15 + +

16

Experimentation

Employing a mechanized welding system, experiments were conducted as per the

conditions set in the design matrix of Table 2. Welding current observations were made by

making weld runs of 25 cm length on 13 mm thick plates of aluminum. The power source used was a three-phase transformer cum full-wave rectifier unit having a flat V-I (volt-ampere)

characteristics with a current capacity of 425 A at 60% duty cycle and an infinitely variable OCV

(open circuit voltage) of 12-48 V in two ranges. A 1.6 mm diameter NG-6 filler wire (Al-5%Mg)

with argon as shielding gas at a flow rate of 30 1/mm was used.

/

106
Sixteen weld runs were made using the conditions set in the design matrix but to avoid

any systematic error in experimentation, the sets of conditions were chosen at random. The

recorded values of welding current (/) for two sets of experiments are given in Table 3.

Table 3-Recorded values of welding current

s.# Trial

No.

W V N e s Welding Current

Ii h

1 1 + + + + - 260 270

2 2 - + + + + 240 240

3 3 + + + + 260 260

4 4 - + + - 220 220

5 9 + + + - + 280 290

6 10 - + + - - 245 245

7 11 + + 270 270

8 12 + + 240 240

9 13 + + 290 290

10 14 + + 240 240

11 15 + + 280 280

12 16 240 240

13 5 + + + + 290 290

14 6 + + 240 250

15 7 + + 260 260

16 8 + + 245 245

Evaluation of Coefficients of the Model

To determine the regression coefficients of the selected model, following formula (Ref. 4,

5) based on the method of least squares was used:

N

bj=^^^— ,j = 0,l,2,...,k

N

where:

Xji = value of a factor or interaction in coded form.

Yi = average value of the response parameter, that is, the welding current in this case N = number of the observations.

k = number of the coefficients of the model.

A matrix designed to apply this formula to calculate different coefficients is given in Table 4.

107
Table 4-Design matrix to calculate different coefficients

s.# Trial

No.

bo bi b2 b3 b4 b5 b6 bv bg b9 bio bn bi2 bi3 bi4 bi5 Yavg

1 1 + + + + + - + + + - + + - + - - 265

2 2 + - + + + + - - - - + + + + + + 240

3 3 + + - + + + - + + + - - - + + + 260

4 4 + - - + + - + - - + - - + + - - 220

5 9 + + + + - + + + - + + - + - + - 285

6 10 + - + + - - - - + + + - - - - + 245

7 11 + + - + - - - + - - - + + - - - 270

8 12 + - - + - + + - + - - + - - + - 240

9 13 + + + - - - + - - - - - - + + + 290

10 14 + - + - - + - + + - - - + + - - 240

11 15 + + + + + + + 280

12 16 + + + + + + + + + + + 240

13 5 + + + + + + + + + + + 290

14 6 + + + + + + + 245

15 7 + + + + + + + 260

16 8 + + + + + + + 245

The estimated coefficients of the variables and their interactions computed using the above

relationship are given in Table 5.

Table 5-Coefficients of the model

bo bi b2 b3 b4 b5 b6 by bg bg

w V N 9 s wv WN we WS

257.2 17.8 5.3 -4.1 -4.1 2.8 2.2 -0.9 -2.2 0.9

blO bll bl2 bl3 bl4 bl5

VN V0 VS NO NS es

0.3 1.6 -1.6 -2.8 0.3 2.8

Using these coefficients, the complete model, in coded form, can be expressed as follows:

I = 257.2 + 17.8W-h5.3V-4.1N-4.ie + 2.8S-H2.2WV-0.9WN-2.2We

+0.9WS + 0.3VN + 1.6V0 - 1.6VS - 2.8Ne + 0.3NS + 2.898

Checking the Adequacy of the Model

The adequacy of the model was determined by the analysis of variance technique. The

regression coefficients were determined by the method of least squares, from which the F-ratio

)

108
for the polynomial was found. The F-ratio of the model was compared with the corresponding Fratio from the standard tables and it was found that the model was adequate within 95% level of

confidence, thus justifying the use of assumed polynomial. Details of analysis of variance are given in Table 6.

Table 6-Details of analysis of variance

Response Degrees Variance Standard Variance T' T' Adequacy

of of Deviation of Ratio Ratio of

Freedom Response of Adequacy Model Table Model

for Coefficients

Sy Sa Sy Sa p =si at Whether

Sbj = J— "

Si (10,16,0.05) Fn,<F,

V df

Welding 16 10 9.375 0.765 7.50 0.80 2.49 Yes

Current

Checking the Signiflcance of the Coefficients of the Model

To eliminate the statistical insignificant terms of the model, if any, it is essential to check

the significance of each of the coefficient and to do that Student's 't'-test was employed. It was

found that the coefficients of all the main effects and WV, W0, N9, and 9S interactions were

significant. The rest of the coefficients could be dropped conveniently. Thus the final model with

only the statistically significant coefficients in the coded form is given as:

/ = 257.2 + 11. SW + 5.3V - 4.0N - 4.00 + 2.85 + 2.2W - 2.2W0 - l.Wd + l.m

Results

A large number of observations were taken under different sets of parameters within the

limits investigated and the corresponding estimated values of welding current were determined

from the model. The observed and the estimated values were then represented in a graphical form

as a scatter diagram shown in Fig. 1

.

The significant interactions viz. WV, W0, N9 and 9S as deduced from the model are shown in Figs. 2-5.

Analysis of Results

Fig. 1 shows that the estimated and the observed values of the welding current are scattered close to 45° lines, passing though the origin, indicating an almost perfect fit of the

developed empirical model. Furthermore, the analysis of results show that the increase in the

levels of W, V, and S resulted in the corresponding increase in welding current whereas, the

increase in the levels of N, and 9 proved to be otherwise.

109
Effect of Wire Feed Rate (W)

Wire feed rate is the most important parameter affecting the welding current. An increase

in welding current from 6.1 to 7.6 m/min resulted in the increase in welding current by a magnitude of 34.36 amperes. This increase in welding current with the increase in wire feed rate

is to keep the wire feed rate and burn off rate in equilibrium.

Effect of Arc Voltage (V)

The second largest increase in welding current is caused by arc voltage by a magnitude of

10.6 amperes when it is varied from 24 to 29 volts. The increase in welding current with the

increase in arc voltage indicates the rising characteristics of the welding arc.

Effect of Nozzle-to-Plate Distance (N)

The welding current decreased by approximately eight amperes with the increase in

nozzle-to-plate distance from 15 to 20 mm. This is due to the fact that as the value of 'N' is increased, the arc length also increases momentarily thereby increasing the arc voltage but the

same is restored to its original value due to the self-adjustment of the welding arc. This increases

the stick-out and hence the amount of I^R heating. Increase in the preheating of the stick-out

reduces the welding current slightly so that the equilibrium between wire feed rate and the burn

off rate is maintained.

Effect of Electrode/Torch Angle (0)

The increase in the torch angle from 80° to 100° resulted in the decrease of welding

current by approximately 8 amperes. The reason for this could be explained due to the fact that as the electrode angle increases the effective arc length increases, and hence a decrease in welding

current is observed. ,

•

Effect of Welding Speed (S)

The increase in welding speed from 25 to 40 cm/min resulted in the increase in welding

current by approximately 6 amperes. This could be due to the fact that at low welding speed the

arc is very close to the deposited metal and since the melting point of the filler wire is quite low,

the current required to maintain the burn off rate equilibrium, is less and when the welding speed

is increased, the heat content of the arc is reduced and hence to maintain the burn off rate equilibrium, the welding current requirement increases.

Effect of Interactions

Fig. 2 shows that welding current increased with the increase in wire feed rate. The

increase in welding current was 6 and 12 amperes for low and high values of wire feed rate when, arc voltage was varied from 24 to 29 volts. This shows that the interaction of W&V is highly significant at higher levels.

Fig. 3 shows that welding current increased with the change in the torch angle from 80° to 100°, backhand to forehand. The increase in the welding current was just 3 amperes compared to 12 amperes for low and high values of wire feed rate. Fig. 4 shows that welding current decreased with the change in torch angle from

backhand to forehand position. Effect of torch angle on welding current at low levels of wire feed

110

- , ' I
rate is quite insignificant compared to the variation of 14 amperes at the high level of nozzle-toplate distance.

Fig. 5 shows that welding current decreased with the increase in torch angle from 80° to 100°. There is no effect of welding speed on the welding current at low level of torch angle

however; it became quite significant at the high level of torch angle. The figure also shows that

there is hardly any effect of torch angle of 80° on welding current. The welding current decreased

by 12 amperes at 40 cm/min & forehand angle of 100°.

Conclusions

1. In GMAW, the welding current is affected not only by the wire feed rate but also by

the arc voltage, the nozzle-to-plate distance, the electrode/torch angle and the

welding speed.

2. An. empirical model was developed to estimate the welding current by correlating

these parameters for GMAW of aluminum.

3. At low wire feed rates, the effect of arc voltage on welding current was negligible.

However, it increased enormously at higher feed rates.

4. Increase in nozzle-to-plate distance resulted in the decrease in welding current

possibly due to the increase in arc length.

5. The increase in arc voltage resulted in the increase in welding current indicating a

rising volt-ampere characteristic of the welding arc.

6. The increase in electrode/torch angle resulted in the decrease in welding current.

7. The increase in welding speed resulted in the increase in welding current.

Acknowledgement

The author wishes to thank the research vice-chancellor of the University of Razi, for

granting permission to publish this paper.

References

1. Walser, H.; and Zehnder, M. 1981. Practical and technological aspects of inert gas

welding of aluminum and its alloys. Colloquium on Aluminum and Its Alloys . Porto (Portugal): nW, Paper 1-8.

2. Halmoy, E. 1979. Wire melting rate, droplet temperature and effective anode melting

potential. Proc. of International Conference on Arc Physics and Weld Pool BehaviorPaper 44. Welding Institute, London, pp. 241-249.

3. Trindade, H. 1982. M.Sc. thesis . School of Industrial Science, Cranfield, Cranfield

Institute of Technology.

4. Adler, Yu.P.; Markova, E.V.; and Granovsky, Yu.P. 1975. The design of experiments

to find optimal conditions, Moscow: Mir Publishers.

5. Box, G. E. P.; Hunter, W.G.; and Hunter J. S. 1978. Statistics for experimenters: An

introduction to design data analvsis and model building. New York: John Wiley &

Sons.

6. Montgomery, D.C. 1991. Design and analvsis of experiments. Third Edition, New

York: John Wiley & Sons.

Ill
/
Weld Data Flow

113

CONTROL OF GMA BUTT JOINT WELDING BASED ON NEURAL NETWORKS

K. H. Christensen*, T. S0rensen*

ABSTRACT

This paper presents results from an experimentally based research on Gas Metal Arc Welding

(GMAW), controlled by the artificial neural network (ANN) technology. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a high degree of quality in the challenging field of butt joint welding with full penetration under

stochastically changing boundary conditions, e.g. major gap width variations. GMAW experiments performed on mild-steel plates (3 mm of thickness), show that high quality welds with

uniform back-bead geometry are achievable for gap width variations from 0.5 mm to 2.3 mm - scanned 10 mm in front of the electrode location. In this research, the mapping from joint geometry and reference weld quality to significant welding parameters, has been based on a static multi-layer feed-forward network. The Levenberg-Marquardt algorithm, for non-linear least

square error minimization, has been used with the back-propagation algorithm for training the

network, while a Bayesian regularization technique has been successfully applied for minimizing

the risk of inexpedient over-training.

KEYWORDS

GMA welding, butt joint welding with full penetration, open-loop weld geometry control, neural

network modeling, Marquardt algorithm, and Bayesian regularization.

INTRODUCTION

Gas Metal Arc Welding (GMAW) is - due to its considerable flexibility, productivity and weld

quality - the most prevalent arc welding process in industry; but unfortunately also one of the

most complex processes. Basically, the GMAW process involves conductive, convective, radiative heat and metal transfer, phase transformations among many unknown disturbances. Accordingly, the GMAW process is a time varying, non-linear, coupled, multi-variable process whose

highly complex physics, is not fully comprehended neither qualitatively nor quantitatively. Selecting appropriate welding parameters to get a given weld quality (i.e. weld geometry), is clearly a non-trivial task. Furthermore, even minor changes in system dependent parameters, e.g. joint geometry, heat conduction, etc., from given reference values necessitate a continuous adjustment of significant welding parameters, such as welding speed, current, voltage etc. to facili- tate a sufficient quality and shape of a desired weld. Therefore, to handle such situations in a rea- sonable way it is appropriate to obtain a mathematical model that correlate welding process pa-

* Department of Mechanical Engineering, Technical University of Denmark, DK-2800 Kgs.

Lyngby, Denmark

115
rameters to weld quality - or vice versa; but obviously, it seems to be a challenging issue in the

field of arc welding automation.

During the past decades, intense research into the area of mathematical modeling of the complex

arc welding processes has been made to increase the understanding of the effects of the arc welding process parameters on the weld quality. This extended work includes theoretical studies

based on heat transfer and fluid flow theory etc. as well as empirical methods based on knowledge collected from specific weld task experiments. However, the many years of work has

proven that the development of exact and reliable models based on the fundamental physical

laws are extremely complicated if not impossible, and even the most promising models are typically unsuitable for real-time control purposes. Even though empirical models inherently have

many drawbacks, such as sensitivity on geometry and material properties, and much more, they

tend to have some advantages in the area of arc welding automation. Classic regression analysis

was applied to welding geometry research in the late half of the 80'ties (Ref. 1), while the first step towards the application of artificial neural network (ANN) was reported a few years later (Ref. 2). In this, ANN was used to determine the relationship between significant welding parameters and characteristic weld bead dimensions for gas tungsten arc welding.

Since the publication of (Ref. 2), a diversity of papers, typically addressing optimization and

control, have been published underlining the quality of ANN, as a generic tool with a high degree

of flexibility for modeling and control of a great variety of arc welding processes on different

kind of joint geometries, materials, etc. (Ref. 3-8). Some of the publications, e.g. (Ref. 7-8) show

that artificial neural networks are indisputable alternatives to classic regression analysis.

Still, research on modeling and control of real-life welding based on ANN techniques are limited

and more contributions are needed. Therefore, in this paper research on the ANN based modeling

and control of the GMAW process, is presented. A model based control system has been developed, that continuously compensate for gap width variations in butt joint welding with full penetration by selection and maintenance of the equipment parameters necessary to achieve a user

specified reference back-bead geometry, which is naturally a considerable factor to the final weld

quality.

After a short introduction of ANN, the paper proceeds with a description of the general considerations concerning the welding experiments that have been carried out. Next, the results from

these experiments are presented and discussed together with the applied control strategy, and fi- nally a conclusion on the work is drawn.

THE MODELING TECHNIQUE: ARTIFICIAL NEURAL NETWORK (ANN)

Due to their theoretical ability to approximate arbitrary non-linear mappings, ANN's are typically

applied when appropriate analytical models are unknown or extremely complex. In mathematical

terms, an ANN can be characterized as a highly complex, non-linear mapping function, which in a given domain transforms input to output. Although an ANN model may be very complex, the

basic mathematics is relatively simple. Modeling with ANN is empirical modeling, and thus a sample database covering the entire problem domain must be available, similar to conventional

regression analysis. When presented with inputs inside this domain, but not appearing in the

training data, a suitably trained network will have the ability to generalize well.

The static multi-layer feed-forward network with biases and at least one sigmoid layer is widely

recognized for the capability of approximating any function with a finite number of discontinui116

/
ties (i.e. non-linear, coupled, and multivariable systems such as e.g. the GMAW process addressed here). In the context of this paper, a feed-forward network of this type, shown in Figure

1 , is applied.

x(No)

Figure 1 : Two-layer feed-forward network, performing the mapping y2 = F(x) : 9t o ^

91 2 .

The mapping from input to output can be expressed as follows:

=f^(A^y^_j +b^) fork = l,2,-,n

where x (No x 1) is an input vector, while y*: (Nk x 1) is an output vector from the k'ih layer. The

sigmoid-function is applied as transfer function: fk(s) = (l+exp(-s))'* (A^jtX 1). is a weight matrix of dimension NkXNk-i, bk is a bias weight column vector of length Nk, and n is the number of

layers. In this paper the Levenberg-Marquardt algorithm for non-linear least squares (a standard numerical optimization technique) is used with the back-propagation algorithm for training the feedforward neural network. In (Ref. 9), it has been shown that the Levenberg-Marquardt algorithm

is much more efficient than other known algorithms, when the network contains less than a few

hundred weights, which is the case in this paper.

Minimizing the risk of inexpedient over-training by use of a so-called regularization technique

(Ref. 10), is more important than guaranteeing a fast rate of convergence. Instead of minimizing

the sum of squared errors, F = Ed, which is the traditional approach for network training, the objective of this technique is to minimize a performance function with the following structure (for

training based on a set of weld data of size m containing: {x,t}(i),(x,t}(2),...,{x,t}(;„)):

m F = ^E^+ a% with E,=Y, (to) - f (to) " y1"

)

1=1

As shown in (Ref. 10), this modification of the performance function - the addition of the term

Ew, representing the sum of squares of the network weights - will improve network generalization, so that any modestly oversized network will have the ability to sufficiently represent the

true underlying function. The basic idea of the method, which consistently produces networks

with good generalization abilities, is that the true underlying function is assumed to have a limited degree of smoothness, for which reason this method constrains the size of the network

weights. The optimal setting of the regularization parameters, a and p, is based on the Bayesian

117
theory, and so this technique is referred to as Bayesian regularization. Furthermore, it is assumed

that the experimental data are infected by Gaussian noise, and finally, it should also be noted that

input and output, in this paper, are scaled similar to (Ref. 2):

^ 0.9-0.1 ( . ) + o.l

where /?max and p^m are maximum and minimum, respectively, of the physical data parameter p,

and pn is the scaled parameter applied to the network.

GENERAL CONSIDERATIONS CONCERNING THE WELDING EXPERIMENTS

The developed system is based on an ANN model, which facilitates the mapping from some

characteristic features describing the weld quality and joint geometry to some significant welding

equipment parameters. For that reason, it is assumed that a correlation between the characteristic

features, shown in Figure 2, and significant welding parameters (welding speed, voltage, and

wire feed speed) emerge during welding, by. which it is possible to design and train an ANN

model that estimate feasible welding parameters. Furthermore, it is to be noted that the control

system design is based on the static characteristics of the welding process.

Joint geometry

Reference weld

quality

Mathematical

model

W/M

Welding

equipment

parameters

Joint geometry

Weld

quality

Back-bead

height

Back-bead width

Figure 2: The process model maps weld quality and joint geometry into welding parameters.

To perform the described experimental validation, a PC-based welding system has been established, consisting of a welding machine, a two-axis table system, and a laser vision system,

which is applied for real-time seam tracking and online gap width measurement. The joint geometry is scanned 10 mm in front of the welding torch. A schematic diagram of the system is shown in Figure 3, while technical specifications relevant for the experiments are given in Table

1. Commanded voltage and wire feed rate

Figure 3: Structural diagram of the experimental system.

118
Joint type

Plate material

Plate thickness

Plate dimensions (LxW)

Power supply

Wire type

Wire diameter

Shielding gas Gas flow rate CTWD

Butt weld (without backing)

Mild steel (S235)

3 mm

330 X 50 mm

Constant voltage machine

EN440-G3Sil

01.2 mm

Mixture: 92 % Ar and 8 % CO2

15 1/min

10 mm

Welding torch orientation Horizontal (PA) with a travel angle of 90 degrees

Table 1: Technical specifications for the experiments conducted (CTWD: Contact Tip to Workpiece Distance).

OPEN-LOOP CONTROL STRATEGY

The experimental investigation of the applicability of ANN in an open-loop control strategy contains two steps: (1) To verify that the proposed ANN model based on static weld data in fact can

fit the characteristics of the weld task in a suitable way. (2) To examine the applicability of the

static ANN process model in an open-loop control system controlling a dynamic process.

Input parameters Output parameters

Back-bead height

Back-bead width Gap width

Neural network

Welding speed

Wire feed speed

Voltage

Figure 4: The developed process model applied to an open loop control system.

According to (1) the applicability of the developed process model (Figure 4), in terms of precision and robustness, has been demonstrated. A series of GMAW experiments has been conducted

to obtain the decisive knowledge about how the significant welding parameters affect the backbead geometry, given different joint geometries. Consequently, a total of 39 quality welds have

been made, and the welding parameters have been selected within the domain of useable parameters, when parts to be welded are fixed to produce given constant gap widths within a prescribed

interval. During the welding process, the gap width is scanned 10 mm in front of the welding

torch following the centerline of the gap, while the back-bead width and height have been measured afterwards. The relatively few experiments have been carried out in an unsystematically

way for gap widths within the interval of 0.0-2.3 mm (Table 2), and the main objective of the ex- periments has been to maintain homogenous back-bead geometries, as shown in Figure 5. Obviously, different sets of welding parameters for a given gap width can produce identical

back-bead geometries. Therefore, for later use of the weld data it is crucial to perform these preliminary experiments with a certain degree of attention to the aim of this original research.

119
Weld Wire feed Voltage Welding Back-bead Back-bead

No. speed speed width width height

[m/minj [VJ [mm/min] [mm] [mm] [mm]

8 3.8 19.0 325 l.OU J. IKJ

1 nn 1 .uu Id ^ n J.XJ Z 1 .u J 1

J

0 AO 7 80 0 60

25 2.8 18.0 310 2.00 3.70 0.85 26 3.0 18.0 350 1.60 2.80 0.40

Min 2.8 18.0 300 0.00 1.50 0.25 Max 5.8 22.0 375 2.30 5.10 2.70

Table 2: Four out of 39 experiments are shown with the boundary limits of each parameter. The

considerable variation of the gap width obviously implies major changes in wire feed speed and

voltage.

gS•o

U 2 a

(0

o o

'o o

2.5

1.5-2

I

1

-0.5

10 15 20 25 30 35 Experiment number Figure 5: The x's indicate the back-bead width, for which the solid line represent the mean value

(<vvf,> = 3.17 mm), while the o's and dotted line represent the back-bead height (</iz,> = 0.92

mm).

Based on these experimentally obtained weld data, a static multi-layer feed-forward network has

been trained by supervised learning. The trained network manages the mapping shown in Figure

4. The selection of feasible design parameters of the neural network, such as the number of hidden layers, the number of nodes in each layer, type of activation function etc., rely on the specific

modeling task. In this study, a two-layer network with a hidden layer containing three neurons and an output layer containing three neurons as well - according to the number of outputs - has

been selected. All neurons contain the sigmoid transfer function. Data sets from 33 of these ex- periments have been used for network training, while the remaining 6 data sets have been used

for testing the generalization capabilities of the trained network.

The Bayesian regularization technique (Ref. 10) introduces y, a so-called effective number of

parameters, which is a measure of the number of parameters (weights) in the neural network that are effectively used in reducing the error function. In the network training y = 19.52, which is much less than the 24 weights that appears in the chosen network. Accordingly, the neural net- work applied seems to be large enough to properly represent the true function.

The performance evaluation of the trained network has been based on a traditional error analysis

applied to the training and test data sets, respectively. Table 3 shows the absolute mean errors for

the welding parameters, and on the basis of these results the trained network seems to generalize

120

/
well. Furthermore, to investigate the network performance in further detail, a linear regression

analysis between the network response and the corresponding measured data sets has been performed. The result of this analysis is shown in Figure 6, and it appears that the wire feed speed

and voltage correlate well, while the welding speed does it barely as well. However, it is notable

that especially the wire feed speed has a great influence on the penetration, and consequently, it

is very important that this particular welding parameter is well correlated. Since the wire feed

speed varies between 2.8-5.8 m/min, while the welding speed only varies 300-375 mm/min, it is concluded that the influence of the welding speed is much less that the effect of the wire feed

speed in this series of experiments.

Wire feed speed Voltage Welding speed

[m/min] [V] [mm/min]

Training data 0.20 0.32 12.37

Verification data 0.35 0.41 12.36

Table 3: The prediction accuracy of the trained ANN is shown by the absolute mean errors for

the welding parameters. It seems as if the ANN identifies the underlying trends rather than just

memorizes the input/output relations of the experimental data sets.

Measured wire feed speed [m/min] Measured voltage [V] Measured welding speed [mm/min]

Figure 6: Predicted versus measured welding parameters for the complete data set. For wire feed

speed, voltage, and welding speed, the correlation coefficients are 0.95, 0.94, and 0.77, respectively.

Simulations of control performance were conducted with the trained network, and according to Figure 5, the desired back-bead geometry were set as hb = 0.75 mm and w^, = 3.0 mm. In Figure 7

and 8 results from a simulation with significant gap width variations along the joint - from 0.5

mm to 2.3 mm - are shown. From the photograph shown in Figure 7, it is seen how the frontbead geometry varies due to the continuous adjustment of welding parameters. As expected, the

front-bead geometry became larger when the gap width became smaller and vice versa. From

Figure 8, it follows that a uniform back-bead has been obtained, even though minor local variations of the height, as well as of the width, appear along the weld. The mean values of the backbead height and width are 0.76 mm and 3.19 mm, respectively, and in general, it is concluded

that quality welds, fully competitive with skilled manual welding, are achievable.

121
^—I—

h

50 0 50 100 150 200 250 [mm] Figure 7: A test weld and its measured gap width variation.

100 150 Travel distance [mm]200 250

1.5

0.5

Reference Measuremenls

50 100 150 Travel distance [mm]

200 250 100 150 Travel distance [mm]

Figure 8: Back-bead height and width according to the simulation shown in Figure 7.

CONCLUSION

Physical experiments have shown that the weld bead geometry is extremely sensitive to gap

width variations in GMA butt joint welding with full penetration, and consequently, a continuous

adjustment of welding parameters is essential for the realization of a uniform back-bead geometry. An open-loop control strategy, based on neural network technology, was experimentally investigated and applied successfully to this particular weld task. In fact, a series of laboratory based experiments shows, that it is possible to apply static data to

train an ANN to obtain a model of the GMAW process, and that the model is feasible for high

quality, open-loop butt joint welding with full penetration of 3 mm plates with variations in the

gap width from 0.5 to 2.3 mm.

Unfortunately, the weld quality has proven to be quite sensitive to changes in welding conditions

(not only the gap width), and since it is expensive, impractical, and often impossible to sustain

constant welding conditions in an industrial environment, the open-loop control strategy is typically too vulnerable to be applied in such disturbing surroundings. For that reason future work

will be focused on development of a closed-loop control system based on online monitoring and

control of the weld quality.

122

/
REFERENCES

1. Raveendra, J., Parmar, R.S.: Mathematical models to predict weld bead geometry for flux

cored arc welding, Metal Construction, Vol. 19 Issue 1, pp. 31R-35R, 1987.

2. Andersen, K., Cook, GE., Karsai, G, and Ramaswamy, K.: Artificial Neural Networks

Applied to Arc Welding Process Modeling and Control, IEEE Transactions on Industry

Applications, Vol. 26 No. 5, pp. 824-830, 1990.

3. Lim, T.G, Cho, H.S.: Estimation of weld pool sizes in GMA welding process using neural networks, Proceedings of the Institution of Mechanical Engineers, Part I (Journal of

Systems and Control Engineering), Vol. 207 Issue 1, pp. 15-26, 1993.

4. Chan, B., Pacey, J., and Bibby, M.: Modelling Gas Metal Arc Weld Geometry Using Artificial Neural Network Technology, Canadian Metallurgical Quarterly, Vol. 38 No. 1, pp.

43-51, 1999.

5. Chen, S.B., Lou, Y.J., Wu, L., and Zhao, D.B.: Intelligent Methodology for Sensing,

Modeling and Control of Pulsed GTAW: Part 1 - Bead-on-Plate Welding, Welding Journal - Including Welding Research Supplement, Vol. 79 Issue 6, pp. 151s-163s, 2000.

6. Chen, S.B., Zhao, D.B., Wu, L., and Lou, Y.J.: Intelligent Methodology for Sensing,

Modeling and Control of Pulsed GTAW: Part 2 - Butt Joint Welding, Welding Journal - Including Welding Research Supplement, Vol. 79 Issue 6, pp. 164s- 174s, 2000.

7. Di, L., Chandel, R.S., Srikanthan, T.: Static Modeling of GMAW Process Using Artificial

Neural Networks, Materials and Manufacturing Processes, Vol. 14 No. 1, pp. 13-36,

1999.

8. Lee, J., Um, K.: A comparison in a back-bead prediction of gas metal arc welding using

multiple regression analysis and artificial neural network, Optics and Lasers in Engineering, Vol. 34 Issue3, pp. 149-158, 2000.

9. Hagan, M.T., and Menhaj, M.B.: Training Feedforward Networks with the Marquardt Algorithm, IEEE Transactions on Neural Networks, Vol. 5 No. 6, pp. 989-993, 1994.

10. Dan Foresee, F., and Hagan, M.T.: Gauss-Newton Approximation to Bayesian Learning,

1997 IEEE International Conference on Neural Networks, Vol. 3, pp. 1930-1935, 1997.

123

ARC WELDING PROCESS SIGNATURE VISUALIZATION AND ANALYSIS

C. Hsu*

ABSTRACT

Arc voltage-current population (histogram) density two dimensional color plots are generated for

various arc welding processes, including CV spray, CV short-circuiting, pulsed MIG, AC

submerged arc, tubular electrodes. Surface Tension Transfer® (STT®), and stick electrode in pipe

welding.

Actual voltage and current signals are recorded at a sufficiently high sampling rate (50 KHz or

higher). Each voltage-current data pair, or data in combination with its first order derivates (e.g.

dv/dt, di/dt) is digitized to a pixel location on a raster image area. The occurrences of data pairs

at each location are accumulated for a fixed weld time. Color models (e.g. RGB) are used to represent the histogram depth, based on normalized density, from "warm to cold", as a signature

of each welding process. Attractors in the short region and the arc region are identified, and the

transition between regions, such as between short and arc, is animated and analyzed statistically.

Applications such as welding process stability and welding procedure optimization, derived from

digital image processing techniques, are illustrated.

KEYWORDS

Arc Monitor, quality monitor, weld quality assurance, arc stability, stubbing, GMAW, GMAW-P,

GMAW-S, SAW, SMAW, FCAW, STT, AC, CV, MIG, MAG, data acquisition, SPC, histogram,

statistics, pulse welding, pipe welding, short circuiting, dip transfer, spatter, droplet transfer,

digital signal processing, digital image processing

INTRODUCTION

Weld quality assurance is becoming an important consideration in manufacturing industry. Many

process variations in production, such as poor part fit-up, arc blow, poor grounding or voltage sense connection, multiple arc interference, disturbed gas shielding, poor wire feeding, etc. can

yield weld defects. A returned assembly from an automaker to its first tier supplier incriminates

not only the welding process but also the quality inspection process. Therefore there is a growing

demand for weld quality monitors, preferably based on simple signals, such as arc voltage and

current, wire speed and gas flow rate.

Techniques using statistical process control (SPC) tools such as data trending, tolerance chart and

sequential analysis are discussed by Cook et al. [1]. Statistical algorithms were developed by

Quinn et al. in CV arc welding that flag out defective welds by comparison with a baseline of

Engineering Manager, The David C. Lincoln Research and Development Center, The Lincoln

Electric Company, 22801 St. Clair Avenue, Cleveland, Ohio 44117

125
defect-free welds [2]. Power spectral density of arc signals is used to predict contact tip wear [3]

and porosity and burn-through [4]. Fast Fourier Transform of the current waveform is used to determine stability of short-circuiting metal transfer [5].

The goal of a weld monitor is obviously to catch all faults determined by an application's

acceptance criteria without any false alarms. Common faults in manufacturing are burn-through,

weld too cold or too small, porosity, undercut, wire stub sticking out of the weld, insufficient

weld length, weld off joint, and weld simply missing. The foundation of an effective statistical weld monitor comprises of 1) all faulty welds will be accompanied by significant arc signal

disturbances above the noise level; 2) an arc signal departing from the statistical norm will

necessarily produce unacceptable weld faults. These are presumptions in the absolute sense. In practice it is a leap of faith from arc signal behavior to weld quality.

One commercial system based on statistical methods called ARCAgent^"^ from Impact

Engineering is applied in semi-automatic applications [6] and another called ADM IVTM from

Computer Weld Technology is used in hard automation and robotic applications. Fanuc's Data

Monitor option integrated in its R-J3 controller tracks statistics of up to 6 arc signals at 250 Hz

maximum rate and reports error conditions, using Data Monitor Schedule to set upper, lower and

time limits. These systems work well to identify gross weld defects in simple CV spray process.

However, they have limitations arising from relatively low sampling rates (e.g. 10-1000 Hz) and

may not be effective in pulse welding, short-circuiting welding or STT welding. The arc waveforms are commanded at a few hundred Hz with large swings in amplitude, which make it difficult to identify statistical anomalies. In many automotive applications, the welds may last one second or less, and 1 KHz sampling only captures 1 ,000 data points where a good fraction is spent in arc strike, run-in, crater fill and arc end transients.

One approach is to use the power source itself as both waveform generator and waveform

monitor in synergic pulse welding and STT welding. This has the advantage of a priori

knowledge of time segmented commanded signals [7]. For example, the background time and

background current can be monitored and compared to programmed settings, when the power

source is adjusting background time and current to adapt for stickout changes at a specific

synergic wire speed. Fluctuation of background current or time would be indicative of process

instability. Externally extracting peak current or monitoring average current would not be as useful or effective when the power source uses a fixed peak current at a given wire speed. The

welder manufacturer can predetermine fault thresholds as part of the waveform development

effort, to be adjusted for a given application if necessary.

Barborak and Conrardy used histograms of current, voltage, power and conductance to detect

stickout, gas, and wire speed disruptions in short-circuiting welding [8]. Cross-plots, or scatter diagrams of voltage and current produce a graphical view of the short-circuiting process.

Simpson applied chaos theory and drew his cross-plot on an adaptive scale to improve resolution

in regions of interest, using arc voltage and an "artificial current" derived from the weighted time

history of arc voltage [9]. A plot is generated every second with a 8 KHz sampling rate for online

stability computation and fault detection, commercially known as WeldPrint^^.

/

126
This paper introduces a 2D color artificial image to represent a histogram of arc signals, using

arc voltage, arc current and their time derivatives, to display and analyze the stability of many

arc welding processes.

DATA ACQUISITION AND ANALYSIS SOFTWARE

Equipment

Arc current and voltage are acquired using a high-speed data acquisition device with channel-tochannel isolation, 14-bit measurement resolution, and 250 KHz aggregated sampling rate. Most

of the data is collected at 50 KHz - 75 KHz per channel. Current is measured by an AEMC

MR561 current probe. The data acquisition box streams data to the laptop hard disk through a USB connection. All instruments are NIST traceable.

Arc Caliper

Arc Caliper is a software program developed at the Lincoln Electric Company to analyze highspeed data acquisition files producing arc signal and wire feeding statistics, timing and

synchronization statistics, arc start behavior, burn-back and arc ending characteristics. Optionally

a

2"^* order Butterworth low pass digital filter with 3dB cutoff set at 2500 Hz can be engaged. Ad

hoc algorithms are developed to represent or estimate arc stability, spatter, heat input, fume,

operating voltage etc. for welder, feeder, robot and consumable evaluation. It is written in Java

for portability to non-Windows platforms such as PDA or mobile phone as part of an online user

interface or as an embedded web page residing in the power source or robot, and for ease of

factory networking.

One output of Arc Caliper is the so called voltage-current population density plot. Each voltagecurrent data pair is saved to a pixel location on a raster image area. The occurrence of data pairs

at each location is accumulated for the entire weld (e.g. 20 seconds of welding). Color models

(e.g. RGB or HSB) are used to represent the histogram depth, based on normalized density, from

"warm to cold", as a signature of the welding process. The signals can be played back (animated)

to reveal the dynamic behavior of a measured process and the results can be exported as a spreadsheet.

APPLICATIONS IN ARC WELDING PROCESSES

CV Spray transfer process

A stable CV spray process above the transition current is simply one blob in the VI density plot.

The weld is done at about 22.5V and 365A using S-6 wire on an inverter. A sequence of white,

yellow, red, pink, and blue is used to represent the decreasing levels of normalized frequency of

digitized voltage-current pairs, or histogram density. Most of the data falls within the "hot zone"

where the zone width corresponds to inverter ripple and the blue fringes may be explained by the

127
voltage disturbances from "incipient" shorts when a droplet is stretched and may briefly kiss the

puddle surface before being detached.

Vottaga Amperage Populafton Density

Figure 1: CV spray VI density plot from PowerWave 455, L-56 electrode, 90% Ar+10% CO2

Pulsed spray process

7m

'

. i

i

. !

1

0 HI I i 1 ^ .

i \

I

1 Q 20

Voltage Figure 2: Pulsed spray transfer VI density plot from PowerWave 455, L-50 electrode, and 90%

Ar+10% CO2

Two distinctive "attractors" or hot zones appear in pulsed spray transfer process, corresponding

to the high peak current and low background current and their transition behavior. Both zones are above 12V implying that a plasma is always present.

CV short-circuiting process

A typical CV short-circuiting process (see Figure 3) also has two zones, one at low voltage or in a short, and the other at high voltage 15-2 IV. During the short the current is slowly ramped up,

128
applying pinch force to the liquid bridge, until it breaks into an arc, at which instant it snaps into

the arc zone.

Vottage Amperage PopyMonDens^

7O0 ~

350

0 20 40

Voltage Figure 3: CV short-circuiting VI density plot from PowerMIG 300, L-50 electrode, 90% Ar+10%

CO2

STT controlled short-circuiting transfer process

360

vettageAfTiperage Population Density

0 20

Vottage 40

Figure 4: STT VI density plot from STT II, AutoWeld electrode, 75% Ar+25% CO2

The low spatter STT process has four hot zones in Figure 4. A background current of 70 amps

maintains the arc and contributes to plate heating at the right bottom corner. After the electrode

initially shorts to the weld pool, it jumps to the low voltage-low current zone to ensure a solid

short. Pinch current is then applied following the S-shaped curve to the top left zone. Molten

metal is squeezed down and the derivative of voltage (dV/dt) is monitored. When the liquid

bridge is about to break, dV/dt increases sharply and power source reacts by driving the process

back to the low voltage-low current zone. Immediately following the arc establishment, it

129
swings up to the high current-high voltage zone on the top right corner, to produce plasma force

pushing down the weld pool. Finally it returns to the background zone and stays there until it shorts again.

AC submerged arc process

Voltage Amperage 1Population Density

1 000 ]

0

-100C-60 0 Voltage

60

Figure 5: AC sub-arc VI density plot from PowerWave AC/DC with L-60 electrode and 860 flux

The AC submerged arc process circulates between a DC positive zone and a DC negative zone in a loop. One advantage of inverter based, computer controlled AC sub-arc equipment is exhibited

in the transition between the two zones. The current is being reduced gradually so that zero- crossing occurs at low current in order to reduce stress applied to the power electronics. It also

shows that the zero-crossing occurs rapidly with a large voltage swing from the center tap choke

design that prevents arc outages,

CC process with stick electrode

Pipe welding using E6010 electrode on an electronically controlled engine welder is shown in

Figure 6. The main hot zone is characterized by a constant current with voltage ranging from 18-

34V. The stick electrode is being whipped back and forth, and shorting does occur as shown in

the left hot zone. Transition dynamics in the VI plot from the shorting zone and arc zone are

essential for puddle fluidity control in all position pipe welding.

130
Figure 6: CC process VI density plot from Ranger 305G, Fleetweld 5P+ electrode

Globular transfer process

Figure 7: Globular transfer VI density plot from Invertec® V350, Innershield® electrode, no gas

131
Globular transfer is exhibited in an example of pipe cap pass welding by an inverter V350 and

self-shielded flux cored electrode shown in Figure 7. The process stays mostly in the arc region

above 13V building up a large droplet hanging at the bottom of the electrode, and occasionally it shorts briefly to transfer the molten ball to the puddle and returns to the arc region. The size and

shape of the hot zone and its blue fringes usually indicate the equipment's ability to hold a

constant arc length.

Advanced cross-plots

Voltage Vollage-Change PopulaWon Density 10 MMMMHH

0 20 40

; Voltage Figure 8: Pulsed spray transfer V-dV density plot from PowerWave 455, L-50 electrode, and

90%Ar+10%CO2

40

20

Voltagetn] Voltage[i>li Populatlon Density

Voltage Figure 9: Pulsed spray transfer V(n) vs. V(n-l) density plot from PowerWave 455, L-50

electrode, and 90% Ar+10% CO2

132
Figure 8 illustrates pulse welding in a voltage vs. time derivative of voltage image. Time derivate

of voltage is useful in prediction of short-to-arc transition and droplet detachment. Figure 9

illustrates the same process in voltage vs. its immediate past. Comparing a signal to its past

history can serve as a predictor of change. Figure 10 illustrates the same process in current vs. time triggered at 150A down-slope, where the fluctuation of the waveform including shorting

response is clearly visible. Other graphs such as voltage-time graph, cyclical current period vs. voltage period can also provide insight into the arc welding process. Arc power, arc impedance

and their time derivatives can also be used for plotting. In general, an arc signal passing through

a digital filter with any transfer function, with IIR or FIR implementation in the Z domain, can be used as one axis or both axes in the plot. Digital image processing techniques can be applied

to extract weld quality and arc stability. Unstable process tends to have an "out-of-focus" VI plot

without high frequencies in its power spectrum. Segmentation, template matching and statistical pattern recognition can be used to distinguish a defective weld from an acceptable weld.

800

/-

Current Time PopulaBon Density

1

A

a

m 400 I, it

J- -r' . y'C

. {

7

1 0 \ i

f : 1

i

i D 10 Time (ms) 20

Figure 10: Pulsed spray transfer arc voltage vs. time density plot from PowerWave 455, L-50

electrode, and 90% Ar+10% CO2

This paper is intended to provide an overview of using a voltage-current population density plot

to characterize each arc welding process. Concepts for using this technique in analyzing process

stability, power source and consumable dynamic performance characterization, procedure

optimization, waveform development, weld defect identification, and fume and spatter

estimation are evident but not explored in detail.

133
CONCLUSION

Voltage-current cross-plot histogram population densities of many arc welding processes are presented, including CV spray, CV short-circuiting, STT, pulsed spray, CC stick, AC sub-arc and

globular transfer with tubular electrode. Image analysis tools can be employed to measure arc welding process stability from artificial image created by arc signals directly, their time

derivates, or digitally filtered arc signals.

ACKNOWLEGEMENT

The author wishes to express his appreciation to James Heam for conducting many experiments

cited in this study.

REFERENCES

1. Cook, G.E. et al. 1997. Statistical Process Control Application to Weld Process. IEEE

Transactions on Industry Applications. Vol. 33. No. 2.

2. Quinn, T.P. et al. 1999. Arc Sensing for Defects in Constant-Voltage Gas Metal Arc

Welding. The Welding Journal. September 1999. 322s-328s.

3. Siewert et al. 1993. Sensing of Gas Metal Arc Welding Process Characteristics for

Welding Process Control. United States Patent 5,221.825 . 4. Crane, B.L.; Kilty, A.L.; Ludewig, H.W.; Jones, B.A. 2001. Method and apparatus for

monitoring weld quality, United States Patent 6,184,491.

5. Lucas, W.; Tapp, J. 1998. Development of a PC based system for monitoring arc welding

parameters. Research Report 88264.01/97/966.3 . The Welding Institute.

6. Ivkovich, S.P., Noch, R.F. 2000. Automated Verification of Complex, Manually Welded

Production Assemblies. Gas Metal Arc Welding for the 21^^ Century Proceedings

7. Hsu, C. 2002. Monitor for Electric Arc Welder. United States Patent 6,441,342. 8. Barborak, D.; Conrardy, C. 1998. Monitoring of Weld Quality During Thin-Sheet GMA

Welding. Cooperative Research Program Summary Report SR9806. Edison Welding

Institute

9. Simpson, S.W. 200 1 . Weld Quality Measurement. United States Patent 6,288.364.

134
NETWORK COMMUNICATIONS FOR WELD CELL INTEGRATIONSTATUS OF STANDARDS DEVELOPMENT

W.G. Rippey**

ABSTRACT

Network technology is being used more and more to replace direct-wired links to integrate

equipment components of automated and semi-automated welding systems. The standardization

of device-level network technology, and the gradual appearance of network capable welding

devices, means that users and integrators are facing the challenge of using networks for the first time, or of choosing one among multiple kinds of networks. A survey of network technologies

currently being used for welding products is presented. Two formal welding standards efforts, one by the American Welding Society (AWS) A9 Committee and one by the Open DeviceNet

Vendor Association (ODVA), are described. An effort by the Robotics Industries Association

(RIA) in sponsoring general purpose network standards for robots is described. Examples of

non-standard uses of device-level networks in welding products are given.

The AWS A9 committee can gather experts to express the needs of vendors, integrators, and

users, and if appropriate develop standards that will reduce the number of diverging choices to be

made.

This paper should be useful to users and integrators of weld cells to evaluate the different

network technologies available for implementing welding systems. It may also help device

developers make decisions about which technology to adopt, or it may suggest new welding

areas where networks can be employed. The goal of standards efforts is to make a common

communications solution available for new products and new systems, allowing vendors to provide compatible component products to integrate. Standards can also ease the burden of

system developers who currently spend significant effort designing custom protocols or point-topoint interfaces.

KEYWORDS

CAN bus, A9.4, Ethernet, networks, high level protocol, weld cell integration, robotic welding,

automatic welding, standards

INTRODUCTION

Networks are being used in many industries. Go to any network web site (Ref. 1-9) and see their

"applications". You won't see welding. But there are welding products that use network

technology and their numbers will probably increase.

What is network technology? One view is of the physical interfacing connections. To integrate

products of different vendors, current methods use the connectors like those in Figure 1 . Use of

networks would use a single type of connector and cable on all devices. Each networked device

has its own input/output circuitry for processing a transducer signal, and embedded software to participate in the network protocol. Instead of using one wire per control signal conveyed, a network uses a digital message with parameters that are configured in software. What is the

* National Institute of Standards and Technology, Gaithersburg, Maryland.

135
benefit from using networks? The main benefit is easy connection of a wide variety of devices

for real-time control. Wouldn't it be efficient to easily cable together a robot, power source, and

a laser vision system with a single connector and cable style and then configure some software

parameters to have them work together to weld ship joints? And networks make possible the

exporting of data from within cells and devices for use by computer controllers and/or human

monitors, and even internet processes.

Figure 1. Multiple connector and cable types found in a welding cell.

Weld Cell Integration - the Technology is Changing

Figure 2 shows the point-to-point connections, typical of a non-networked weld cell. The legacy

of point-to-point connection can be represented by an AWS standard. Guide for Components of

Robotic and Automatic Welding Installations, D1 6.2-2001 . It describes a 37 pin cable and

connector that connects a cell or robot controller to a power source. Each wire conveys binary

interlock or 0-10 volt analog signals to and from the power source.

on-Standard interfaces

Figure 2. Opportunities for network connections in a weld cell.

136
Development of Networks in Other Industries

Several industries are moving forward with comprehensive formal standards for their

applications. Table 1 shows a few of them. Their approaches take a broad look at the

communications needs for multiple devices that work in integrated units.

INDUSTRY STANDARD

Semiconductor manufacturing SEMI E4, E5, 54-0997, IPC 2500

Motor vehicle control SAE J 1939, J 1708 (heavy equipment), LIN

Building HVAC and control and monitoring BACnet, LonTalk (EIA 709.1)

General purpose interface to robots RIA R15.04 Committee Drafts

Packaging Leverages device standards of e.g. CAN,

Profibus, Ethernet/IP, DeviceNet

Process Instrumentation HART

Table 1. Comprehensive Industry Standards

Some standards have narrower scope and define standards for specific devices. Examples are shown in Table 2. These efforts define the characteristics of individual devices within the

framework of an established general-purpose network specification.

APPLICATION NETWORK

Bar code readers CAN based, Profibus, Ethernet, . .

.

Weaving machines, extruder downstream

devices, asphalt paver, diesel locomodves

CANOpen

Lab devices, water treatment Profibus, Profinet

Coal Mining Australian project using Ethernet/IP

Components of petrochemical, refining control Foundation Fieldbus

Pneumatic vales, AC/DC drives SDS, DeviceNet, Profibus

General purpose devices: Analog 10, binary

10, motion control, valves, photo sensors, proximity sensors, temperature, pressure, limit

switches, motor drives

Most field buses

Table 2. Standard Application-Specific Devices

Most industries use generic component-level specifications defined in device-level standards.

These specifications define standard interfaces to binary and analog input/output (I/O), proximity

switches, limit switches, photo sensors and encoders.

How much is welding control like other applications? Can current networks meet the

requirements of welding? Table 3 shows analogies between current standard network devices

and welding devices. Many simpler welding devices fit well with models used in other

industries. The more complex devices will need welding-specific models but still fit the

performance guidelines of existing networks well. The most challenging welding application

for networks is the coupling of torch positioners (including robots) with adaptive control

processes like thru-arc tracking, or with real-time vision sensors that "see" weld joints and report

137
parameters as the torch is moving. The devices involved are fairly complex, with rich data sets needed for configuration and control. Data rates must meet the needs of torch positioning to produce good welds while adapting to changing sensor parameters.

Welding Component Standardized Network Device

Clamp, wire snipper Binary actuator

Part positioner Motion controller

Switches, optical and proximity sensors Switches, optical and proximity sensors Power source Analogous to position/motion controller

Robot or cell controller PLC or cell controller

Sensor driven torch positioning system Coordinated multi-axis motion control e 3. Standard general purpose networked devices and similar welding components

WELDING CELL ARCHITECTURE

Opportunities for Networks Inside Welding Cells

The interfaces shown as lines in Figure 3 can be implemented using network technology.

Additional devices could include safety sensors, clamps, wire snippers and manual switches.

The power source could be a controller for some of the devices, or a robot or PLC could be used.

The primary issue for standardization is, are there useful generalizations of each device type that can be used as a standard device profile so that products from different manufacturers will

respond the same to network messages, and perform the same functionality. An important

vendor concern about standards is that they must also accommodate use of unique device

features.

Issues for Welding Component Developers and Integrators

At this time there are very few commercial network-capable products, and no formal welding

standards. There is no network technology that dominates in number of available welding

devices, and thus no obvious preference expressed by users. Component developers and

integrators must risk choosing a network technology with no guarantee of future availability of

more devices. In many cases they develop their own network capable devices instead of buying

them, and in some cases invent new protocols. Regarding choice of network technology, in

some cases where different networks have similar data rate and message properties, gateways

can be used to translate messages back and forth. This can ease the integration effort. Some

current standard network pairs can by linked by off the shelf gateway products.

There are few off-the-shelf welding devices that come network ready. Some companies develop

their own interfaces and protocols because of this. An example is a maker of automated pipeline

welding systems that uses basic Controller Area Network (CAN) bus technology with a proprietary high level protocol for their in-house developed components (Ref 11). Standards,

and products that conform to them, could have reduced their integration effort. They got their

system running with a simplified protocol while the standards situation is not well defined, but

will have to develop their own compatible devices. Standards can help pursuade component

vendors to implement network interfaces because conformant products are guaranteed to be

compatible. Developers within each industry are looking to implement the smallest set of

138
network choices to make their products attractive in the market, where there isn't and probably

won't be a single dominant network technology.

Arc Data Monita

y

Power

Sojrce

PC

d Monitoring,

Configuration,

Prog-anming Tods

Positioning Device

Gas

Controller

Wre Drive and Old

(§)(§)

00

User Conttds

Flme

Bdracta

Wre Drive and Gun

Controller

0

V\feter Cooler

Figure 3. A sampling of welding components interconnected within an automated cell.

CURRENT COMMERCIAL NETWORK TECHNOLOGY FOR WELDING

There are three broad layers of network technology that are targets of standardization. The

Packet layer defines cable, connectors, and electrical signals, and raw packets of data. The

High Level Protocol (HLP) defines the formatting and meaning of packets and how they are routed, and general definitions of device and data types. The Device layer describes very

specific application-dependent devices, their internal information, and their functionality in

response to HLP messages.

Non-Standard Network Interfaces

Table 4 shows examples of welding products that implement network technology, but do not

implement standard, formal public specifications. The packet layer and HLP used are usually

formal standards, but the device layer is defined by vendor specifications.

139
PRODUCT NETWORK TECHNOLOGY

Wrist interface, resistance welding controller,

resistance weld monitoring, welding robot (for

10), power source

DeviceNet, InterBus

Resistance welding controller Profibus, ControlNet

Power source CANOpen, Ethernet with proprietary protocol

Pneumatic valves, AC/DC drives SDS

Automated pipeline welding system (Ref 1 1

)

CAN bus with proprietary protocol

Wire feed, gas controller, power source CAN Bus with proprietary protocol

Table 4. Examples of welding products with non-standard interfaces

CURRENT FORMAL WELDING STANDARDS EFFORTS

There is one formal public standard for welding devices that encompasses the device layer. The

InterBus-S Weld- COM-Profile CO Welding Controllers [Ref 5] specifies 16 bits of command

data and 16 bits of status data. The remainder of the 4 byte interface is allocated to manufacturer

data. This narrow scope interface may not support modern power sources. There are two

projects underway to develop complete interoperable public specifications, by the American

Welding Society (AWS) and the Open DeviceNet Vendor Association (ODVA). Both

techniques use the CAN bus and protocol for conveying messages, but they differ in their scope

and the details of their application level protocols. Both techniques use 5 wire cables that

include 2 wires for DC power. There are also variations in physical cable specifications and the

electrical characteristics of signals.

The American Welding Society develops public standards for a wide variety of welding products

and processes. The resulting standards are called "American Standards" because the

development procedures are proscribed and monitored by American National Standards Institute

(ANSI). Participants in AWS standards activities can be any interested and qualified person.

The AWS project, A9.4 Specification for Data Structures and Protocols for the Exchange of

Intra-cell Welding Information, specifies a device-level network for integrating welding

components such as power source, wire feeder, gas controller and positioner. Figure 3 comes

from the A9.4 document. A9.4 uses the standard CAN specification (ISO 11 898) for the bus

access arbitration and message encoding. A9.4 defines services implemented using network

messages, and ways to define the devices that use the network. It also specifies the descriptions

of welding devices in terms of attributes and services that can be accessed. A9.4 is an ongoing

project and should be published in 2004. AWS also has a committee on Robotics, called D16,

which could sponsor robot-related specifications.

ODVA is a controls and networks industry vendor organization (Ref 5). It sponsors the

DeviceNet and Ethernet/IP specifications for networks. Participants must be members of

ODVA. The DeviceNet specification contains several general purpose devices, with provision

for Special Interest Groups (SIGs) to develop further refinements of device profiles for specific

applications. The Arc Welding SIG is working on a DeviceNet profile and lO addressing

scheme for an arc welding power source. The network view of the power source includes status of lower level devices but no control of them. The interface would be used by a robot controller

140
or cell PLC to configure welding parameters, command arc on/off, purge gas, and to assess status

like voltage and current, arc-started and faults. The scope of this spec is "looking into" the

power source from supervisory control. The A9.4 scope covers the interfaces to devices

typically "seen" by the embedded real-time controller of a power source or PLC. A network

configuration using both specifications would be a DeviceNet interface conveying commands to the integrated power source, possibly from a robot controller, and the internal controller of the

power source connected to the wire feeder and gas controller using A9.4.

A standards effort that could affect welding systems is the Robotics Industries Association (RL\)

R15.04 Communication and Information Committee (Ref 12). RIA is a North American trade

group formed to serve the robotics industry. R15.04 is working on general purpose interfaces to robot controllers, using Ethernet, for functions including controller boot up, configuration, file transfer, status reporting. The committee has begun by choosing among the wide list of current

services and defining standard data sets for robots, rather than developing new communications

protocols. The effort will eventually address real-time control issues. The specification could

eventually apply to welding robotics.

What Organizations Would Sponsor Formal Welding Network Standards?

Specifications for welding devices could remain as informal vendor-developed or corporate

versions that could be quite useful to users and integrators of welding systems. However formal

standards can produce greater market effect and lead to wider adoption of networks in welding.

The most common candidate technologies come from industry associations that represent

network technology segments or specific industries. The A9.4 document is an exception to this

trend, and AWS, as an American national standards organization, is the sponsor of the standard.

AWS standards are eligible for International Organization for Standardization (ISO)

consideration, but no plans are yet made for A9.4.

Industry association specifications are often adopted by national, regional (e.g. European), or

international organizations. The most likely protocols for welding devices are: DeviceNet,

Ethernet/IP, ControlNet, Profibus, CANOpen and ModBus. DeviceNet (EN 50325-2:2000),

CANOpen (EN 50325-4:2002) and parts of Profibus have been adopted by the European

organization CENELEC (Ref 8), and by International Electrotechnical Commission (lEC). It could be expected that welding device specifications would also follow this route. CENELECs network committee would be Technical Body CLC/TC 65CX (Fieldbus). From their mission

statement: "CENELEC, the European Committee for Electrotechnical Standardization, was

created in 1973. CENELEC is a non-profit technical organization set up under Belgian law and

composed of the National Electrotechnical Committees of 20 European countries. CENELEC's

mission is to prepare voluntary electrotechnical standards that help develop the Single European

Market/European Economic Area for electrical and electronic goods and services by removing

barriers to trade, creating new markets and cutting compliance costs."

The lEC is an international organization that works with CENELEC and ISO to jointly develop

documents. lEC prepares and publishes international standards for all electrical, electronic and

related technologies. The lEC group that would develop welding network specs would be TC65

(Industrial Measurement and Control), SC 65C (Digital Communications) or SC 65B (Devices).

The ISO branch is TC 184 (Industrial Automation Systems)/SC5 (Communications and

Architecture).

141
SUMMARY

Network capable products are used in manufacturing, process control, and transportation

industries extensively. Network implementations for control seem to be yielding the advertised

benefits of easier integration, ability to convey data outside of cell limits, more reliable operation

than point-to-point, and possible wider marketing opportunities for smaller equipment vendors.

A few networked products for welding are available: some use proprietary protocols and some

leverage public standard networks, complemented by vendor-defined device profiles. It is likely

that the welding industry could benefit from more extensive use of networks for device control,

with standards helping to guide component vendors and system integrators in narrowing the

choice of network to use among the wide variety available.

There are currently two formal network standards efforts for welding specific devices. There are more non-formal public and proprietary implementations, which may make their way to the

formal public standards arenas. AWS supports the "A9.4" effort and can serve as a forum for

interested users, vendors and integrators to work together.

REFERENCES

1. ARCnet Trade Association, http://www.arcnet.com.

2. ASHRAESSPC 135, sponsor of BACnet. http://www.bacnet.org.

3. CANOpen, http://www.canopen.org. 4. Foundation Fieldbus, http://www.fieldbus.org.

5. InterBus, http://www.interbusclub.com/en/doku/index.html .

6. Modbus, http ://www. modbus. org. 7. Open DeviceNet Vendor Association (ODVA), sponsor of ControlNet and Ethernet/IP -

http://www.odva.org. -

8. Profibus, http://www.profibus.org

9. Smart Distributed System (SDS), http://content.honevwell.com/sensing/prodinfo/sds/. 10. European Committee for Electrotechnical Standardization (CENELEC),

~ http://www.cenelec.org.

1 1 . IPC website for semiconductor manufacturing specifications,

http://www.ipc.org/html/fsstandards.htm, and http://www.gencam.org. 12. "Men and Machines Set New U.S. Pipeline Productivity, Quality Record". Welding Design

and Fabrication Monthly, Nov. 2002, Penton Media Publisher. Article available at http://www.appliedwelding.com/arcs in action/arc lsu02.html . 13. NIST/RIA Open Architecture Workshop, http://www.isd.mel.nist.gov/projects/openarch. RIA R15.04 Chairman Jeff Fryman, (734) 994-6088, jfryman@robotics.org,

http//:roboticsonline.com.

14. Sink, Perry, "A Comprehensive Guide to Industrial Networks", June 2001, Sensors

Magazine, http://www.sensorsmag.com/articles/0601/28/index.htm

15. AWS A9.4, working draft 8. Specification for Data Structures and Protocols for the

Exchange ofIntra-cell Welding Information.

16. Balfour, Chris, Smith, Jeremy, "Controller Area Network (CAN) Applied to Welding

Systems", Proceedings of Welding Technology Institute of Australia, 50th Annual

Conference, Melbourne, Australia, August 2002.

/

142
Commercial equipment and materials are identiHed in order to describe certain

procedures. 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

materials or equipment identified are necessarily the best available for the purpose.

143
o .2 -5

o

5"1

2 2 5?

^

Co

144
is

Id

00

o

0^
146
147
148


<

^ O

c O

2 z;!

o 2

o

o

I ^ ^ ^

C3 O ^3

CD..

8 H Q

5 ^ o

O 1:- O

Oh P >

<N m ^ r-- oc

' m i/^

151
O

CD

s

a.

13 ^ O C

C3 (/3 Oh go

o

o

u ;3 < o

;

a.

00

03

/
153
154



\ /

158
bX) ^3 ti)

ri. ^ r^^

3 o

159
160
161
/
I

163
164
g

'Si) > oo

cdOO

C3

O• oa

p-i oOO

a,

>^o

13

415

165
mi

il

mm

St!

I

Mi

CO

r <• ^ r-l

P Ph

Ph H O

0-)

m

o

oo

166
167
1 /
169
p

mm

m

^^^^

in

WHMl

p

o

o

o

^1 I

IMHK

^^^^^^^^ ^^^^^^^^^

W^^MS*^^i

170
Computers and Automation

171

A COMPUTER PROGRAM FOR CHOOSING WELDING PARAMETERS

IN SPIRALLY WELDED PIPE PRODUCTION

Kahraman §iRiN, Mannesmann Pipe Company

§ule Y. §iRiN, University of Kocaeli

Erdin^ KALU^, University of Kocaeli

Abstract: The main problem faced in the manufacturing of the pipe by SAW is the

choosing of the process variables. Therefore, identification the effect of the process

parameters and finding their limits are essential in order to get the required bead size

and quality. Regarding with the pipe production, a computer program in Excel is developed to choose the welding parameters. This program is mainly based on the

relation between welding parameters and weld bead size and quality.

Introduction: The principle of the spiral pipe production is simply the bending of the

steel strip into cylindrical shape and ultimately welding edges together in two steps as inside and outside weld. The submerged arc welding (SAW) process is widely used

for spirally welded pipe production. In this process, welding parameters namely

welding current, arc voltage, weld speed, diameter of the wire, number of the

electrodes, melt-off rates, edge joint design including bevel angle and root face, wire

configuration and etc. are very effective on welding quality. All those parameters

should be optimized to get satisfactorily weld quality. This computer program is developed to optimize welding parameters. The working steps of the program are given below in detail.

The working steps of the program:

After entering the diameter and wall thickness of the pipe to the program, it will be

seen automatically permissible number of the electrodes, which can be used for inside

weld. In spirally welded pipe production, there is only a very limited area to positioning the inner welding heads, when the diameter of the pipe is getting smaller.

Therefore, the number of electrodes for inside weld should be limited in accordance

with the diameter of the pipe.

Secondly, the program asks the required pipe production to define the main

geometrical dimension of the weld. Penetration, width of the bead and height of the

reinforcement are taken into consideration as main characteristics of weld by the

program. Penetration, for both inside and outside weld, should be at least 60 per cent of wall thickness to provide good joint. Width of the bead should be as wide as to give

enough surface area to the gases to leave easily to the weld pool.

As a third step of the program, the grade of the material is asked to give the lower and

upper limits of the heat input. An increase in heat input could result in a wide heat

affected zone (HAZ) with low impact strength. In spirally welded pipe production,

high rates of welding speeds can be achieved by using multiple electrodes. In two-pass

173
SAW process, each pass (inside and outside weld) can be considered as one puddle even using multiple electrodes. Heat input for each pass could be calculated equation

given below;

H„ = (fII.E)/v

where, //„ is net heat input, / is welding current, E is arc voltage, v is welding speed,/

is heat transfer efficiency and n is number of electrodes. Also, two-pass multielectrode SAW process could be characterized by cooling time, which is around 50

seconds. Cooling time, between 800 to 500 "C, can be calculated by using Rosenthal's

equations given below.

" 2kX ^^ 500 - To 800 - T,

On the next step, user should define the joint preparation in conjunction with the wall

thickness. Generally, Y or X type of joints could be used for thickness from 8 mm (see

figure 1). The main purpose of joint preparation is to ensure required penetration.

Here the main criteria are the angle of the bevel and the height of the root face. The

bead width and depth ratio (W/D) should be between 1 and 4 to prevent hot cracking.

Therefore, bevel angle should be bigger than 60°. This will also help not to having

flux inclusions in the weld. The root face could be 60 per cent of wall thickness.

I type Y type X type

Figure 1. Type of joints

After definition of joint type, automatically the cross-sectional area of reinforcement

and bevel are calculated in accordance with weld geometry design and type of joint.

By this way, the cross-sectional area of the added filler metal in mm" is cleared. This

is done for both inside and outside weld separately. After that, the program easily

computes possible highest weld speed. As a last step of in this stage, the weld speed is chosen by the user according to permissible limits given by the computer.

>

1

174
Finally, the user should choose welding currents starting from the first head of the

inside weld then second and third ones if necessary. Multiple arc welding usually

consist of a DC(+) welding head on the lead arc followed by AC welding heads on

trailing arcs. To define the current levels it is necessary to know the relation between

current and wire melt-off rate. The below equations developed by Chandel and Baya

could be used for predicting of the deposition rate of submerged arc welding process;

where MR is melting rate in kg/h, I is welding current, L is the electrode extension

and d is diameter of the wire.

MR DC(+) = 0.010371.1 + (2,2426x10"* .I-.D/d" - 0,462

MR DC(-) = 0.016178 .1 + (2,087x10"^I-.LVd- - 0,643

Measured wire feeding values, under various conditions, are plotted in Figure 2. It can be seen that actual values are closed to the theoretical values, originated from

Chandel' s equation (Table 1). There is around 5,5 % difference which is acceptable to make prediction about deposition rate.

Table 1. Comparison between actual and theoretical values about deposition rate.

#

Type of

current

Curreot (A)

Wire

diameter

(mm)

Wire

extension (mm)

Average

feeding

speed

(m/min)

Calculated

deposition

rate (kg/hour)

Deposition

rate acc. to Chandel

(kg/hour)

(%)

1 AC 675 3,2 26 2,77 10,30 10,91 5,6

2 DC(+) 600 3,2 26 2,02 ^^.81 3,8

3 AC 460 3,2 28 1,87 6,94 6,81 1,9

4 DC(+) 750 4,0 26 1,59 9,26 9,37 1,2

5 DC(+) 550 4,0 26 1,05 6,09 6,34 3,9

3,5 •

0,5 •

0,0

' -

Figure 2. Wire feeding speed diagrams.

175
On the real work condition, another experiment is done to check the deposition rate. 1016 X 17,7 mm dimension pipe is produced under various conditions. The weld

speed is changed while all the other electrical parameters, given in Table 2, kept

constant. Geometrical dimensions are measured by taking samples from each step of

the weld process. Then the real deposition rate is calculated (Table 3). Maximum 5,4

% difference is found in compare with the formula basis calculation. The sequence of

the weld seam is given in Figure 3.

Table 2. Electrical parameters of 1016x17,7 mm dimension pipe.

Inside weld Outside weld

Current

(A)

Voltage

(V)

Wire

diameter

(mm)

Current

(A)

Voltage

(V)

Wire

diameter

(mm)

. 0C (+) 1100 30 4,0 1100 30 4,0

LAC 850 30 3,2 750 30 3,2

I. 2. AC 550 30 3,2

Table 3. Electrical parameters of 17,7 mm wall thickness pipe

Weld

speed

(na/min)

Weld

position

Cross

sectional area of

the added

filler metal (mm^)

Cross sectional area of the reinforcement

(mm^)

Cross

sectional area of

the weld

bead

(mm^)

Deposition

rate (kg/hour)

Theoretical deposition

rate for 26

mm wire

extension (kg/hour)

%

Inside 48,22 23,51 145,52 40,10 28,47 4,2

1,80

Outside 31,85 21,37 120,81 26,49 27,90 5,0 1

Inside 58,53 33,82 169,76 40,56 38,47 5,4

1,50

Outside 39,89 29,41 153,11 27,64 27,90 1,0

< Inside 72,46 47,75 195,63 40,17 38,47 4,4

1,20

Outside 48,19 37,71 187,24 26,72 27.90 4,2

/

176
1,80 m/min 1,50 m/min 1,20 m/min

Figure 3. The sequence of the weld seam with the different weld speeds.

(Pipe dimension : 1016x17,7 mm Grade of the material : St 52)

After the choosing the welding current, the program automatically gives the diameter

of the wire, arc voltage, deposition rate, heat input applied to the material and cooling

rate. Also computer always checks the results and compare to the permissible values.

If the results are beyond the limits, immediately warning are sent o the user and also

blocked to the next stages of the program. A view of the program is given in Figure 4.

177
Figure 4. A view of the computer program.

^ MANNESMANN

BORU WELDING PARAMETEItS 1016 X 17.7 mm

Sundan : TS 1997 Material: St 52 Y joint typt

ft; V 'J-' ST

"ji^^ h= U.5 nun

:.A(: I.AC t)c^

20.0- 4 10,0'

A r

Outside weld

DC i.AC 0 10'^

type- of-; cynrent (ni/mm)

insidt! weid oiitade weld

currerrt vuitapc (V)

-1,0

extension (mm)

mciting

rate

diamCler

wire-

;xlcnsjt)Ti (mm)

melting

rate

iveat iaf>m[-

References

- ASM Handbook, "Welding, Brazing and Soldering", Metals Handbook, vol.6, page 1 15-152,

1983

- Lincoln Electric Company, "The Procedure Handbook of Arc Welding", 13* Edition, Sec. 6, 1994.

- G.E.Linnert, "Welding Metallurgy" Fourth edition, vol.1, chapter 7, 1994.

- J.Tusek, "Mathematical Modeling of Melting Rate in Twin-Wire Welding", Journal of

Materials Processing Technology 100, page: 250-256, 1999.

- R.S.Chandel, H.P.Seow, F.L.Cheong, "Effect of Increasing Deposition Rate on the Bead

Geometry of Submerged Arc Welding", Journal of Materials Processing Technology 72, page:

124-128, 1996.

- M.Graf, K.Niederhoff, "Toughness Behavior of the Heat-Affected Zone (HAZ) m Double

Submerged-Arc Welded Large-Diameter Pipe", Mannesmann Rohrenwerke.

- P.A.Peters, H.G.Frackmann, "The Manufacture of Spiral Welded Pipe with

Integrating Quality Assurance" Mannesmann Rohrenwerke

178
METAL INERT GAS (MIG) WELDING PROCESS OPTIMIZATION FOR EXTRUDED

(6063-T52) T-JOINT CONFIGURATION USING OTC/DIAHEN EQUIPMENT

R. Koganti, Zaluzec. M, Velez. J, Karas. C, Joaquin. A, Caliskan. A., and Wang. J.*

ABSTRACT

The prodcution of aluminum intensive vehicles requires the development of optimum

manufacturing processes for the joining and assembling of lightweight architectures. These

manufacturing processes not only have to provide cycle time viability but also need to maintain

or surpass product safety and quality. T-joint configurations are an integral design used on front

end structure structures, crash boxes, etc. used in hybrid aluminum vehicle architectures. The

purpose of this study was to find optimum welding parameters for the welding of 6063-T52

aluminum materials in a T-Node configuration. The welding process factors considered were power input, pulse frequency, gas flow rate, torch angle, and arc intensity. A partial factorial

design of experiment (DOE) was conducted to understand the effect of these factors on joint

strength and weld geometry. All the welded T-Nodes were subjected to tensile, bending and

shear testing as per the experimental matrix. Results showed power input as the most statiscally

significant factor with an adverse effect on joint strength and weld geometry at high settings. In

addition, a correlation between joint shear and tensile load to failure and weld throat was found.

None of the MIG welding process factors showed any statistical significance on bending load to

failure.

KEYWORDS

Metal Inert Gas Welding, Aluminum, Extrusion, Power Input, Pulse Frequency, Torch Angle,

Gas Flow, Penetration, T-Nodes, Tensile Load, Shear Load, Bending, Heat Affected Zone,

Depth of Penetration

Mfg. And Vehicle Design Research and Advanced Engineering Laboratory, Ford Research and

Advanced Engineering, Ford Motor Company, Dearborn, Michigan, 48124.

* DTE Energy, 37849 Interchange Drive, Farmington Hills, Michigan

179
INTRODUCTION

This study was developed to identify the influence of MIG welding process factors on weld

strength and weld geometry on 6063-T52 extruded aluminum in a T-Node joint configuration

(Figure 1). The welding system used in this experiment was an OTC/Daihen CPD-350 welding

system (Figure 2) accompanied by a DR-4000 pulse power supply with a weld controller capable

of handling a superimposed waveform ranging from 0-30Hz by the uses of a dual pulse wave

controller. The ranges for power input (torch speed, voltage, current, wire feed); pulse frequency,

gas flow rate, torch angle, and arc intensity were selected based on screening experiments of

previous DOE's.

Post-weld analysis consisted of mechanical testing, weld geometry evaluation, and

microhardness testing. Fifteen samples of each experimental run were subjected to tensile, shear

and bending tests. One additional T-Node of each experimental run was used for metallographic

evaluation and microhardness testing. Standard sectioning and mounting practices were used to prepare cross sections of the welds. Photomicrographs were obtained from mounted cross section

samples. The same samples were used for weld geometry measurements (depth of penetration,

throat, etc.) and for microhardness characterization. Weld geometry principal factors and

microhardness were then correlated to input parameters and mechanical test results.

EXPERIMENT

The welding equipment process factors selected for this DOE were power input (torch speed,

voltage current, wire feed), pulse frequency, gas flow rate, torch angle, and arc intensity. The

extreme levels of power input, pulse frequency, gas flow, torch angle, and arc intensity are shown in Table-1. Argon gas (@99.9%), commonly used for aluminum MIG welding [1], was

used as the shielding gas. The filler wire selected was a 1.2 mm diameter Al-4047 filler wire

supplied by Alcotech. Other factors maintained constant throughout the experiment were lead

push angle (20°), weld length (75mm), and wire stickout (15mm). A partial factorial DOE was

conducted consisting of eight experimental runs as shown in the experimental matrix in Table 2

(Appendix).

The mechanical testing of the T-nodes was performed on the Tinus Olsen testing machine at The

Automotive Safety Center. Fifteen samples of each experimental run were tested for joint load to

failure under three different configurations: tensile, bending and shear loading. Specific

geometry of the test specimen and the test loading conditions are shown in Figures 3-5. The

crosshead speed was lOmm/min. For statistical analysis purposes, maximum joint load to failure was used.

/

180
Table 1. DOE-Tl Process factors matrix

Factor Low Level High Level Unit

Torch Speed

Corresponding Voltage/Current

635 (25)

21.5/150

1270 (50)

25.3/220

mm(in)/min

Volts/Amperes

Pulse Frequency 10 30 Hz

Gas flow rate 0.71 (25) 1.27 (45) m^/hr (ft'/hr)

Torch Angle 45 50 Degrees

Arc Intensity Normal Soft N/A

DATA ANALYSIS

Joint Load to Failure

The typical failure mode was different for each test configuration (Figures 6-8). Under tensile

loading, the failure occurred at both the welds and the heat affected zone. Under bending load,

the majority of the failure occurred in the base alloy at the heat-affected zone while leaving the

welds primarily intact after the test. Under shear load conditions, the failure occurred mostly in

the weldment and at the interface of the weld and the heat-affected zone. Substrate/heat affected zone failure tended to give relative lower peak load as compared to weld/weld interface failure.

Tensile, bending and shear data were used in the statistical analysis to evaluate factors influence on load to failure. The statistical significance of the welding factors was verified by an analysis

of variance (ANOVA). The normal probability plots with a 90% confidence and main effect

plots are shown in Figures 9-15.

The normal distribution plots showed that:

1. The interaction of power, torch angle, and arc intensity have 90% confidence statistical significance on joint load to failure under tensile loading.

2. No process parameters were statistically significant on joint load to failure under bending

loading.

3. Power was the only factor with 90% confidence statistical significant on joint load to failure under shear loading.

The effects of the above mentioned welding factors on joint load to failure under tensile, bending

and shear loading are shown in Figures 12-15. Power input had an adverse effect on joint load to

failure under both tensile and shear loadings. The interaction of power input and torch angle

suggested that the negative effect of high power input settings was even more pronounced at low

181
torch angle settings (Figure 15). Additionally, arc intensity showed a positive effect on joint load

to failure under tensile loading.

WELD GEOMETRY

Transverse Weld Shape

One specimen of each experimental run was cross-sectioned into six segments. Two of those six

segments consisted of cross-sections of the butt weld. The remaining four sections consisted of

the cross-sections of the two transverse welds at one inch after the starting and one inch before

the ending points of the weld (Figure 16).

Throat Length

The weld throat is defined as the area formed by the product of the weld length and the shorter

distance from the intersection of the welded plates to (1) the straight line connecting the ends of

the two legs formed or (2) to the weld bead surface, whichever is less [1]. Based on statistical data, power input was the only factor among the five considered that contributed to throat length.

Power input at high settings tended to reduce the throat length by as much as 20% (3.36 to 2.72mm) - (Figures 17-18). Although throat area does not consider weld beads as part of its area, visual examination of the exterior of the specimens revealed concave beads for low arc intensity

settings versus convex bead for high settings of the arc intensity.

Although in coupon configuration a direct correlation between throat length and joint load to

failure was not attained, for T-Nodes, throat length was directly related to joint load to failure.

Correlations of the averages of each experimental run and their corresponding joint loads to failure values under tensile and shear loading respectively were found with R-Square values of

51% and 82% (Figures 19-20).

Top and Bottom Penetration

Image Pro Plus software was used to measure the depth of weld penetration into the top and

bottom aluminum substrates. The top and bottom penetration of the welds were measured by

projecting perpendicular lines from the weld top and bottom legs respectively to the outermost

point of penetration as shown in (Figure 21). Although power input, gas flow and arc intensity

seemed to affect top penetration of the T-Nodes, they showed no statistical significance (Figure

22). On the other hand, bottom penetration increased as torch angle increased (Figures 23-24).

Although top and bottom penetration values described how susceptible the material was to melt

under different input welding conditions, the shape of the resulting weld cannot be visualized by

only knowing the magnitude of these values. In order to further describe the weld shape, top and

bottom penetrations at the two specimens point of intersection was measured and correlated as a

ratio to top and bottom maximum penetrations. The top ratio was referred to the ratio of the

penetration at the point of intersection and the maximum penetration in the direction of the top

(vertical) specimen, and the bottom ratio was referred to the ratio of the penetration at the point

of intersection and the maximum penetration in the direction of the bottom (horizontal)

specimen. Although power input did not show any input on top coupon penetration, it

182
contributed to a higher top ratio meaning that the weld shape maximum top penetration value

tended to occur near the point of intersection of the two specimens (Figures 25-26). Conversely,

bottom ratio decreased as power input increased (Figures 27-28).

T-Node Side and Measurement Position Relative to the Starting and Ending Points of the

Weld

T-Node side and measurement position relative to the starting and ending points of the weld

showed significance on the weld geometry. Although T-Node side position was not significant on weld throat, top section penetration was more pronounced at the first weld in the welding

sequence (Figure 29). Also, measurements closer to the ending point of the welds showed more

top penetration than those close to the starting points. On the other hand, bottom penetration

showed a tendency to increase from starting to ending points and from first to second weld in the

welding sequence (Figure 30).

Side and position showed minor effects on top ratio; however, T-Node side of the weld (first or second side of the welding sequence) affected the bottom penetration ratio in the way of an

interaction with power input (Figure 31). For high power settings the ratio increased with side

from first side to second side of the welding sequence, while for low power settings the effect was opposite, the ratio decreased from first to second weld.

HEAT AFFECTED ZONE

In order to understand the influence of weld factors on the heat affected zone (HAZ), one sample

of each run was cross-sectioned for microhardness testing. A Leco Micro-hardness Tester MHT

series 200 system was used to measure Vicker's hardness values. The measurements were taken

at 1 .0mm increments across the weld zone and base material with a 0.5kg load applied using a

five second dwell time.

The graphs of the hardness data showed a hardness decrease from 60-65HV for the base material

to 40-45HV at the HAZ as shown in (Figure 32). This pattern was present in every weld

examined; however, the length of the HAZ was higher and the hardness values lower at low

power settings, settings for which, in actual fact, weld strength resulted to be higher (Figure 33).

This loss in hardness across a HAZ is typical in most heat treated materials subject to fusion

welding process. The data obtained from the aluminum materials used in this study is typical of

that obtained in other heat treatable aluminum alloys subjected to MIG welding.

183
SUMMARY AND CONCLUSIONS

This study was developed to identify the influence of MIG welding process factors on weld strength and weld geometry on 6063-T52 extruded aluminum in T-Node

configuration.

The welding equipment process factors selected were power input (torch speed, voltage

current, wire feed), pulse frequency, gas flow rate, torch angle, and arc intensity.

Post-weld analysis consisted of mechanical testing (tensile, bending, and shear loading),

weld geometry evaluation, and microhardness testing.

Power input had an adverse effect on joint load to failure under both tensile and shear

loadings. The negative effect of high power input settings was even more pronounced at low torch angle settings. On the other hand, arc intensity showed a positive effect on joint

load to failure under tensile loading.

Power input at higher settings tended to reduce the throat length by as much as 20% (3.36

to 2.72mm).

Correlations of the throat averages of each experimental run and their corresponding joint

loads to failure under tensile and shear loading respectively with R-Square values of 51%

and 82% were found.

Torch angle at higher settings showed increase in bottom penetration.

Power input at higher settings set the outermost top penetration line close to the specimen

horizontal leg while setting the deepest bottom penetration away from the vertical leg

formed by the specimen intersection.

Top section penetration was more pronounced at the first weld in the welding sequence

and measurements closer to the ending point of the welds showed more top penetration

than those close to the starting points. On the other hand, bottom penetration showed a tendency to increase from starting to ending points and from first to second weld in the

welding sequence.

The graphs of the hardness data showed a hardness decrease from 60-65HV for the base

material to 40-45HV at the HAZ.

The length of the HAZ was higher and the hardness values lower at low power settings,

for which weld strength resulted higher.

)

184
REFERENCES

1. Juvinall, Robert; Marshek, Kurt. Fundamentals of Machine Component Design. USA:

John Wiley & Sons, Inc., 1991, Second Edition, pp. 415-416.

2. Riley, William; Sturges, Leroy; Morris, Don. Mechanics of Materials . USA: John Wiley

& Sons, Inc., 1999, Fifth Edition, pp. 221-223.

3. Beer, Ferdinand; Johnston, Russell. Mechanics of Materials. USA: McGraw-Hill-Inc,

1992, Second Edition, pp. 233-235.

4. Douglas C. Montgomery. Design and Analysis of Experiments. USA: John Wiely &

Sons, Inc.

5. Ashby, Michael; Jones, David. Engineering Materials 2 - An Introduction to Microstructures, Processing and Design. U.K.: Pergamon Press, 1986, First Edition, pp.

85, 126-127.

6. OTC/Daihen Doc. CPDP-599, p. 19.

185
APPENDIX
Er4 ==r Two holes (opposite each other) need to be

drilled on the sample to mount the top plate

A before each test. The clamp has drill guide

J bushings. The top clamp will be bolted to the

sample then located to the platen and fastened.

A solid mandrel inside — the extrusion which stops

at the inner edge of the

collar.

4 inch wide collar to prevent

bending during shear test.

Figure 5. Shear Test Configuration

187
Figure 6. Failure mode of tensile test specimen
Normal Probability Plot of the Standardized Effects

(response is Tensile, Alpha = .10)

A: Power

B: Freq

C: Gas

D: Torch

E: Arc

-6 -5 -4 -3-2-10 1 2

Standardized Effect

Figure 9. Normal probability plot for tensile load

Normal Probability Plot of the Standardized Effects

(response is Bending, Alpha = .10)

2

o

o

CO

raEk-o

A: Power

B: Freq

0: Gas

D: Torch

E: Arc

Standardized Effect

Figure 10. Normal probability plot for bending load

189
Normal Probability Plot of the Standardized Effects

(response is Shear lo, Alpha = ,10)

Standardized Effect

A; Power

B: Freq

C: Gas

D: Torch

E: Arc

Figure 1 1 . Normal probability plot for shear load

11700 -

11400

^ 11100 -

10800

10500 -

Power

Figure 12. Main effects plot (data means) for tensile load (kN)

/

190
Povier Freq Gas Torch Arc

Figure 13. Main effects plot (data means) for bending load

Power Freq Gas Torch Arc

Figure 14. Main effects plot (data means) for shear load

191
4 Welds (A,B,C,D)

6 Sections

Aj- 1 inch from weld A

start point

A2- 1 inch from weld A end

point

a Bj- 1 inch from weld B

start point

B2- 1 inch from weld B end

point

C - Middle of weld C

D- Middle of weld D

T-Node

Welds

Weld

Sectioning

(Top View)

Figure 1 6. Cutting scheme

Normal Probability Plot of the Standardized Effects

(response is Throat, Alpha = .05)

A: Power

B; Freq

C: Gas

D: Torch

E: Arc

-2.5 Standardized Effect

Figure 17. Normal Probability Plot for Throat

/

192
3.28

3.16

2 3.04

2.92 -

2.80

Power Freq Gas Torch Arc

Figure 18. Main Effects Plot for Weld Throat (mm)

Throat to Shear Load Correlation

R2 = 0.8246 60

2-a

reo

0)

55

50

45

40

35

30

2.50 2.70 2.90 3.10 3.30 3.50 3.70 Throat (mm)

Figure 19. Throat to Shear Load Correlation

193
56

-54 ^ 52 ^ 50

S 48 « 46

'I 44

|2 42

40

Throat to Tensile Load Correlation

= 0.5145

2.50 3.00

Throat (mm)

3.50

Figure 20. Throat to Tensile Load Correlation

Normal Probability Plot of the Standardized Effects

(response is Top Pene, Alpha = .10)

A: Power

B: Freq

C: Gas

D: Torch

E: Arc

Standardized Effect

Figure 21. Normal Probability Plot for Top Penetration

194
2.38 -

226 -

OD 2.14 - Q

Q.a. ^ 2.02 -

1.90

Power Freq Gas Torch Arc

Figure 22. Main Effects Plot (data means) for Top Penetration

Normal Probability Plot of the Standardized Effects

(response is Bottom P, Alpha = .05)

A; Power

B: Freq

C: Gas

D: Torch

E: Arc

0 1 Standardized Effect

Figure 23. Normal Probability Plot for Bottom Penetration

195
2.2

2.0 -

Q- 1.8 -

1,6 -

1.4

Power Freq Gas Torch Arc

Figure 24. Main Effects Plot (data means) for Bottom Penetration (mm)

Normal Probability Plot of the Standardized Effects

(response is Top Rati, Alpha = .05)

A; Power

B: Freq

C: Gas

D: Torch

E: Arc

Standardized Effect

Figure 25. Normal Probability Plot for Top Ratio

196
0.88 -

0.83 -

^ 0.78

0.73 -

0,68 -

Power Freq Torch

Figure 26. Main Effects Plot (data means) for Top Ratio

Normal Probability Plot of the Standardized Effects

(response is Bottom R, Alpha = .10)

1 0 1 Standardized Effect

A Power

B: Freq

C: Gas

D: Torch

E: Arc

Figure 27. Normal Probability Plot for Bottom Ratio

197
Main Effects Plot (data means) for Bottom Ratio

Power Freq Gas Torch Arc

Figure 28. Main Effects Plot for Bottom Ratio

198
Main Effects Plot (data means) for Top Penetrat

2"'*Side f'Side End Point Start Point

Side Position

Figure 29. Side and Position Effects on Top Penetration

Main Effects Plot (data means) for Bottom Penet

2"''Side reside End Point Start Point

Side Position

Figure 30. Side and Position Effects on Bottom Penetration

199
Interaction Plot (data means) for Bottom Ratio

CO

CD0.95 -

0.85 -

0.75 -

0.65 -

2"^^ Side reside

Power

• -1

1

Side

Figure 31. Interaction Plot for Bottom Penetration Ratio

200
Low Power Settings T-Node Hardness Profile

1-1-1B2 5-1-1B 2-1-1B2 X 6-1-182 —2 per. Mov. Avg. (1-1-1B2) —2 per. Mov. Avg. (5-1-1B)

•"•"•2 per. Mov. Avg. (6-1-162) 2 per. Mov. Avg. (2-1 -162)

Reference Point

Figure 32. Hardness Profile for Low Power Input Welds

High Power to Low Power Settings T-Node Hardness Profile

70

40

30

20

10

High Power Low Power ~2 per. Mov. Avg. (High Power) ^~2 per. Mov. Avg. (Low Power)

10 15 20 25 Reference Point30 35 40 45

Figure 33. Low Power Input Vs. High Power Input Hardness Profiles

201
Factor Low Level High Level Unit

Torch Speed

Corresponding Voltage/Current

635 (25)

21.5/1 jU

1270 (50)

10.51Ll\j

mm(in)/min

Volts/Amperes

PiilcF* Prpnii*=*nr*\/ 10 30 Hz

Gas flow rate 0.71 (25) 1.27 (45) m'/hr (ft'/hr)

Torch Angle 45 50 Degrees

Arc Intensity Normal Soft N/A

Table 1. DOE-Tl Process factors matrix

Run Power Input Pulse

Frequency

Gas Flow

Rate

Torch Angle Arc Intensity

1 -1 -1 -1

2 -1 -1 1

3 1 1 1

4 1 1 -1

5 -1 1 -1

6 -1 1 1

7 1 -1 1

8 1 -1 -1

Table 2. Experimental matrix

/

202
Technical Publications

Periodical

Journal of Research of the National Institute of Standards and Technology—Reports NIST research and development in metrology and related fields of physical science, engineering, applied mathematics,

statistics, biotechnology, and information technology. Papers cover a broad range of subjects, with major

emphasis on measurement methodology and the basic technology underlying standardization. Also included

from time to time are survey articles on topics closely related to the Institute's technical and scientific programs. Issued six times a year.

Nonperiodicals

Monographs—Major contributions to the technical literature on various subjects related to the

Institute's scientific and technical activities. Handbooks—Recommended codes of engineering and industrial practice (including safety codes) devel- oped in cooperation with interested industries, professional organizations, and regulatory bodies. Special Publications—Include proceedings of conferences sponsored by NIST, NIST annual reports, and

other special publications appropriate to this grouping such as wall charts, pocket cards, and bibliographies.

National Standard Reference Data Series—Provides quantitative data on the physical and chemical

properties of materials, compiled from the world's literature and critically evaluated. Developed under a worldwide program coordinated by NIST under the authority of the National Standard Data Act (Public

Law 90-396). NOTE: The Journal of Physical and Chemical Reference Data (JPCRD) is published

bimonthly for NIST by the American Institute of Physics (AIP). Subscription orders and renewals are

available from AIP, PO. Box 503284, St. Louis, MO 63150-3284.

Building Science Series—Disseminates technical information developed at the Institute on building

materials, components, systems, and whole structures. The series presents research results, test methods, and

performance criteria related to the structural and environmental functions and the durability and safety

characteristics of building elements and systems.

Technical Notes—Studies or reports which are complete in themselves but restrictive in their treatment of

a subject. Analogous to monographs but not so comprehensive in scope or definitive in treatment of the subject area. Often serve as a vehicle for final reports of work performed at NIST under the sponsorship of

other government agencies.

Voluntary Product Standards—Developed under procedures published by the Department of Commerce

in Part 10, Title 15, of the Code of Federal Regulations. The standards establish nationally recognized

requirements for products, and provide all concerned interests with a basis for common understanding of

the characteristics of the products. NIST administers this program in support of the efforts of private-sector

standardizing organizations.

Order the following NIST publications—FIPS and NISTIRs—from the National Technical Information

Service, Springfield, VA 22161.

Federal Information Processing Standards Publications (FIPS PUB)—Publications in this series

collectively constitute the Federal Information Processing Standards Register. The Register serves as the

official source of information in the Federal Government regarding standards issued by NIST pursuant to the Federal Property and Administrative Services Act of 1949 as amended. Public Law 89-306 (79 Stat. 1127), and as implemented by Executive Order 11717 (38 FR 12315, dated May 11, 1973) and Part 6 of

Title 15 CFR (Code of Federal Regulations).

NIST Interagency or Internal Reports (NISTIR)—The series includes interim or final reports on work

performed by NIST for outside sponsors (both government and nongovernment). In general, initial distribution is handled by the sponsor; public distribution is handled by sales through the National Technical

Information Service, Springfield, VA 22161, in hard copy, electronic media, or microfiche form. NISTIR's may also report results of NIST projects of transitory or limited interest, including those that will be

published subsequently in more comprehensive form.
U.S. Department of Commerce

National Institute of Standards and Technology

325 Broadway

Boulder, Colorado 80305-3337

Official Business

Penalty for Private Use, $300

/