Thermal Spray Coatings Workshop:

Sensors, Modeling and Control Strategies

Summary of a Workshop Held at

National Institute of Standards and Technology

NISTI R 646 0

Frank S. Biancaniello

Stephen D. Ridder

U.S. DEPARTMENT OF COMMERCE

Technology Administration

Metallurgy Division

Materials Science and Engineering Laboratory

National Institute of Standards

and Technology

Gaithersburg, MD 20899

U.S. DEPARTMENT OF COMMERCE

Technology Administration

National Institute of Standards and

Technology

Thermal Spray Coatings Workshop:

Sensors, Modeling and Control Strategies

Summary of a Workshop Held at

National Institute of Standards and Technology

NISTI R 646 0

Frank S. Biancaniello

Stephen D. Ridder

U.S. DEPARTMENT OF COMMERCE

Technology Administration

Metallurgy Division

Materials Science and Engineering Laboratory

National Institute of Standards

and Technology

Gaithersburg, MD 20899

November 1998

U.S. DEPARTMENT OF COMMERCE

William M. Daley, Secretary

TECHNOLOGY ADMINISTRATION

Gary R. Bachula, Acting Under Secretary

for Technology

NATIONAL INSTITUTE OF STANDARDS

AND TECHNOLOGY

Raymond G. Kammer, Director

Table of Contents Page i

TABLE OF CONTENTS

TABLE OF CONTENTS .............................................................. i

DISCLAIMER ..................................................................... ii

WORKSHOP SUMMARY ............................................................ 1

Purpose ............................................................. 1

WORKSHOP AGENDA .............................................................. 2

PRESENTATIONS .................................................................. 3

Introductory Presentations ............................................... 3

Workshop Presentations ................................................. 3

DISCUSSION ...................................................................... 4

CONCLUSIONS .................................................................... 5

ATTENDANCE LIST ................................................................ 7

Industry ............................................................. 7

Academia ............................................................ 9

National Labs (non NIST) ............................................... 10

NIST .............................................................. 11

PRESENTATION SLIDES ........................................................... 13

NIST Ceramic Coatings Program S. J. Dapkunas (NIST) ............... 13

Process Diagnostics S. D. Ridder (NIST) ................ 23

Spectroscopy Measurements D. W. Bonnell (NIST) ............... 29

Thermal Imaging J. E. Craig (Stratonics) ............... 39

Numerical Simulation of Underexpanded Jets A. Johnson (NIST) ................. 47

Process Control S. A. Osella (ICT) ................. 53

Sensors and Controls for Thermal Spray: Is there a need? C. C. Berndt (SUNY Stony Brook) ........ 59

Currently used Sensors C. Moreau (NRC-CNRC) ............. 65

Measurement of DC Plasma Arc Fluctuations J. Heberlein (U. of Minnesota) .......... 83

Enthalpy Probe M. Boulos (U. of Sherbrooke) ........... 87

Impact and Solidification of Molten Nickel Droplets W. H. Hofmeister (Vanderbilt U.) ........ 99

NIST THERMAL SPRAY RESEARCH PROGRAM .......................................109
Page ii Disclaimer

DISCLAIMER

This report is intended as a record of the presentations and discussions which took place at a NIST

Metallurgy Division sponsored workshop. The opinions, conclusions, or recommendations that are expressed

herein are those of the organizers or individual presenters and do not necessarily reflect the views of NIST.

All references to commercial equipment in this report are for identification purposes only and in no way

constitute any endorsement or evaluation of the relative merits of such equipment by NIST.

RETURN TO

TABLE OF CONTENTS
Workshop Summary Page 1

WORKSHOP SUMMARY

Purpose

The NIST Metallurgy Division has initiated a research program to investigate coatings produced by

thermal spray (TS) techniques. The focus of this research is the development of measurement tools that will

aid in the understanding and/or control of the plasma spray process. This process uses plasma jets (generated

by either DC or AC arcs) to melt or soften coating feed-stocks and then propel this material onto various

substrates. The geometry and operating parameters of the plasma jet hardware, or “gun”, depend on the

intended function of the resulting TS coated part. Currently TS coatings are produced by skilled technicians,

however, it is now being adapted for automatic control using robotics. Intelligent Processing incorporating

expert systems will probably be employed in most advanced systems. This move to robotics is not only to

reduce costs, but to improve the reliability of spray coatings and thus enable the use of coatings in high

volume applications such as automotive components and property critical devices such as the proposed highefficiency gas turbines.

Recently a number of advances have been made in new measurement systems, sensors, and modeling

techniques that can lead to improved design and control of thermal spray processes. The objectives of this

workshop were to present descriptions of some of these advances and their industrial applications,

demonstrate some of the systems currently available or under development at NIST, and provide a forum to

allow discussion of the current industrial measurement needs for thermal spray coatings.

RETURN TO

TABLE OF CONTENTS
Page 2 Workshop Agenda

WORKSHOP AGENDA

9:00 Introduction ................................................ J. R. Manning (NIST)

9:05 Overview of NIST Mission .............................. C. A. Handwerker (NIST)

9:10 ATP Programs .......................................... R. J. Schaefer (NIST)

9:20 MSEL Programs ......................................... S. J. Dapkunas (NIST)

9:30 NIST Prior Expertise in Intelligent Processing ......................... S. D. Ridder (NIST)

9:40 NIST Previous Discussions with Thermal Spray Industry .............. F. S. Biancaniello (NIST)

9:45 NIST Current Status and Future Plans (intro with SBIR activities) .......... S. D. Ridder (NIST)

Diagnostics (high-speed video, cinema, holography, spectroscopy, etc.)

9:50 High-Speed Video .................................. S. D. Ridder (NIST)

10:00 Spectroscopy Measurements .......................... D. W. Bonnell (NIST)

Sensors (thermal imaging, velocity, size, etc.)

10:15 Thermal Imaging ................................... J. E. Craig (Stratonics)

Modeling (CFD, schlieren, etc.)

10:30 Numerical Simulation of Underexpanded Jets ............... A. Johnson (NIST)

Expert Systems (parameterization, truth tables, etc.)

10:45 Process Control ...................................... S. A. Osella (ICT)

11:00 Break

11:15 Demonstration of NIST Spray Facility

(Industrial Building, Room B122) ................................. SDR and FSB (NIST)

Importance of Sensors and Diagnostics in controlling Industrial Thermal Spray Processes

11:45 Overview .................................... C. C. Berndt (SUNY Stony Brook)

12:00 Currently used Sensors ................................. C. Moreau (NRC-CNRC)

12:15 Modeling of Thermal Spray .......................... J. Heberlein (U. of Minnesota)

12:30 Enthalpy Probe .................................... M. Boulos (U. of Sherbrooke)

12:45 Imaging for Rapid Prototyping ..................... W. H. Hofmeister (Vanderbilt U.)

1:00 Lunch

2:00 Industrial Needs as viewed by equipment manufacturers (intro) ........... D. Crawmer (Praxair)

2:05 Discussion and Suggestions concerning industrial needs ......................... participants

3:30 adjourn

RETURN TO

TABLE OF CONTENTS
Presentations Page 3

PRESENTATIONS

Introductory Presentations

The program started with an introductory welcome by J. R. Manning, group leader for Metallurgical

Processing in the NIST Metallurgy Division. C. A. Handwerker, Chief of NIST Metallurgy Division, then

gave an overview of the NIST mission. This was followed by slide presentations by R. J. Schaefer outlining

the NIST Advanced Technology Program (ATP) and S. J. Dapkunas, of the NIST Ceramics Division, on

the current MSEL Ceramics Coating Program.

Workshop Presentations

Following the introductory slides were presentations of NIST work. S. D. Ridder of the NIST Metallurgy

Division presented slides to provide an overview of the NIST Metallurgy Division’s previous research in

intelligent processing, imaging diagnostics, and advanced sensors. F. S. Biancaniello, also of the NIST

Metallurgy Division presented slides outlining the recent discussions between NIST researchers and the

thermal spray industry. These presentations were intended to show the attendees the current status of

research in metals processing at NIST and how these recent activities on powder production are relevant to

new programs being organized on thermal spray processing. 

New research results on non-contact spectrometer-based temperature measurements of particles in flight

within a plasma jet were presented by D. Bonnell. He discussed correction of the measured spectrum by

subtracting emission from the plume. The technique appears to be working well for temperature

measurements but showed the puzzling result of temperature increasing with distance of travel. The next

speaker, J. Craig (Stratonics), showed results from a new imaging pyrometer system based on 2 images,

typically at 950 nm and 700 nm, in which the pixels of the two images match precisely. He concluded that

temperature decreases with distance of travel. Work on calibration of this new sensor continues through

funding provided by a NIST Small Business Innovative Research (SBIR) award.

A. Johnson (NIST Fluid Flow Group) presented slides on techniques for Computational Fluid Dynamics

(CFD) modeling of underexpanded gas jets as they apply to a thermal spray plasma jet. This NIST effort on

CFD modeling of compressible fluid jets was initiated several years ago to provide a software tool that could

be used by engineers to help optimize the design of gas atomizers used for the production of metal powder.

This CFD tool has been applied to the design of commercial gas atomizers with significant improvements in

production efficiencies. It is likely that similar studies of thermal spray gas jet assemblies could lead to more

efficient and/or more controllable thermal spray gun designs.

S. A. Osella (Intelligent Computing Technologies, Inc.) presented slides on a novel technique to develop

and implement expert system driven process controllers. The software tool shown in these slides was

developed by ICT using funding provided by a NIST SBIR award. The need for this tool was realized during

the development of an expert system controller for the NIST gas atomizer. This software can help organize

and validate complex process controllers as used for a thermal spray system.

The attendees were brought to the NIST thermal spray facility for a demonstration of the Stratonics sensor

measuring in-flight particle temperature and velocity of plasma sprayed zirconia powder. Following this

laboratory demonstration several invited speakers presented their views on various aspects of the thermal

spray process.

C. Berndt (SUNY Stony Brook) pointed out the need for sensors to be complementary to modeling, and

to be related to real sprayers. An abundance of measurements have already been made but standards are

needed. One important subject is thermal spray processing of nanoparticles, on which a conference will be

held next year. An economic analysis was shown that outlined the current and future projection of the

thermal spray market.
Page 4 Discussion

C. Moreau (NRC-CNRC) pointed out the need to understand three zones: zone 1, where heat is

generated, and voltage fluctuations depending on the surface of the electrode are generated: zone 2, in which

the particles are heated and accelerated: and zone 3, in which the coating builds up.

J. Heberlein (U. of Minnesota) described work to characterize the environment that heats the powder.

This requires small time-scale resolution monitoring of sound and voltage fluctuations. Using a 100 μs to

1 ms time-scale resolution could provide useful data concerning the arc characteristics related to cathode and

anode erosion that ultimately affect the output flux of torch power.

M. Boulos (U. of Sherbrooke) pointed out that cold particles entrained in the plasma will not be detected

by emission techniques, but they can ruin a coating. He described an enthalpy probe which is intrusive but

yields good data.

W. Hofmeister (Vanderbilt U.) described NASA-sponsored work on velocity of solidification of

undercooled melts, droplet splats with G. Trapaga of MIT, and application of high-speed thermal imaging to

the LENS process (a net shape processing technique that consolidates powder with a high-power laser).

DISCUSSION

The discussion was lead by Darryl Crawmer of Praxair. He started the discussion by presenting his own

views, as an equipment manufacturer, of industrial needs. The first area he mentioned was SRM’s. We

already have one for particle size, and Phase II of this is coming out. They need one for X-ray diffraction to

show the crystallography of yittria-stabilized zirconia.

The major point of his discussion, however, was the need to bring the thermal spray process under control:

at present it is not. If the process were well controlled, post-processing quality control would be a moot

point. There is a long way to go before plasma spray can be a 6F process. Control of the spray process has

moved from the old location at the back of the gun (i.e., controlling the applied voltage and current) to

control of the energy at the front of the gun (10 years old but still not widely accepted). Still some distance

off, in development and acceptance, is control based on particle velocities, temperatures, and trajectories, or

characterization of the deposit itself.

Advanced control technology needs to be embedded in such a way that it can be used by real operators, not

skilled technicians, and so that it will be available to users beyond the high-tech companies such as Praxair,

GE, or Howmet.

In the discussion, it was asked what will inspire people to invest in advanced technology. The response was

the opening of new markets which will become available with higher reliability. Most plasma spray is now at

1F (68%), while high-end shops try to be at 2F(95%). This is not acceptable when failure of a plasma spray

process could shut down a production facility. Another example is coating of a paper roll, which can involve

spraying for 24 hours at a time, so there is a need to know if the deposit is uniform.

The drive for these sensors is working from the top down: Praxair, Tafa, GE, P&W, and vendors are

developing tools. Interest from the automobile industry is not huge at present, but bore coating could become

a driver. Big customers are not currently putting pressure on small suppliers to do more.

Currently, in a $10 million shop, $3 million is used to purchase powder, easily half of which is discarded

due to various process inefficiencies. The cost of wasted powder (as well as any other costs resulting from

poor process control) is simply passed on to the customer, a situation which will change only as the

competition increases.

Users want a “magic wand” to stabilize the process - better predictability, reliability, etc. They need

better process control, substrate control, and powder control, all parameters that contribute to control

of the coating structure. Current practice is to qualify the spray booth at the start of each day, which

is not reliable if the gun deteriorates too fast. Current practice by many is reputed to be “if it sticks,

ship it,” or application of the “hammer test.” A sensor for residual stresses would be highly desirable.

Powder supply has huge day-to-day and lot-to-lot variability, and most customers will not pay extra for
Conclusions Page 5

more uniform powder. Customers can specify powder characteristics, with GE, Pratt, and RR driving this but

the sprayers will pay extra for the powder only if the specifications are tightened. Results can change with

time as a result of wear of the electrodes (anode and cathode). Sensors are desired which are fast, cheap,

easy, and non-intrusive.

Sensors, in order of importance, are:

1) Temperature

2) Velocity

3) Trajectory

4) .... (others)

5) Residual Stress

Temperature, velocity, and trajectory sensors are here but implementation is the problem. A pulsed laser

visualization system costs $80k to $100k. A few sales might be made of a $50k system, to high-end users, a

system for $10k to $20k would sell to every shop. It may be necessary to be more selective in what you need,

to cut down on the price. 

Benchmarking could be done at NIST to relate product to input and process conditions - a typical NIST

activity.

An ASM subcommittee is trying to standardize metallographic techniques to prepare coatings for

examination, so that coatings can be more readily compared to each other. The first standard procedures

should address zirconia powders and coatings followed by similar procedures for WC. We need standardized

powder and standardized evaluation of coatings.

Education, new technologies, and patience are needed.

CONCLUSIONS

Thermal spray technology could attain much more widespread use if one could attain higher levels of

reliability, predictability, repeatability, etc., the same characteristics which were identified as needs for

numerous surface engineering technologies in the recent ATP-sponsored workshop. High volume markets

such as the transportation and consumer electronics industries are reluctant to take advantage of the potential

cost savings derived from using thermal spray to replace other processes until they are convinced that the

reliability issues are addressed. This generally means that the coatings must be consistent from one

production run to the next using feedstock from different sources. There appeared to be a consensus that

these characteristics could be achieved by control based on the temperature, velocity, and trajectory of the

particulate materials. This would compensate for variability due to differences in powder feed stock and

erosion of the plasma gun electrodes. Characterization of gun voltage in the temporal domain with a 100 μs

to 1 ms time-scale should be investigated as a means to quantify erosion of the electrodes.

Currently available sensor/control systems (pulsed laser illuminated) are too expensive ($80k to $100k) to

be attractive. At $50k, a few systems might be sold to high technology users, and at $10k to $20k the

systems would probably become universal. There is some concern among those producing sensors as to the

current and future market for the sale of spray equipment. This has a direct effect on the potential market for

sensor and control systems. If this total spray processing equipment market is not sufficiently large, then

sensor and control system sales will not reach the volume required to realize price reductions. One likely

development that will provide increased markets for sensor and control systems is that many of the sensors

needed for thermal spray equipment will find use in other processing tools such as atomization, spray

forming, rapid prototyping, and welding.
Page 6 Conclusions

Another technical challenge that needs to be met before substantial improvements can be made to coating

reliability is the development of better coating and substrate quality tests. Standard test methods of coating

performance such as density, hardness, wear, adhesion, roughness, thermal conductivity, etc. have been

developed but most are not well accepted and for most products each company has their own specialized test

procedures that they rely on for process quality control. Development of on-line process control sensors that

measure coating and substrate properties in real-time will likely lag behind the development and acceptance

of off-line standard test methods. A short list of some of the more important test methods and process

control sensors existing or needed that were mentioned follows:

Coating quality standard test methods needs

1) metallographic specimen preparation methods

2) measurement of crystal phase content

3) substrate surface preparation and surface roughness measurements

4) coating performance or quality measurements (roughness, density, thermal properties, adhesion, etc.)

Reliable, inexpensive rugged sensor needs

1) particle temperature, velocity (trajectory)

2) substrate roughness

3) coating properties (density, thermal properties, etc.)

4) anode wear monitor

As techniques for measuring coating and substrate properties are improved the ultimate process potentials

will be better known. These measurements will also provide information concerning what sensor and control

technologies are needed to realize the higher quality coatings. Further work is needed to assure potential

users that the sensors provide valid measurements and that the control systems can use this sensor

information to produce reliable, predictable, and repeatable thermal spray coatings. However, even if higher

quality coatings are possible, thermal sprayers are unlikely to be interested in new sensor/control systems that

will produce these better coatings until their customers start demanding higher quality.
Workshop Attendance Page 7

ATTENDANCE LIST

Industry

Vladimir Belashchenko Daryl Crawmer

TAFA Inc. Praxair Thermal Spray Systems

146 Pembroke Rd. N 670 Communication Drive

Concord, NH 03301 Appleton, WI 54915

603-223-2188

603-225-4342 (fax)

vlad@tafa.com

Jacques Blain

TECNAR Automation Ltée

3502 First Street

St. Hubert (Quebec)

Canada J3Y 8Y5

450-443-5335

450-443-4880 (fax)

jblain@tecnar-automation.com

Rick Burmeister

Measurement & Control Technologies

810 Greenleaf Ave.

Charlotte, NC 28202

704-334-5878

704-334-1539 (fax)

rburmeister@compuserve.com

J. J. (Sean) Conway

Crucible Compaction Metals

1001 Robb Hill Road

Oakdale, PA 15071

toll free-888-923-2670

412-923-2670

412-788-4240 (fax)

conway@cruciblecompaction.com

James E. Craig

Stratonics, Inc.

23151 Verdugo Drive, Suite 114

Laguna Hills, CA 92653-1340

949-461-7060

949-461-7069 (fax)

info@stratonics.com

920-997-6167

920-734-2160 (fax)

dcrawme@appl.psti.praxair.com

Brian A. Hann

Crucible Compaction Metals

1001 Robb Hill Road

Oakdale, PA 15071

toll free-888-923-2670

412-923-2670

412-788-4240 (fax)

hann@cruciblecompaction.com

Y. C. Lau

GE CRD

1 Research Circle

Niskayuna, NY 12309

518-387-6017

518-387-7495 (fax)

Lau@crd.ge.com

David Y. Lee

Stratonics, Inc.

23151 Verdugo Drive, Suite 114

Laguna Hills, CA 92653-1340

949-461-7060

949-461-7069 (fax)

Timothy McKechnie

Plasma Processes

4914 D Moores Mill Rd.

Huntsville, AL 35811

258-851-7653

258-859-4134 (fax)

Tim.McK@plasmapros.com

Stephen A. Osella

ICT, Inc.

6309 John Chisum Ln.

Austin, TX 78749-1839

512-301-2444

intcomtec@aol.com
Page 8 Workshop Attendance

Tsung-Yu Pan

Ford Research Laboratory

20000 Rotunda Drive

MD 3135, SRL, P.O. Box 2053

Dearborn, MI 48121-2053

313-322-6845

313-323-1129 (fax)

tpan@ford.com

Ron Parker

Stratonics, Inc.

P.O. Box 206

Geneseo, NY 14454

716-346-2447

parker_ron@compuserve.com

Jack Ramsey

Trans-Tech

5520 Adamstown Road

Adamstown, MD 21710

301-874-6453

301-695-7065 (fax)

P.O. Box 69

jramsey@alphaind.com

Jim Ruud

GE Corporate R&D

K-1 MB165

1 Research Circle

Niskayuna, NY 12309

518-387-7052

518-387-5576

rwd@crd.ge.com

Gregory Wuest

Sulzer Metco

Westbury, NY

516-338-2217

516-338-2488 (fax)

gregory.wuest@sulzer.ch
Workshop Attendance Page 9

Academia

Christopher C. Berndt

SUNY at Stony Brook

306 Old Engineering

Stony Brook, NY 11794-2275

516-6328507

516-632-8525 (fax)

cberndt@notes.cc.sunysb.edu

Maher Boulos

Universite de Sherbrooke

Centre de Recherche en Technologie des Plasmas

Faculte des Sciences Appliquees

Sherbrooke (Quebec)

Canada J1K 2R1

819-821-7168

819-821-7955 (fax)

Donna Hale

INEEL/Univ. Of Idaho

P.O. Box 1625

MS 3765

Idaho Falls, ID 83415-3765

208-526-1744

208-526-0425 (fax)

lh5@inel.gov

Joachim Heberlein

Univ. Of Minnesota

Dept. Of Mechanical Engineering

111 Church St. S.E.

Minneapolis, MN 55455

612-625-4538

612-624-1398 (fax)

jvrh@me.umn.edu

William H. Hofmeister

Vanderbilt Univ.

Dept. Of Chemical Engineering

P.O. Box 1604

Nashville, TN 37235

615-322-7053

615-343-0466 (fax)

hof@vuse.vanderbilt.edu
Page 10 Workshop Attendance

National Labs (non NIST)

James R. Fincke John E. Smugeresky

INEEL Sandia National Laboratories

Lockheed Martin Idaho Technologies Co. MS 9403; Dept. 8712

P.O. Box 1625 Livermore, CA 94551-0969

MS 2211 925-294-2910

Idaho Falls, ID 83415-2211 925-294-3410 (fax)

208-526-2031 smug@sandia.gov

208-526-5327 (fax)

jfl@inel.gov

Kendall J. Hollis

Los Alamos National Laboratory

MS G770

Los Alamos, NM 87545

505-665-8223

505-667-5268 (fax)

hollis@mst.lanl.gov

Leslie Kohler

NSWC

9500 MacArthur Blvd.

West Bethesda, MD 20817

Code 612

301-227-5096

301-227-5548 (fax)

KohlerLK@nswccd.navy.mil

Luc Leblanc

National Research Council Canada

75 De Mortagne

Boucherville (Quebec)

Canada J4B 6Y4

450-641-5232

450-641-5106 (fax)

luc.leblanc@nrc.ca

Christian Moreau

National Research Council Canada

75 De Mortagne

Boucherville (Quebec)

Canada J4B 6Y4

450-641-5228

450-641-5106 (fax)

christian.moreau@nrc.ca
Workshop Attendance Page 11

NIST

Mail Address for NIST employees:

Name

NAT’L INST STDS & TECH

100 BUREAU DR STOP xxxx

GAITHERSBURG, MD 20899-xxxx

Frank S. Biancaniello Carol A. Handwerker

STOP 8556 STOP 8550

301-975-6175 301-975-6158

301-869-5629 (fax) 301-975-4553 (fax)

frank.biancaniello@nist.gov carol.handwerker@nist.gov

William J. Boettinger John W. Hastie

STOP 8555 STOP 8522

301-975-6160 301-975-5754

301-975-4553 (fax) 301-975-5334 (fax)

william.boettinger@nist.gov john.hastie@nist.gov

David W. Bonnell Rodney D. Jiggetts

STOP 8522 STOP 8555

301-975-5755 301-975-5122

301-975-5334 (fax) 301-869-5629 (fax)

Bonnell@nist.gov rodney.jiggetts@nist.gov

Paul A. Boyer Aaron Johnson

STOP 8556 STOP 8361

301-975-6970 301-975-5954

301-869-5629 (fax) 301-258-9201 (fax)

paul.boyer@nist.gov aaron.johnson@nist.gov

Sam R. Coriell John R. Manning

STOP 8555 STOP 8555

301-975-6169 301-975-6157

301-975-4553 (fax) 301-975-4553 (fax)

sam.coriell@nist.gov manning@nist.gov

Stanley J. Dapkunas Robert L. Parke

STOP 8520 STOP 8556

301-975-6130 301-975-6174

301-990-8729 (fax) 301-869-5629 (fax)

stanley.dapkunas@nist.gov robert.parke@nist.gov

Albert Davydov Albert J. Paul

STOP 8555 STOP 8522

301-975-4916 301-975-6004

301-975-4553 (fax) 301-975-5334 (fax)

albert.davydov@nist.gov albert.paul@nist.gov
Page 12 Workshop Attendance

Patrick Pei

STOP 8520

301-975-3681

301-990-8729 (fax)

patrick.pei@nist.gov

Cary Presser

STOP 8360

301-975-2612

301-869-5924 (fax)

cary.presser@nist.gov

Richard E. Ricker

STOP 8553

301-975-6023

301-975-4553 (fax)

richard.ricker@nist.gov

Stephen D. Ridder

STOP 8556

301-975-6175

301-869-5629 (fax)

stephen.ridder@nist.gov

Robert J. Schaefer

STOP 8555

301-975-5961

301-975-4553 (fax)

robert.schaefer@nist.gov

Jay S. Wallace

STOP 8520

301-975-5984

301-990-8729 (fax)

jay.wallace@nist.gov
NIST CERAMICS COATINGS PROGRAM

S. J. Dapkunas

Ceramics Division

Materials Science and Engineering Laboratory

National Institute of Standards and Technology

Thermal Spray Workshop on Sensors, Modeling and

Control Strategies

November 19, 1998

Gaithersburg, MD

PROGRAM OBJECTIVE

To develop measurement, characterization and modeling

methods which support improvement of process control and

property/performance prediction

PROGRAM STRATEGY

Current Emphasis

Future Emphasis

Utilize NIST analytical capability with academic

and industrial processing capability.

Cooperatively set goals with partners, focus on

specific issue.

Focus NIST research efforts on the same

materials/samples to intensify effect.

Transfer results through direct collaboration.

Implement measurement methods through

codified standards, SRMs, data, models.

- Thermal Barrier Coatings

- Wear and Erosion Resistant Coatings

- Plasma Spray Deposition

- Additional Processing Types

- Functionally Graded Materials

Workshop Slides Page 13

PRESENTATION SLIDES

NIST Ceramic Coatings Program

S. J. Dapkunas (NIST)
COLLABORATIONS

Powder Producers

Equipment Manufacturers

Instrument Manufacturers

OEMs

Oak Ridge National Laboratory

Sandia National Laboratory

National Aerospace Laboratory/Kakuda

Mechanical Engineering Laboratory/Tskuba

SUNY/Stony Brook

BAM, Berlin

DLR, Cologne

IPP, Prague

CERAMICS COATING PROGRAM

Plasma Torch Deposit/Substrate Applications

Equipment

Torch Design Gas Velocity Porosity

Gas Flow Density

Power Phases

Atmosphere Chemistry

Feedstock Anisotropy

Feed Stock

Chemistry TGO

Chemistry

Size Distribution

Conductivity

E

Hardness

CTE

Cohesion/Adhesion

Fracture Properties

Residual Stress

Hardness

CTE

Cohesion/Adhesion

Residual Stress

Morphology Phase

Phase

Phase Chemistry

Chemistry

MODELS

Important Parameters

Process Microstructure TBC Wear

Temperature

Temperature

Substrate Temperature

Spray Angle, Distance

Velocity

Melting Point

Conductivity

E

Page 14 Workshop Slides

NIST Ceramic Coatings Program (cont.)

S. J. Dapkunas (NIST)
PROGRAM STRUCTURE

Develop characterization and measurement methods for

feedstock powder and relate to deposition behavior and

microstructural features

Develop methods to examine microstructure and properties

of coatings to provide input to property model development

and relate to processing parameters

Develop microstructural models to describe microstructural

effects on properties/performance

Processing

Coating Characterization

Modeling

PARTICLE SIZE, mm

CUMULATIVE MASS FRACTION, %

60

40

20

0

80

100

0 50 100 150 200 250

Laboratory

"A"

"B"

"C"

"D"

"E"

"F"

"G"

"H"

"I"

SRM 1982- Zirconia Thermal Spray PowderCertified Values- SEM

Reference ValuesLeeds & Northrup, Alloys International, Hoeganaes, Sulzer Metco,

Zircoa, Stellite Coatings, Metallurgical Technologies,

Pratt & Whitney, Praxair Surface Technologies, Horiba Instruments,

Coulter Scientific Instruments, Amherst Process Instruments,

Caterpillar, H. C. Starck

Additional InformationLaser Light Scattering

Sieving

Chemistry, Specific Gravity, Tap Density,

Hall Apparent Density, Hall Flow Rate,

Specific Surface Area

Particle Size Distribution

METHODS INCLUDED IN CERTIFICATION

PARTICIPATING ORGANIZATIONS

PSD OF ZIRCONIA BY DIFFERENT LABORATORIES USING

DIFFERENT MODELS OF MICROTRAC

Workshop Slides Page 15

NIST Ceramic Coatings Program (cont.)

S. J. Dapkunas (NIST)
COLLABORATION WITH SANDIA

“ ”, R. A.

Neiser and T. J. Roemer, 1996

An Investigation of Particle Trajectories and Melting in an AIR Plasma Sprayed Zirconia

Fig. 5 - Mean particle velocity vs. axial position.

Axial Position (mm)

20

120

260

240

140

160

180

200

220

40 60 80 100 120 140 160

Mean Particle Velocity (m/s)

Medium

Fine

Coarse

Ensemble

Fig. 7 - Mean particle temperature vs. radial position.

Radial Position (mm)

-12 -8 -4 0 4 8 12

2800

2900

Mean Particle Temp (K)

3000

3100

3200

3300

3400

3500

3600

Medium

Fine

Coarse

Ensemble

~ T m

Fig. 8 - Mean particle velocity vs. radial position.

Radial Position (mm)

-12 -8 -4 0 4 8 12

120

260

240

140

160

180

200

220

Mean Particle Velocity (m/s)

Medium

Fine

Coarse

Ensemble

Fig. 6 - Mean particle temperature vs. axial position.

Axial Position (mm)

20

2800

4000

3000

3200

3400

3600

3800

40 60 80 100 120 140 160

Mean Particle Temp (K)

Fine

Ensemble

Medium

Coarse

~ T m

SRM 1984-

SRM 1984 I - Sintered and Crushed, 1 to 40 um

SRM 1984 II- Aglomerated and Sintered, 10 to 50 um

Round Robin Participants

Powder Manufacturers- METCO, METECH, H. C.

Starck, Osram/Sylvania

Instrument Manufacturers- Leeds and Northrup,

Horiba, Coulter

Other- Sandia, Japan Thermal Spray Society

TAFA, Stellite, Spray Tech

Size Distribution

WC/Co Thermal Spray Powder Particle

-

-

-

- CoatersPage 16 Workshop Slides

NIST Ceramic Coatings Program (cont.)

S. J. Dapkunas (NIST)
Relative Section Depth [μm]

Variation in Elastic Modulus with Thickness, ZrO 

2

- 8% Y2 O3

-50 0 50 100 150 200 250 300 350

30

32

34

36

38

M

odulus [G

P

a]

SX 100 TBC

4.7 N Indentation Load

Modulus Evaluated for 4 N Load

Sample Inclined at 3.7° to Stage

Scattering angle [degrees 2o]

80

Comparison of XRD and Neutron Spectra of Sylvania Sx223

(ZrO - 8% Y O ) Feedstock Powder 2 2 3

Scattered intensity [arb. units]

upper

middle

bottom

Tetragonal Phase

Cubic Phase

XRD data

neutron data

Rietveld fit on neutron data

70

COATINGS CHARACTERIZATION

Thermal properties of coatings

Guarded hot plate adapted to measurement of coating

thermal conductivity.

Correlation with laser flash method in progress.

Standard Reference Material for thermal conductivity to be

initiated by FY 1998.

Workshop Slides Page 17

NIST Ceramic Coatings Program (cont.)

S. J. Dapkunas (NIST)
Results for Two Thicknesses of 7%

Yittria-Stabilized-Zirconia PVD Coating

T

h

e

r

m

al C

o

n

d

u

ctivity, W/(m•K)

Temperature, oC

400 600 800 1000

0.5

1.5

2.5

1

2

3

0 200

0

COATINGS CHARACTERIZATION

Instrumented Indentation Measurements

Development of technique for measurement of elastic

modulus

- Micro-indentation for thick TBCs

- Nano-indentation for thin coatings

Variation of E through thickness determined

BAM collaboration

Data used in microstructural modeling

VAMAS Round Robin

Praxair - ATP

Workshop on Indentaion Measurements and Standards planned

to identify issues and approaches.

Page 18 Workshop Slides

NIST Ceramic Coatings Program (cont.)

S. J. Dapkunas (NIST)
MODELING

Object Oriented finite Element (OOF) model developed to

provide guidance on role of thermal sprayed microstructure

on properties.

Physics Based Finite Element TBC model - SBIR/Optimal

Corp.

-

-

-

-

Predicted E compared with measured E (Instrumented

indentation).

Models available on WWW

http://www.ctcms.nist.gov/~wcraig/oof.html

PPMZOOF - Tool to take an image to an element based

representation with constitutive properties specified by a

user.

OOF - Tool to perform physical tests and obtain

microstructural behavior.

Ceramic Coatings Database Design, NIST, Nov. 1998, Page 1 of 1

Design of the Ceramics Coatings Database

The database is divided into four components:

1. Bibliography

2. Material Identification

3. Measurement Methods

4. Property Tables

Because the information is more or less uniform from one source to the next, a fixed field

structure is useful and effective. Separate fields are used for authors’ names, title of

journal or book, title of paper, volume, issue, page numbers, year, publisher, editor

Because processing methods and the kinds and extent of information reported vary greatly

from one paper to the next, material identification is provided using a small set of fixed

field variables to record generic classification information (such as chemical class is oxide,

chemical family is Al-O, formula is Al O , informal name is alumina, etc.) plus a text field

in which the description of the processing method can be entered as fully as information is

available.

2 3

Because measurement methods and the procedures followed can vary greatly from one

paper to the next and the procedures can be adapted for special purposes,

measurement methods are described using only one fixed field variable (to record the

generic name of the test method) and one text field in which the description of the

measurement method can be entered as fully as information is available.

ad infinitum

Property data are contained in tables with a configuration that is partially predefined.

Each table consists of five columns. The names and unit of the property column (such as

Hardness in GPa) are preassigned. The names and units of the remaining four columns are

defined (if used) at the time of data entry. (This design has been found to be sufficiently

flexible to accommodate nearly all of the studies encountered in the development of the

databases for bulk structural ceramics and for high temperature superconductors.) The

layout of a typical data table might look like:

Mass Fraction

of Y2O3

%

Indentation

Load

N

Hardness

GPa

Workshop Slides Page 19

NIST Ceramic Coatings Program (cont.)

S. J. Dapkunas (NIST)
Variables and Properties for a Ceramic Coatings Property Database, R. G. Munro, NIST, 7/30/98, Page 1 of 4

The tables on the following pages contain lists of variables and properties that

were used, reported, or discussed in a sampling of the literature on ceramic

coatings. The sampling consisted of 57 papers drawn from 23 journals.

Variables and Properties

for a

Ceramic Coatings Property Database

ACerS Bul.

Adv. Matl. Proc.

Colloque de Phys

J.Alloy.Comps.

J.Am.Cer.Soc.

J.Eur.Cer.Soc.

J.Mat.Sci.

J.Mat.Sci.Let.

J.Sol.St.Chem.

J.Th.Spray Tech.

J.Thermophys. Ht.Trans.

J.Tribol.

J.Cer.Soc.Jpn.

J.Chem.Soc.Jpn.

J.de Phys.

J.Engr.Gas Turb.Powr.

Mat. Char.

Mat.Sci.Engr. A

Nuc.Instru.Meth.Phys.Res. B

Plasma Chem.Plas.Proc.

STLE Trib.Trans.

Surf.Coat.Tech.

Surf.Interface Analysis

1

1

1

1

3

1

1

1

2

1

2

2

3

11

1

1

1

3

1

1

1

16

1

Journals # of Papers

Variables and Properties for a Ceramic Coatings Property Database, R. G. Munro, NIST, 7/30/98, Page 2 of 4

Processing Variables

Variable Type

Process name

Carrier gas

Carrier gas flow rate

Flow rate

Fuel gas

Fuel gas flow rate

Nozzle diameter

Nozzle length

Oxyfuel ratio

Particle flow pattern

Particle velocity

Plasma gas

Plasma gas flow rate

Plasma gas pressure

Plasma spray power level

Powder feed rate

Cooling rate

Deposition efficiency

Deposition method

text

text

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

text

text

image

text

Variable

Powder injection point

Powder wheel speed

Primary gas

Secondary gas

Primary gas flow rate

Total gas flow rate

Primary gas mass flow rate

Secondary gas mass flow rate

Spray standoff distance

Spray impingement angle

Spray gun input power

Substrate temperature

Torch rotation speed

Torch translation velocity

Torch traversing speed

Raw material feed rate

Relative surface travel rate

Reynolds number

Rotation speed

Type

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

text

text

Page 20 Workshop Slides

NIST Ceramic Coatings Program (cont.)

S. J. Dapkunas (NIST)
Variables and Propertiesfor a Ceramic Coatings Property Database, R. G. Munro, NIST, 7/30/98, Page 3 of 4

Powder Variables

Variable Type

Name

Density

Hall flow rate

Melting point

Particle size

Particle size aspect ratio

Particle distribution

Particle shape

Thermal conductivity

Thermal expansion (CTE)

text

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

text

Variable

Binder

Binder, Amount of

Bond coat

Bond coat, Amount of

Top coat

Top coat, Amount of

Element

Element, Amount of

Phase

Phase, Amount of

Substrate

Type

numeric

numeric

numeric

numeric

numeric

text

text

text

text

text

text

Specimen Variables

Variable

Test name

Corrodent spicies

Environment

Heating rate

Sliding speed

Temperature (of coating)

Temperature (of substrate)

Load

Loading rate

Lubricant

Number of cycles

Penetration depth (of indenter)

Type

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

text

text

text

text

Test Variables

Variables and Properties for a Ceramic Coatings Property Database, R. G. Munro, NIST, 7/30/98, Page 4 of 4

Properties

Property

Oxidation, Scale thickness

Oxidation, Weight gain

Poisson’s ratio

Pore size

Porosity

Specific heat

Spectral refectivity

Strength, Adhesion

Strength, Bond

Strength, Cohesion

Strength, Compressive

Strength, Creep

Strength, Flexural

Strength, Shear

Strength, Tensile

Strength, Tensile bond

Stress relaxation exponent

Surface roughness

Texture coefficient

Wear coefficient

Wear rate

Weibull modulus

XPS spectra

Thermal conductance

Thermal conductivity

Thermal diffusivity

Thermal expansion

Thermal shock resistance

Refractive index

Scratch adhesion critical load

Sound velocity

Spalling onset time

Type

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

Property

Absorption band

Absorption coefficient

Coating thickness

Corrosion rate

Creep rate

Creep stress exponent

Fracture toughness

Friction coefficient

Grain size

Grain size, Aspect ratio

Grain size, Distribution

Hardness

Heat transfer coefficient

Infrared spectra

Interfacial toughness

Lattice parameters

Lifetime, Coating

Lifetime, Fatigue

Lifetime, Thermal cycling

Lifetime, Thermal fatigue

Lifetime, Thermomechanical fatigue

Oxidation, Activation energy

Oxidation, Products

Oxidation, Rate

Oxidation, Resistance

Mean free path

Melting point

Micrograph

Density

Elastic (Young’s) modulus

Electrical resistance

Erosion resistance

Type

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

numeric

image

numeric

text

numeric

numeric

Workshop Slides Page 21

NIST Ceramic Coatings Program (cont.)

S. J. Dapkunas (NIST)
Page 22 Workshop Slides
Process Diagnostics

S. D. Ridder and F. S. Biancaniello

Thermal Spray Coatings Workshop

National Institute of Standards and Technology

Gaithersburg, MD 20899

November 19, 1998

Diagnostic and Control Sensors

for Spray Systems

1. Fraunhofer diffraction

-size of particles/droplets in flight

-rapid response (>2 Hz) control sensor

2. Schlieren/Shadow photography

-diagnostics of gas and plasma jets

-non-intrusive

-used to validate fluid flow models

3. Double pulse xenon flash illuminated video

-30 fps double exposure

-DIV (Digital Image Velocimetry) of particles/droplets

-size and shape of particles/droplets in flight

-potential for control sensor

4. High-speed video (50-100 ns exposure time)

-30 fps multiple exposure

-will be optimized for harsh plasma spray environment

6. Holography (3D image of particles/droplets in flight)

-hologram provides infinite depth of field

-multiple exposures (20 ns pulse duration)

-DIV of particles/droplets

5. 10,000 fps cinema

-20-30 ns exposure high intensity diffuse illumination

-surface details of particles/droplets

-particle entrainment in the plasma jet

-trajectory of particles/droplets in flight

-particle/droplet impact with substrate

Workshop Slides Page 23

Process Diagnostics

S. D. Ridder (NIST)

Past work at NIST in spray processing was focused on

metal powder production via gas atomization. Several

imaging techniques were developed to provide

information to help understand the disruption process

and to provide data for process models. Many of these

techniques are suitable for use in diagnostics of thermal

spray systems.
10,000 fps Cinema

diagnostics of dynamic particulate

plumes that preserves sequencial

events (temporal resolution=100 μs)

dynamic breakup events and high

speed particles can be "frozen"

with short , 20 ns, laser pulses

highly luminous spray processes are

imaged with coherent light through

narrow band filters

images can be formed from thermal

incandescence or by triggered laser

light pulses

frame sequence from high speed cinema

Dt = 100 sm Inconel alloy 625 Ar atomizing gas

Page 24 Workshop Slides

Process Diagnostics (cont.)

S. D. Ridder (NIST)

10,000 fps cinema is used to capture dynamic flow phenomena.

This slide shows a three frame

sequence from a 10,000 fps movie

imaged with the incandescent light of

the atomization plume. These movies

show several interesting phenomena

associated with the liquid delivery and

disruption in a close-coupled gas

atomizer. The liquid is drawn into the

gas flow from a recirculating “base

flow” region in the vicinity of the

metal pour tube tip. The pour tube tip

is at the top of each frame but does not

show in these images. The liquid

metal is seen to recirculate with little

gas mixing in the upper half of each

frame. It then rapidly accelerates

(blurs) and moves away as it mixes

with the gas flow in the bottom half of

each image.
Holography of

droplets and particles

coherent optical technique for

recording high resolution images

of dynamic 3-D particulate plumes

dynamic breakup events and high

speed particles are "frozen" with

short , 20 ns, laser pulses

highly luminous spray processes are

viewed with coherent light through

narrow band filters

resolution of 10 μm possible

throughout spatial volumes of

several cubic centimeters

Holocamera

HeNe laser

Beamsplitter

Reference

beam

Object beam

Reconstructed

plume image

Processed

plate

Hologram

Metal atomization

(side view)

Plume

Molten-metal

droplets

Atomization chamber

Imaging

telescope

Illumination

beam

"snorkel"

 tubes

Ruby laser

Workshop Slides Page 25

Process Diagnostics (cont.)

S. D. Ridder (NIST)

The NIST holocamera can produce

holograms of highly dynamic

phenomena such as gas atomization

or thermal spray. Each hologram

can be recorded with from one to

several (two or three) separate 20 ns

laser exposures. Multiple exposures

can provide velocity and time

resolved disruption data. Droplets or

particles as small as 20 μm can be

resolved and each hologram has

“infinite” depth-of-field. This is a

common characteristic of

holographic images. The hologram

itself is a record of the light phase

information present in the object

beam over the time of exposure.

In the NIST holocamera setup the

atomization plume passes through the

object beam between the “snorkel”

tube viewports. The holograms are

analyzed by placing them back in an

optical setup that duplicates the

reference beam configuration used

during exposure. A reconstructed

image is formed in space that

faithfully represents the light phase

information present during exposure.

Standard photography equipment can

be used to view the reconstructed

image focusing anywhere within the

three-dimensional space defined by

the object beam and the two “snorkel”

tube viewports.
Need for Diagnostics, Sensors

and Modeling in Thermal Spray

Thermal spray coatings have not been sufficiently

reproducible to satisfy most industrial requirements

improved process design and processing control needed

development of process simulators to test effect

of varying process conditions. Use computer

simulations rather than expensive production tests.

automated feedback and control to provide

reproducibility and reliability in thermal spray coatings

automated "intelligent" processing needed

need improved reliability in coatings so that industry

does not need to inspect part before installation

(inspection is very costly)

every

Diagnostics, sensors, and modeling will lead to:

Page 26 Workshop Slides

Process Diagnostics (cont.)

S. D. Ridder (NIST)

This slide shows a typical

two-dimensional image taken

from a double exposure

hologram of the plume of a

gas atomizer (SiGMA). In

this hologram the object

beam was directed to pass

through the plume downstream from the region

shown previously in the 3

frame high-speed movie

sequence. Two 20 ns laser

pulses were used with 3 μs

delay between each exposure.

On either side of the central

low magnification image are

several higher magnification

views from selected regions.

These double exposure

holograms reveal both the

motion of stable spherical

droplets, and disruption

dynamics in larger unstable

liquid metal shapes.

Rational for NIST program

aimed at developing

sensors and control systems

for thermal spray.
Industry Needs to be fulfilled:

DOD/DOE are de-emphasizing non-military technology

transfer, thus making NIST involvement in the

development of these technologies more important

Several US industry representatives: (GE, GM,

Ford, Caterpillar / Solar Turbine, Miller Thermal / Praxair)

have expressed the need to implement

in commercial spray systems, primarily aimed at

moving from the current practice based on "operator art"

to an

using 

automated control and design improvements

"Intelligent Processing" control technique

advanced sensors

This project is aimed at addressing the ,

, , and issues as applied to

Thermal Spray Processing in general and

Plasma Spray Coatings in particular

diagnostic

sensing modeling control

in-flight measurements of particle size, speed,

and temperature

spray deposit temperature, thickness, texture,

and porosity

as functions of processing conditions

Specific measurement needs to be addressed include:

a)

b)

c)

d)

diagnostics

and sensors:

(temperature,

velocity, and

size distribution)

diagnostics

and sensors

(temperature, velocity,

thickness, porosity,

thermal conductivity)

particle / droplet

impact and substrate

plasma

gun

powder

feed tube

for

Plasma Spray

Diagnostics, Sensing, and Modeling

diagnostics,

modeling (CFD),

and process control

particle

entrainment

particles / droplets

in flight

Workshop Slides Page 27

Process Diagnostics

(cont.)

S. D. Ridder (NIST)

Schematic showing plasma spray

process and where NIST research will

focus.
nist1260.bmp

triple exposure:

E = E = E = 50 ns

D = D = 3 s

1 2 3

1 2 μ

.

.

 70 m

 140 m•s

μ

- 1

particle size

particle speed

.

.

 75 m

 135 m•s

μ

- 1

particle size

particle speed .

.

 50 m

 130 m•s

μ

- 1

particle size

particle speed

.

.

 60 m

 130 m•s

μ

- 1

particle size

particle speed

.

.

 70 m

 130 m•s

μ

- 1

particle size

particle speed

500 μm

Cooke Corporation High-Speed Video

Duran 50NB

axial powder feed

NIST Thermal Spray Facility

Praxair Guns

BP400

wire feed

SG-100

powder feed

Page 28 Workshop Slides

Process Diagnostics (cont.)

S. D. Ridder (NIST)

This slide, of thermal spray particles

in flight, is an example of one of the

imaging systems being developed at

NIST for spray processing diagnostics.

This image was made using a new

high speed video camera with a peltier

cooled CCD. Developed in response to

a NIST funded SBIR solicitation, this

camera can superimpose up to 10

images per frame with exposures as

short as 50 ns. Framing rates are

determined by CPU and data bus

speed. 30 fps are possible using a 300

MHz CPU with a 100 MHz data bus.

Some of the spray guns available in

the NIST Thermal Spray Facility.
Workshop Slides Page 29

Mention of specific companies or products is solely for identification. NIST makes no claim that these companies or *

products are particularly more appropriate for the applications mentioned than other similar items.

Spectroscopy Measurements (Summary)

Spectroscopy of Thermal Spray Plumes

P.K. Schenck, D.W. Bonnell, J.W. Hastie,

(presented at the Thermal Spray Coatings Workshop, NIST, 11/19/1998)

Background: NIST is in the latter stages of an SBIR development program (Stratonics) aimed at implementing two-color

pyrometry of particulates in thermal spray plasmas. High speed CCD camera technology is being used to both time- and

spatially-resolve the particulates and to derive their temperature. However, the effect of discrete spectral emissions on the

measurements needs to be established. Also, an independent method of temperature measurement is desirable to validate the

pyrometric approach. Both of these needs can be accomplished through simultaneous detailed spectroscopic measurements on

plasma spray plumes. A tandem spectroscopic–pyrometric study was initiated jointly with the Metallurgical Processing Group

(MPG) and Stratonics, using the MPG’s research spray facility at NIST. The spectroscopic approach and preliminary results are

outlined here. This collaboration utilizes expertise in spectroscopy, high temperature processes, and temperature measurement,

and the availability of a highly portable fiber-optic-coupled visible/near-IR spectrometer. Of particular interest was the question

of contributions to the emission from the arc, including possible interferences from constituent line spectra, both from plasma

gases and vaporized material.

Experimental: The spectrometer used was an integrated Ocean Optics mini-crossed Czerny-Turner spectrometer/CCD *

detector system (see figure below), with a fixed range of approximately 380-926 nm and approximately 1 nm resolution. The

spectrometer’s fiber-optic coupling had a fixed 25 μm slit assembly (1000 μm high) to assure that the fiber position from one

setup to the next did not affect the system calibration. For this work, spectra were taken at 50, 20 and 10 kHz digitizer rates,

averaging 4 scans, for effective collection times of 81, 204, or 409 ms. End points are normally discarded before data processing

as not being fully illuminated by the grating, to give a final range of 384-895 nm.

The optical fiber used was supplied by the Metallurgical Processing Group, and was a polymer-clad single-strand quartz

fiber 1.0 mm effective diameter with standard SMA couplings at each end. This fiber showed a single flaw (evidenced by light

leakage through the sheath), but the output image of a distributed light source showed no obvious patterning that might be

indicative of a seriously damaged fiber. The fiber was approximately 7-1/2 m long and was installed in the experimental facility

through a long pipe, allowing the spectrometer and data system to be located outside the spray area. Most of the fiber length was

shielded from stray light.

Initial survey spectra (see Bare Fiber figures, below) were taken at two positions using just the fiber tip as a collector,

indicating that plasma light was a serious background contaminant, and that careful shielding and a collimating optical element

was needed. The collection optic prepared consisted of a 12.7 mm f/1 quartz lens mounted in a housing specially constructed to

allow the fiber to be adjusted at the rear focal point. A blackened lenshood extending approximately 3.5 cm beyond the face of

the lens (l/d ~3) was added to further reduce off-axis light. The fiber was fine-adjusted to the focal point by projecting light

through the fiber and moving the fiber to produce the most uniformly illuminated projected spot. This spot had a diameter of 2.5

cm at a distance of 25 cm, the nominal working distance. This corresponds to an acceptance angle (full-cone angle) of less than

6 degrees (~0.1 rad). Off-axis light acceptance appeared to be minimal.

Our optical system was aimed at the pyrometer input lens using projected light (see Schematic of experimental layout,

below) At the lens of the pyrometer, the projected light circle from the fiber’s optical system was completely within the

pyrometer’s front lens element. Still, we found that including a simple lenshood on the pyrometer as a scattered light trap

significantly reduced background light.

A set of measurements were taken under actual spray conditions, with particles of nearly monosized Inconel 625.,

beginning 30 cm downstream (“Far”), and at 5 cm intervals moving upstream to the 15 cm location (“Near”). The vertical

location of the particulate stream was adjusted based on the pyrometric image, and did not necessarily peak the spectrometer

signal. Integration intervals chosen to give maximum signals of at least 1⁄2 full scale. Neither blank nor particulate free spectra

were taken except after the final 15 cm data collection for this series. Analysis with, and without blank subtraction at this point

indicated were nearly identical, indicating that the background plasma interference had been effectively eliminated. The plasma

operating parameters (gas flow/composition) were altered slightly toward the end of this set because of low supply gas pressure.
I(8,T) '

A

8

5

exp

c

2

/8T

&1

Page 30 Workshop Slides

Spectrometer wavelength calibration was accomplished by taking a spectrum of a standard Hg calibration lamp, and

deriving a polynomial correction expression that corrected all observed line centroid channel (pixel) positions to the known line

positions. Wavelength error after calibration was negligible with respect to the wavelength span of individual pixels.

Spectrometer sensitivity calibration was accomplished by taking a spectrum of a calibrated standard radiance source

(Optronics Labs, Inc. - a broad-filament incandescent source) with the entire spectrometer assembly, including the fiber optic

and lens assembly attached just as used for experiments, aimed at the lamp. It was necessary to use an aperture to restrict the

field of view to just the calibration point on the lamp. Calibration without the restriction aperture affected final temperatures

determined by approximately 8 K (resulting in lower derived T’s). This difference is probably the largest calibration error effect.

The standard lamp was supplied with a series of absolute radiance calibration points at selected wavelengths. These data were

fitted piecewise with Planck functions to interpolate to the exact wavelengths for each channel (pixel) of the spectrometer. Each

point of the observed calibration spectrum was then divided into the resulting calibration table to obtain a new set of factors that

could then be used to correct each observed spectrum, pixel by pixel, for the complete system sensitivity.

To process each data spectrum, dark subtractions were done before sensitivity corrections. It should be noted that, while Ar

lines were still discernable in the particulate-bearing plumes at 15 cm, the contributions were relatively small, and became much

smaller at longer distances. We consider this good evidence that stray light is controlled, but additional tests are needed to verify

that conclusion. When the particle-free plume spectra were subtracted, all line-spectral features essentially vanished, except

perhaps slight distortions at the locations of the lines. For some of the data, it was not practical to take comparable particle-free

spectra. Those data sets were treated without that correction, and the effect was minimal. After background subtractions and

sensitivity corrections were made, the resulting spectra were fitted to a Planck function with amplitude and temperature as the

fitting variables.

where A and T were non-linear fitting parameters; A was a scale factor that included factors in viewing and particle geometry

and T was the derived blackbody/graybody temperature. 8 are the measured wavelengths and c2 = 1.438786E7 [nm@K] is the

second radiation constant.

Results and Discussion: The included figure shows final corrected I vs 8 data for the last measurement position in the spray

(at ~15 cm downstream), where all experimental setup corrections were finalized and there was an opportunity to obtain a

particle-free background spectrum. The best-fit Planck-law curve gives a derived temperature of 2557±2 K, where the statistical

uncertainty is the standard deviation of the parameter. The curve data appear to arise from a very good blackbody, with only

minor deviations between 850 and 900 nm, which have no significant effect on the result. We implicitly assume that particle

emissivity is essentially independent of wavelength (the “gray-body” assumption). Since the fit includes an amplitude parameter,

only the wavelength dependence of the emissivity is not accounted for. We did notice that, using an initial sensitivity calibration

series from our standard radiance lam, no Planck curve fit all the data. That initial calibration was taken without an aperture to

restrict FOV, and thus included light from filament locations at significantly different temperatures from the calibration point.

We thus note that the fitting process does not yield this good a fit over the entire span of the thermal spray data unless the data

are Planckian (i.e., represent a region with a defined average T).

We feel that we have resolved most of the data analysis issues, and that there are only small systematic errors remaining in

the data analysis. Clearly, It is more difficult to assess the accuracy with the data currently available and with the uncertainties

inherent in making ensemble-average measurements of an inherently dynamic process. We were able to analyze other data sets

by assuming that blanks were similar, and ignoring the particle-free background subtraction. For the replicate point at 15 cm,

the resulting derived temperature was 2549 ±3 K (after recalibration), indicating that the statistical and replicate uncertainties

are comparable. An earlier data set, from a separate start/run of the torch (but at the same conditions) before the final scattered

light changes were made to the experimental setup yielded a value of 2536±2 K. Thus, the replicate error could be of the order

of 20-25 K. We examined fitting subsets of the data, with the expected result that the uncertainty in derived T was greater. The

single-data-set fit figure below also includes equivalent Planck curves, matched to the data at the average corrected intensity,

with values of T 100 K more and less than the fitted value. It is clear that differences in T of that magnitude would result in very

different curve shapes than we observed. Thus, our true sensitivity is much better than ±100 K. The replicate error value of 25 K

noted above seems to be a reasonably conservative estimate of our run-to-run uncertainty.
Workshop Slides Page 31

Other sources of error include the possibility that the particles are not good gray bodies (i.e., the emissivity variation with

wavelength, ,(8)Öconstant), or that different-sized particles have different effective emissivities. We reanalyzed the data,

assuming 1 percent and 5 percent linear variations in emissivity over the span of the spectra. These different assumptions

resulted in changing the derived temperatures by about 5 K and 22/24 K, respectively. Decreasing ,(8) increases T, and vice

versa. This order of variation is certainly not unrealistic. When we applied a reference ,(8) curve for W as a model to our data,

the derived T decreased by 90 K. Thus, ,(8) uncertainties could be a significant source of error, even for two-color-type

pyrometry or for our multispectral determinations.

Another possible source of experimental error for the spectroscopic measurements is the acceptance angle of the current

optical train. We are clearly sampling over the entire width of the particulate stream at all distances, and physical differences in

the plume at different distances could affect the averaging process. In particular, the absolute total intensities were greater at

greater distance, indicating that the wider plume at longer distances was still within the field of view. Less likely is the

possibility that we are still accepting significant amounts of stray light, or that reflected light from the plasma is a major

contributor. Other possible sources of error include the possibility that particles are obscured by vaporizing material or that there

are dynamic changes in average T on the timescale of our data collection. Our observation of Cr emission in the wide area early

scans clearly indicates that some metal vapor is present and excited by the plasma. Simple calculations of vaporization rate

indicate that the vapor pressure of Cr can be more than one bar, and that several percent of the Cr could be vaporized during the

particle flight time. All these errors, it should be noted can affect the individual particle pyrometry, as well. 

It should be noted that the averaging process of the spectrometric data tends to be essentially exponentially weighted toward

the higher temperature contributors. Thus, differential cooling processes (i.e., cooling of some particles by entrained air or

radiant emission, as opposed to others remaining shielded by hot gas) might not be well averaged without a more restricted view

of the plume. There is clearly sufficient emission to allow additional restriction of the field of view.

Analysis of the points taken from 30 cm to 15 cm as discussed above all fall within ±11 K, and are thus only statistically

different from the other measurements. The slight upwards trend shown in the Temperature vs Position figure may easily be due

as much to changes in experimental conditions (perhaps due to the increased visual area presented by the downstream plume

with respect to its spatial extent upstream, or to the likelihood that hotter particles will move further, changing the temperature

distribution of the plume, etc.) as to actual trends in the plume. As noted, there are several possible physicochemical effects

(including the possibility of reaction heating of plume particles by atmospheric oxygen, changes in size and emitting surface due

to vaporization, vaporization obscuring the upstream particle emission more than downstream, axial particle energy distribution

effects, and others still to be considered) that could explain such a trend, but we do not currently consider our replicate precision

to be sufficient to assert that the apparent data trend is significant.

Comparison of our data with the results of the particle-imaging pyrometer were treated in Stratonics’ presentation. In comparing

measurements, it needs to be recognized that there is likely a distribution of temperatures among the particles at any point in the

plume stream. The integration time of our spectrometer is sufficiently long, and the current field of view sufficiently broad, that

we are averaging over an ensemble of the particles passing our sampling region. That we see what amounts to a well

equilibrated blackbody simply means that all the major emission sources in view have a blackbody-like visible emission profile

(i.e., Planckian) and the sum over all particles is again Planckian. Thus, to compare the two types of measurements, the

pyrometric analysis needs to assess the effective distribution of particles (both number and by area), and apply a suitable

averaging function. It would be desirable to use the temperature and size distribution data from the imaging pyrometer to

generate a simulated DC or average brightness of the spray for comparison with our data.

Conclusions: We have demonstrated a spectroscopic technique to obtain a high-quality ensemble average temperature of the

particulate stream in a plasma spray apparatus. We still need to consider questions regarding the radial temperature distribution,

and the “best” way to arrange the spectrometry apparatus to provide a comparable viewpoint to that of the particle-imaging

pyrometer. It would also be useful to extend our wavelength range to more closely match that of the pyrometric filters in use, in

order to identify just where emission interferences may become important in the pyrometer’s band pass regions. Other effects,

such as particle size, size distribution, and plasma emission interferences at closer points in the spray plume still need to be

investigated.
Page 32 Workshop Slides

Spectroscopy Measurements (Vuegraphs)

D. W. Bonnell (NIST)

Multichannel Spectroscopy of plume/particles

Purpose:

! thermal vs non-thermal emission

! interferences for purpose-built pyrometer

! Planck Law temperature probe - test of “temperature”

! insight into transport - hot or what? 

Approach:

! commercial broad multispectral spectrometer

! silica fiber-optic coupled

! control of imaging area - only moderate time/space resolution

! Blank & particle-free backgrounds subtracted

! calibrate relative spectrometer response, correct spectra

! fit Planck fn, ~375—900 nm

Problems:

! Temperature/energy distribution in plume

! Light acceptance 

! Scattered light control - from particles (Pls), walls, apparatus

! Plume axis vs Pl axis and torch axis

! Pl visual density - optically thick or semitransparent?

! 2-color pyrometry vs 1000+-color spectrometry

! Pl-by-Pl imaging vs “ensemble averaging”

! Strong surface curvature of small Pls

! Gas effects!

S torch gas(es)

S vaporized from Pls - obscuration?

S turbulence, segregation, expansion cooling...

! Sensitivity, resolution of T

! What do we mean by “temperature”
Powder

Feed

Pyrometer

Plasma spray Particles

 torch

f/1 collimation

 assembly

optical

fiber

Workshop Slides Page 33

Spectroscopy Measurements (Viewgraphs, cont.)

D. W. Bonnell (NIST)

Schematic of fiber-coupled spectrometer - Courtesy OceanOptics, Inc.

Schematic of experimental layout for simultaneous measurements with ThermaViz® imaging pyrometer and fiber optic-coupled

spectrometer.
EMISSION SPECTRA - CLOSE IN

WAVELENGTH (nm)

EMISSION INTENSITY (counts)

(Bare Fiber - UNCORRECTED)

NONE POWDER DIFFERENCE

~5 cm downstream; 5 cm FOV Ar

Cr

Cr

5000

4000

3000

2000

1000

350

0

-1000

400 450 500 550 600 650 700 750 800 850 900

350 400 450 500 550 600 650 700 750 800 850 900

WAVELENGTH (nm)

0

1000

2000

3000

EMISSION INTENSITY (counts)

EMISSION SPECTRA - FAR OUT

(BARE FIBER,UNCORRECTED)

NONE POWDER DIFFERENCE

Page 34 Workshop Slides

Spectroscopy Measurements (Vuegraphs, cont.)

D. W. Bonnell (NIST)

Spectra taken with bare fiber probe, ~5 cm

downstream, ~5 cm FOV. The discrete line

cluster at ~440 nm, and at ~530 nm are from

Cr; the lines above ~700 nm are Ar plasma

lines. Note that almost none of the plasma light

comes from the powder (difference spectrum).

Spectra taken with bare fiber probe,

~15 cm downstream, ~5 cm FOV. The

lines above ~700 nm are, as before, Ar

plasma lines, and are essentially all

plasma light.
350 400 450 500 550 600 650 700 750 800 850 900

WAVELENGTH (nm)

0

500

1000

1500

2000

2500

EMISSION INTENSITY (counts)

EMISSION SPECTRA AT 15.24 cm

(UNCORRECTED)

BLANK POWDER DIFFERENCE

350 400 450 500 550 600 650 700 750 800 850 900

WAVELENGTH (nm)

0

1000

2000

3000

4000

EMISSION INTENSITY (counts)

EMISSION SPECTRA

(uncorrected, unscaled, offset)

15.24cm 20.32cm 25.4cm 30.48cm

Workshop Slides Page 35

Spectroscopy Measurements (Vuegraphs cont.)

D. W. Bonnell (NIST)

Uncorrected (for sensitivity) emission

spectra with F/1 lens, and lens-hood

assembly; FOV ~ 2.5 cm. The material

sprayed was 446 Ferritic SS powder, -

63 +23 micron size.

A sequence of spectra, as above, at

various distances downstream. Note

that the actual raw intensities are

comparable, and have been offset here

for visibility.
0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90

(E-4)

WAVELENGTH (microns)

0

10

20

30

40

50

60

70

80

90

100

EMISSION INTENSITY (arb.)

EMISSION SPECTRA AT 15.24 cm

(CORRECTED-lamp2)

data +100 K -100 K

2557 K

T

E

M

P

E

R

AT

U

R

E (K)

2535

2565

2555

2545

2540

2570

2560

2550

2575

POSITION FROM GUN (cm)

15 20 25 30

TEMPERATURE VS. POSITION

BLACK BODY FIT

data-dark data-blank early

Page 36 Workshop Slides

Spectroscopy Measurements (Vuegraphs cont.)

D. W. Bonnell (NIST)

Spectrum as above, after intensity

correction against standard radiance

source- the vertical axis is relative to

the standard. The red curve is a fitted

Planck function, for the temperature

shown. The two dashed curves

represent Planck curves for

temperatures 100 K higher, and lower.

Estimated error from all sources is

<50 K, excluding non-gray body

emissivity effects.

Resulting Temperature vs position

downstream in torch spray. The trend is

not considered significant. The constant

temperature behavior probably reflects a

combination of reaction heating and

particle surface oxidation.
Workshop Slides Page 37

Spectroscopy Measurements (Vuegraphs cont.)

D. W. Bonnell (NIST)

Conclusions/Summary

! Downstream dominated by Pl thermal radiation

S but still some line emission

S various absorption mechanisms seem small

S Planck radiation behavior looks really good

! Apparent agreement between image and spectroscopy

S far field interferences either small, or similar at both scales

S depends on averaging methods

S other problems noted can be made small

S T nearly constant in far field (25-50 cm downstream)

S T is quite high

S Vapor pressure question

! Measurement/analysis problems grow near the torch

! Calibration effects can be serious error sources

! Similar questions for measurement geometry?!

! Both spectroscopy and imaging have robust advantages

! Simple pyrometry likely to have serious problems

! What data do transport models need?

S reactive effects

S vaporization

S composition changes?

S T distributions
Page 38 Workshop Slides
J.E. Craig, R.A. Parker, D.Y. Lee

Stratonics, Incorporated

Thermal Spray Coatings Workshop

NIST, Gaithersburg, MD

November 18, 1998

A Two-Wavelength Imaging Pyrometer for

Measuring Particle Temperature, Velocity and

Size in Thermal Spray Processes

Introduction

ÿ Two-wavelength imaging pyrometer for metal

processing applications is developed

ÿ Imaging pyrometer applied to hot metal objects

ÿ Application to furnace using borescope technology

ÿ Particle temperature monitoring for thermal spray

Workshop Slides Page 39

Thermal Imaging

J. E. Craig (Stratonics)

An imaging pyrometer was

developed to measure surface

temperature of hot metal objects and

particle temperature, velocity and

size in thermal spray, spray-forming

and atomization processes.

Two-wavelength imaging provides

true, high-resolution temperature

measurement, even with emissivity

variation caused by roughness or

oxidation. The system, having a

field of view that spans the entire

particle stream in thermal spray

devices, provides continuous

measurement of the entire particle

stream. The software locates particle

streaks in acquired thermal images,

determines the intensity ratio and

dimensions of each streak, and

calculates the particle temperature,

velocity and size. Measurements in

the NIST thermal spray facility are

described.

A broad range of material processes

require a temperature imaging

solution that provides accuracy on

objects whose temperature or

emissivity varies across the surface.

Temperature imaging provides

measurements at thousands of

points, as opposed to the single

spatially averaged result from spot

pyrometers. Although infrared

imaging cameras provide spatial

resolution, they utilize a single

waveband and form a temperature

image from the intensity image by

assuming a single value for the

emissivity in the entire scene. This

proves problematic when the

emissivity varies across the object.

Also, infrared imaging cameras are

only available at high cost, thereby

limiting their widespread

application to industrial use.
Temperature Monitoring is Critical to

Process Control

ÿTemperature monitoring is a key

factor in quality and uniformity

Thermal Imaging Sensor for Intelligent Control

of High-Temperature Materials Processing

Thermal Processor

Thermal Imaging

Sensor

Process Controller

Finished Product

Hot Process

Process Model

Materials

Why Two-Wavelength Imaging

Measurements

ÿ Provides true, high-resolution temperature

measurement 

ÿ Insensitive to emissivity variation across surface

ÿ High-resolution temperature images result from

two-wavelength design

ÿ Precision optical design provides matched

magnification, differential focus and registration

to sub pixel resolution*

* Patent Pending

Specifications Imaging Pyrometers

Surface Particle

Temperature Range

600-2300°C (3-10°C)

Resolution

Standard: 300 H x 480 V

Across FOV, 20μm

Optional: 500 or 1000 lines

w/cooled chip &

12 or 16 bit A/D

Electronic Shutter

Speed: 7 exposure times

from 16 ms to 130 μs

Temperature Range

1200-2700°C (60°C)

Velocity:

10-900 m/s, (5%)

Size:

30-300 μm (30μm/pixel)

Field of View:

1/2 inch format: 6.4 x 18.6mm

2/3 inch format: 8.8 x 25.8mm

Electronic Shutter:

0.1 μs to 10 ms

Page 40 Workshop Slides

Thermal Imaging (cont.)

J. E. Craig (Stratonics)

Recently, multiple wavelengths have been

incorporated into instruments in an effort to deal

with the variation in emissivity of the object. The

issue of emissivity variation is particularly

problematic for high temperature materials

processes. A common theme to all this work is

that each sensor was developed to monitor a

particular process, albeit, steel, semiconductor

wafers or pulverized coal combustion. While

Meriaudeau, (Meriaudeau, 1996) described an

imaging device for monitoring steel, which used

only a single wavelength; it was concluded that a

two-wavelength approach would be much more

robust against effects of emissivity variation. For

some materials, such as silicon wafers, this

problem is overwhelming without using many

wavelengths (Kaplinsky, 1998).

The key design feature in the imaging pyrometers

used for this study is a filter pairing of the

brightness of the long and short wavelength at

each point on the heated surface or particle.

Surface temperatures are imaged with a standard

charge coupled device (CCD) video camera while

particle streak imaging is achieved by

incorporating a special short-exposure CCD

camera (Morris, Karmali, 1997). This special

camera features a high-resolution (640 by 480

pixels) array which is cooled and read out with 12

bit dynamic range. Another unique imaging

feature of this camera is its electronic shutter.

This feature provides from 1 to 10 exposures per

video frame with each exposure duration

adjustable from 50 ns to 1.0 ms. The frame rate

for each video camera is 30 Hz. Single exposures

of (1 to 10) μs are typically used to obtain particle

streak images with appropriate lengths. The

streak length, adjusted with the exposure

duration, is set in the range of 10 to 30 pixels.

The signal to noise ratio of the streak intensity

and the velocity resolution is improved by

forming longer streaks. However, care was

required in setting the streak length to avoid

overlapping streaks and to insure that each streak

begins and ends within the FOV.
SURFACE Imaging Pyrometer System

ÿSystem provides simultaneous, high-resolution

images of short and long wavelengths

ÿSoftware analyzes the brightness ratio to measure

true-temperatures of surfaces with varying emissivity

PARTICLE Imaging Pyrometer System

ÿSystem provides continuous monitoring

throughout entire particle stream

ÿSystem captures simultaneous images, locates

streaks and analyzes data

Workshop Slides Page 41

Thermal Imaging (cont.)

J. E. Craig (Stratonics)

A demonstration of the SIP was

performed in the Material Processing

lab at the University of California at

Santa Barbara. A round steel disk

with an internal heating element and

thermocouple was used as the thermal

source of known temperature. The

pyrometer was designed for maximum

accuracy in the relatively low

temperature range of 1200 K to

1400 K. Therefore, filters were

selected in the longer wavelength

region of the camera response. A

video lens was selected having a FOV

of 50 mm by 100 mm at a distance of

500 mm.

The image acquired with the steel

block at its highest temperature is

shown. The left image is from the short wavelength filter and the right image is from the long wavelength filter. A wire was

placed on the steel block surface to provide a focusing aid for the surface pyrometer.

In another sequence of images, recorded as a movie, the temperature of the steel block was slowly raised by 3 K. The

temperature measurement of the SIP, relative to the thermocouple measurement, is in excellent agreement throughout the

movie. The best linear fit to the data shows that the temperature varied 3.3 K degrees, while the thermocouple measurement

indicates slightly less than 3 K, again indicating the good agreement between the imaging pyrometer and the thermocouple

measurements. However, there is local fluctuation of a few degrees in the SIP measurement and an absolute difference of 4 K.

The PIP was installed in the NIST

Thermal Spray Facility to measure

particle temperature, velocity, and

size. The two-wavelength imagery

captures particle streaks in fast

exposures, shown by the long and

short wavelength images (right and

left respectively) of nickel-based

superalloy particles within the spray

plume of a DC plasma torch using a

mixture of argon and helium gases.

Several particle streaks are observed

in the image, which spans a FOV,

5 mm by 15 mm. A single particle

streak image has been magnified.

Particle measurements are currently

performed in a post-processing step.

Since the FOV spans the particle

stream, the particle measurements can

be averaged into several spatial

regions spanning the particle stream.

Thus, every image provides up-dates

to particle temperature, velocity, and

size profiles across the stream.
Pyrometer Calibration

-2

0

2

1000 1500 2000 2500

ÿ Tungsten filament lamp used

for higher temperature range

(1000 - 2500K)

ÿ A gray-body source used for

low temperature range

ÿ Standard deviation, 7.66 K

Pyrometer Calibration, 1000-2500 K

Temperature (K)

Percent Deviation Heat Treating Application of Surface

Pyrometer

Temperature Map

ÿ Cooled borescope is used as

interface to furnace

ÿ Pyrometer optics compatible

ÿ Temperature images achieved

Page 42 Workshop Slides

Thermal Imaging (cont.)

J. E. Craig (Stratonics)

The pyrometer was configured with a

filter pair with pass-bands centered at

800 nm and 900 nm and the

short-exposure CCD camera fitted

with a front lens having a

demagnification of 1/3. The pyrometer

was then focused on the tungsten

filament lamp described in the

previous section. The intensity was

measured in the long and short

wavelength regions for filament

current values between 12 A and 40 A.

The intensity ratios (long over short

wavelength) were determined and

associated with the known

temperatures of the tungsten

filament. A single constant is used

in the calibration of the pyrometer

to achieve the best fit with the

known filament temperature. The

constant is determined using

radiometric model of the pyrometer

response to gray-body radiation.

The uncertainty of the measured

temperature was below 8 K over this

range of calibration.

The radiometric model derived

calibration constant incorporates three

important components of the

two-wavelength, imaging pyrometer

response function: the two band-pass

filter transmission characteristics, the

relative spectral response of the CCD

camera and the spectral curves for a

Planck radiator. The deviation of the

calibration constant from its ideal

value of unity, which is typically

±10 %, is a measurement of the

validity and accuracy of the

radiometric model.
B99-07

Thermal Spray Application of

Particle Pyrometer at NIST

ÿ DC plasma torch, argon/helium gases

ÿ Nickel-based super alloy particles

Particle Measurements

ÿ Short-exposure (5μs) particle streak images

ÿ Measures temperature, velocity and size

Workshop Slides Page 43

Thermal Imaging (cont.)

J. E. Craig (Stratonics)

Movies with 135 frames were recorded in 4 seconds with the pyrometer focused at three points along the particle stream, 15 cm,

20 cm and 25 cm. The number of particle streaks were measured across the particle stream. About 800 to 1000 particles were

included in each number profile. The FOV was adjusted at each distance to re-center on the particle stream, which drops a few

millimeters between 15 cm and 25 cm. The relative vertical position of the three FOV's were not recorded, but were of the order

of a few millimeters. The profiles indicate that the particle stream was spreading with distance. The particle count histogram at

the 15 cm position indicated that the peak occurred at the 150 pixel column position (3.75 mm at 25 μm/pixel). The center of

the FOV was at 300 pixels, or 7.5 mm.
Particle Velocity Histogram

Average velocity drops from 100 to 90 m/s

Number Density vs. Velocity

0

100

200

300

400

500

50 100 150 200

Velocity (m/s)

Number

6 inch

8 inch

10 inch

Particle Velocity Profile

Velocity Profile

80

90

100

110

120

0 200 400 600

Position (Column)

Velocity (m/s)

6 inch

8 inch

10 inch

Page 44 Workshop Slides

Thermal Imaging (cont.)

J. E. Craig (Stratonics)

The velocity histogram was

constructed for data recorded

at all three locations. Most of

the velocity measurements fall

between 75 m/s and 125 m/s.

The mean velocity drops from

100 m/s to 90 m/s as position

changes from 15 cm to 25 cm.

 The velocity profile is shown

in this slide for the data at all

three locations. Again, it is

clear that the mean velocity

drops about 10 m/s as position

changes from the 15 cm to the

25 cm locations. The highest

velocity particles are on the

top of the particle stream (high

column number) but there are

very few of these events.
Particle Temperature Histogram

Average temperature drops from 2675 to 2520K

Numbe r Dens ity vs.Temperature

0

2 0

4 0

6 0

8 0

100

120

140

160

1500 2000 2500 3000 3500

T emperature (K)

Number

6 inch

8 inch

10 inch

Particle Temperature Profile

Temperature Profile

2400

2500

2600

2700

2800

0 200 400 600

Position (Column)

Temperature (K)

6 inch

8 inch

10 inch

Workshop Slides Page 45

Thermal Imaging (cont.)

J. E. Craig (Stratonics)

Most temperature

measurements fall between

2000 K and 3000 K.

The temperature profiles at all

three locations are nearly

constant across the entire

FOV.
Conclusions

ÿ Innovative two-wavelength imaging pyrometer

measures surface and particle temperature with high

resolution and accuracy

ÿ Surface temperature accuracy varies from 3-8K over

200 K range and 2000K range respectively

ÿ Particle temperature measurements accurate to 60K

ÿ Sensor is designed for integration into process control

systems by virtue of its powerful software capabilities

Page 46 Workshop Slides

Thermal Imaging (cont.)

J. E. Craig (Stratonics)

Conclusions: A surface imaging pyrometer

(SIP) and a particle imaging pyrometer (PIP)

have been developed to measure either the

surface temperature of hot metal objects or to

measure particle temperature, velocity and size

in thermal spray, spray-forming and atomization

processes, PIP. 

The resolution of the temperature image is 300

by 480 pixels across the FOV, which can range

from a few millimeters in a highly magnified

configuration to a full meter in a de-magnified

configuration. The pyrometer output is standard

video signal with a frame rate of 30 Hz. The

pyrometer has been calibrated across a wide

range of temperature, i.e. from (873 to 2700) K,

to an uncertainty of 8 K. The major feature of

the technology is the simultaneous imaging at

two-wavelengths and the resulting ability to

measure true-surface temperature of high temperature objects with variable emissivity. The variation can occur across the

surface of the object, or it can occur in time, for example as an object reaches a temperature, where oxidation begins. The feature

is also important for process environments, in which the surface is being coated or formed, by droplets or powder. In that case,

the result is a randomly rough texture or one with a significant variation in emissivity. The sensor is designed for adaptation and

integration into process control systems by virtue of its software system. This technology has the potential to provide a

significant improvement in modern, thermal process control systems.

The SIP instrument with analysis software has been developed to measure true surface temperature distributions with high

resolution and accuracy for materials that are graybody radiators, i.e. a body whose emissivity is constant with wavelength. For

materials that deviate from graybody behavior, an apparent ratio temperature will be inferred. The error will depend upon how

much the emissivity varies with wavelength in the range between the short and long wavelengths. Future research has been

proposed to study and model the error in the apparent ratio temperature inferred from the ratio of spectral radiances for target

materials having unequal emissivities at the two wavelengths, thus permitting more accurate measurement on a broader range of

materials. The technology shows considerable potential to monitor process uniformity, improve quality and reduce cost in

advanced high-temperature, materials processing.

The PIP has a FOV that spans the entire particle stream in typical, thermal spray devices, and provides continuous measurement

of the entire particle stream. Software was developed to determine temperature, velocity and size of each particle from the

intensity ratios and dimensions of the particle streaks. Measurements of plasma-sprayed nickel-based particles have been

obtained over a range of distances from the plasma torch showing the decay of temperature and velocity and the spreading of the

particle stream associated with increasing distance from the torch. The particle temperature measurements were confirmed with

a second measurement with a spectrometer. The comparisons between the two temperature measurements showed agreement to

within 60 K. The particle velocity measurements will be validated in a future effort.

References

M. Kaplinsky, et al, “Recent Advances in the Development of a Multi-wavelength Imaging Pyrometer”, Opt. Eng., 36(11) Nov.

(1998), p. 3176-3187.

F. Meriaudeau, et al, “Temperature Imaging and Image Processing in the Steel Industry”, Opt. Eng., 35(12), (1996), p.

3470-3481.

J. Morris and M. Karmali, “CCD Camera Helps Analyze Print Head Problems”, Photonics, Aug. 1997.
Num erical Sim ulation of

Underexpanded Jets

Aaron Johnson and Pedro I. Espina

Thermal Spray Coatings Workshop

N ational Institute of Standards and Technology

G aithersburg, M D 20899

November 19, 1998

G as-M etal A tom ization

Gas Jet

Gas Flow

Liquid D elivery Tube

Liquid M etal

M etal Spray

Workshop Slides Page 47

Numerical

Simulation of

Underexpanded

Jets

A. Johnson (NIST)
Melt

Chamber

Spray

Chamber

Metal

Spray

Crucible

Cyclone

Separators

Waste

Gas

Powder

Product

Schematic of Conventional

Supersonic Inert Gas Metal Atomizer

Atomization

Nozzle

Assembly

Annular Slit

Base Flow Region

Supersonic Jet

Ceramic Liquid Delivery Tube

(Base)

Gas-Only Flow in a Close-Coupled

Supersonic Inert Gas Metal Atomizer

Page 48 Workshop Slides

Numerical Simulation of

Underexpanded Jets

(cont.)

A. Johnson (NIST)
Compressible Navier-Stokes Solver (NPARC 2.1)

Grid Adaptation Program (SAGE)

Descendant of the NASA ARC2D and ARC3D codes

Beam and Warming Approximate factorization algorithm

2nd order central-differencing

Diagonalized implicit matrices

Implicit 2nd and 4th order Jameson-style artificial dissipation

Euler backward time differencing

Parabolic formulation

Equations formulated in strong conservation form for a

curvilinear system

-

-

-

-

-

-

-

-

Numerical Method

Po , To , γ

Pe , Te

Pr , Tr

rdt

Ldt

R

ro

αdt αjet

Workshop Slides Page 49

Numerical

Simulation of

Underexpanded

Jets (cont.)

A. Johnson (NIST)
Characteristics

(Po , To , v = 0)

Characteristics

(Pr)

Volume

over the

LDT

(57x161)

Annular

Channel

(42x41)

Adiabatic, No-Slip

Adiabatic, No-Slip

Axis of Symmetry

Volume

after the

LDT

(289x215)

2.5

1

.

5

1.0

1

.0

Pe /Pr = 33

Pe /Pr = 46

Pe /Pr = 6.6

Pe /Pr = 20

r/R

r/R

0

-1

-2

-3

Experimental Schlieren Images vs. Numerical Density Contours

x/R x/R

0

-1

-2

-3

0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8

1

.5

2

.5

3.0 2.5

6

.5

1.5

2.0

3

.5

2

.5

3.5

2

.0 1.5 1

.0 1

.5

2

.0

2.5

1.5

2.0

1.5

1.0

2.0

1.5

1

.0 1.5

2

.5

7.5

2

.5 2

.0

1.0

1.5

1

.5

1

.0

2.0

0

.5

2.5

3

.

5

4

.5

4.0

3

.5

2

.5

2

.0

0

.5

1

.

5

1

.0

1.5

4

.0

9

.5

1.0

1.5

4

.0

3

.0

2

.0

1

.0

Page 50 Workshop Slides

Numerical

Simulation of

Underexpanded

Jets (cont.)

A. Johnson (NIST)
1. Results have shown these Pressure Ratio effects.

Numerical results in good agreement with schlieren images.

Aspiration behavior appears to be correlated with the

structure of the separation region.

Low pressure ratios, or long LDT, lead to flow separation

increasing the chances of freeze-off.

Atomizers should be easier to operate at high pressure ratios.

-

-

-

-

Conclusions

2. Limitations of Research

The numerical method can not be used to make quantitative

predictions of the aspiration pressure nor of flow separation.

Predictions of the aspiration and separation need validation.

Liquid is expected to have an influence on the structure of

the gas flow.

Future work should study the effects of particle loading on

the structure of the gas flow.

-

-

-

-

Workshop Slides Page 51

Numerical Simulation of Underexpanded Jets (cont.)

A. Johnson (NIST)
Page 52 Workshop Slides
Process Control

November 19, 1998

Stephen A. Osella, Ph.D.

Intelligent Computing Technologies, Inc.

intcomtec@aol.com

512-301-2444

ICT MEL

ICT Process Control Basics MEL

• Modern process control systems are very

complex involving:

– many instruments measuring pressure,

temperature, flow, level, etc.

– control elements such as valves, pumps,

heaters, etc.

• Output control is either:

– Manual : operator effects control

– Automatic : hardware or software machine

• Control elements are either:

– On-Off : ex. Valve

– Continuous : ex. Motor RPM

Workshop Slides Page 53

Process Control

S. A. Osella (ICT)
ICT Control System Conceptual View MEL

Inputs Outputs

Controller

[Control Function &

Internal State]

The controller transforms the inputs into outputs

using the control function and internal state.

ICT Simplifying Principles MEL

• Decompose control function

– Base decomposition on specific task, subsystem, or control element

– Keep what is independent separate

– Use global variables to synchronize activity

• Use state machines

– Specify individual control functions using statetables

Page 54 Workshop Slides

Process Control

(cont.)

S. A. Osella (ICT)
ICT Control Hierarchy MEL

Root Control Group

Controller Control Group Controller

State

Table

State

Table

...

...

State

Table

State

Table

...

Controller

State

Table

State

Table

...

Controller

State

Table

State

Table

...

ICT Control System Objects MEL

• Controller Hierarchy

– Controller Group

– Controller

• State Table

• Parameter : Input, Output, Internal

• State Table

– State-Variable

• State-Variable Event

– Parameter Reference

Workshop Slides Page 55

Process Control

(cont.)

S. A. Osella (ICT)
ICT Control System Workbench MEL

ICT Control System Workbench MEL

Page 56 Workshop Slides

Process Control

(cont.)

S. A. Osella (ICT)
ICT Control System Workbench MEL

• Each state-variable is defined by a number of events

• Each event is a logical expression

• Event expressions are evaluated in order until one is TRUE

• If none is TRUE, the “None Of The Above” event is triggered

• A State is a combination of state-variable events (State Table row)

ICT Control System Workbench MEL

• Controller Verification

– Controller logic is “syntactically” verified

– Semantic verification is planned

•

• Automatic Code Generation

– C-language code

– LabVIEW Code Interface Node resource

Workshop Slides Page 57

Process Control

(cont.)

S. A. Osella (ICT)
ICT LabVIEW MEL

Page 58 Workshop Slides

Process Control

(cont.)

S. A. Osella (ICT)
C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

Sensors and Controls for

Thermal Spray:

Is there a need?

Christopher C. Berndt

SUNY at Stony Brook

cberndt@notes.cc.sunysb.edu

* NIST presentation 1998

1

C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

The Thermal Spray Market in

1990

Coating applicators &

Job Shops

OEMs

Military

Other

Total

Suppliers of equipment,

systems & materials

Annual Sales ($M)

630 1260

US

410

160

45

15

275

World

900

280

60

20

430

Is this an accurate measurement of the TS Market?

2

Workshop Slides Page 59

Sensors and

Controls for

Thermal Spray:

Is there a need?

C. C. Berndt 

(SUNY Stony Brook)
C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

3 Status of Industry &

Technology

About $M 275-300.

E

q

uip

m

e

nt a

n

d

M

aterials

S

ale

s

$

M

Year

250

200

150

100

50

1920 1940 1960 1980 2000

300

1900

Schoop

invents

process

Ballard's

book

Axline forms Metco

Detonation gun

Aircraft engine

applications

Low-energy plasma

HVOF

Robots

Water-jet stripping

High-energy plasma

Process/

motion

closed-loop

control

New

materials/OEM

applications

C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

The Commercial Future o 4 f

Thermal Spray (November, 1998)

1997 2002 AAGR

Type

Thermal

Spray

CVD

PVD

Other

Total

380

$M

151

38

141

710

53.5

21.3

5.4

19.8

100.0

%

510

218

73

186

987

$M

51.7

22.1

7.4

18.8

100.0

%

6.1

7.6

14.0

5.7

6.8

%

• Source: Business Communications, Norwalk, CT

• AAGR = Average annual growth rate.

• Other = dipping, spraying, sol-gel, laser-assisted.

Note: Only North America

Page 60 Workshop Slides

Sensors and

Controls for

Thermal Spray:

Is there a need?

(cont.)

C. C. Berndt 

(SUNY Stony Brook)
C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

5

Questions & Comments

Thermal spray is being commingled with

the other techniques; e.g., sol-gel, laser.

•

The more important question (which has

not been addressed) is “How many guns /

systems are there in the marketplace? ,

500 or 5,000 units?

”

•

“High technology” is creeping into thermal

spray. What will people be willing to pay for

it?

•

Does control improve the economics of

thermal spray industry?

•

C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

6

Is there Money in “Control/

Sensors”?

Total $'s = $M510

Percent $'s on

Equipment

10

20

30

10

20

30

10

20

30

$M Percent $'s on

Equipment

51

51

51

102

153

102

153

102

153

10

20

30

10

10

20

20

30

30

$M

5.1

10.2

15.3

10.2

15.3

20.4

30.6

30.6

45.9

Total %

1

2

3

2

3

4

6

6

9

Workshop Slides Page 61

Sensors and

Controls for

Thermal Spray:

Is there a need?

(cont.)

C. C. Berndt 

(SUNY Stony Brook)
C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

7

Processes and Markets

Year

1960

1980

2000

Combustion

Wire

Combustion

Flame

15

6

15

15

56

48

Plasma Arc

Wire HVOF

35

11

4

35

28

8 25

“High tech / advanced” methods are taking

a larger market share.

•

C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

8 Control Tools Currently

Available

There are some general commercial

control systems that are available and

that have been proven for thermal

spray.

•

- In-Flight Particle Pyrometer.

- Tecnar DP2000 for measuring

temperature.

- Control Vision.

Who is using these tools - other than

research Institutions?

•

Page 62 Workshop Slides

Sensors and

Controls for

Thermal Spray:

Is there a need?

(cont.)

C. C. Berndt 

(SUNY Stony Brook)
C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

9

Other Control Tools in the

Research Phase.

• Acoustic Emission to measure torch

performance & erosion; hence

implying life and microstructure.

• Laser thickness gages.

• Laser non-destructive methods.

C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

Practicality?

• Is tool development a terminal SBIR

activity?

• Are such tools robust?

• What will these devices enable?

- Higher productivity?

- Better microstructures?

- Processes that would not be

available otherwise?

• Does the work force need an

advanced degree to use 

• Who will pay for control tools /

sensors?

10

Workshop Slides Page 63

Sensors and

Controls for

Thermal Spray:

Is there a need?

(cont.)

C. C. Berndt 

(SUNY Stony Brook)
C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

11

Sensors / Controls that are

Important

• Impact properties (temperature,

velocity, size).

• Residual stress / strain, Elastic

modulus.

• Real time thickness.

• Deposition efficiency.

• Surface roughness.

• A “direct microstructure” sensor.

E.g., porosity

The vital factor is to have these

available in a user-friendly and

economical mode.

C. C.Berndt

STATE UNIVERSITY OF NEW YORK

T NY

12

Conclusions

the thermal spray constituency?

Remember the question:

“Sensors and Controls for Thermal

Spray: Is there a need?”

and

“Will such devices be accepted by

The answer is “YES!”

Page 64 Workshop Slides

Sensors and

Controls for

Thermal Spray:

Is there a need?

(cont.)

C. C. Berndt 

(SUNY Stony Brook)
National Research

Council Canada

Conseil national

de recherches Canada

Sensors for Controlling

Thermal Spray Processes

Christian Moreau and Luc Leblanc

Industrial Materials Institute

Thermal Spray Workshop,

NIST, Gaithersburg, MD

18 November 1998

Outline

Introduction

Sensing Techniques (Processes and Materials)

zone 1: Heat Generation

zone 3: Coating Built-up

Control Strategies

Conclusions

zone 2: Particle Heating and Acceleration

Workshop Slides Page 65

Sensors for

Controlling

Thermal Spray

Processes

C. Moreau (NRCCNRC)
Aerial View of IMI

IMI Mission

Promote the growth and competitiveness of

Canadian industry, through research and

development activities related to materials

processing technologies

Page 66 Workshop Slides

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
IMI - Overview

Created 1978

Moved to Boucherville 1983

Staff 150

of which: Scientific/Eng 75

Technical 50

Budget 17M$

Core Competencies

Materials Behavior

Development and improvement of processes optimizing

microstructure to obtain higher performance materials

Development and experimental validation of process

modeling software

Development and use of optical and ultrasonic sensors for

process and quality control

Workshop Slides Page 67

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Process Instrumentation -

Expertise

Nondestructive characterization

Optical inspection

Ultrasonic techniques

Optimum Process Control

The key physical process variables and key

characteristics of the coating for the application it

is dedicated to must be identified and controlled.

Elements required to control the process:

the optimum value of these process and coating

key variables must be known

the sensors to monitor these characteristics must

be available

the controller must be able to modify the input spray

variables to compensate for any deviations

identified

Page 68 Workshop Slides

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Practical Requirements

In establishing a control strategy, one must take

into account:

costs of the sensors and controllers

ruggedness of the sensors

ease to use

operator training (technical skills)

Diagnostics in Thermal

Spray Processes

Zone 1 Zone 2 Zone 3

Plasma

Powder feeding

Substrate

Coating

Plasma gas

Power supply

- +

Workshop Slides Page 69

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Sensing Techniques

Zone 1: Heat Generation

Spray

Processes

Main input variables

Present state-of-the-art technology based on the

monitoring and control of input variables in Zone 1:

Input energy or net plasma energy

DC plasma

fuel flow rate

oxygen or air flow rate

arc current

arc gas flow rates

HVOF/

flame

Voltage Fluctuations

Time Evolution

At the beginning of the wear experiment

At the end of the wear experiment (40 hours)

Time (ms)

AC voltage (V)

0 5 10 15 20 25 30 35 40

-6

-3

0

3

6

Time (ms)

AC voltage (V)

0 5 10 15 20 25 30 35 40

-6

-3

0

3

6

Page 70 Workshop Slides

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Voltage Fluctuations

Time Evolution of the Root Mean Square

Increase of the

voltage fluctuation

rms value during the

first 20 hours of

spraying. A plateau

was reached for the

remainder of the

wear experiment.

0

Wear Time (hours)

RMS Value (V)

0.0

1.0

2.0

3.0

0.5

1.5

2.5

3.5

10 20 30 40

Voltage Signature Evolution

Three distinct frequency

regions are identified in

the voltage signatures.

During fifty hours of

spraying, regions located

around 8.3 kHz and 12.3

kHz did not evolve

significantly.

However, region located

around 5.0 kHz shifted

significantly.

Spectral intensity (arbitrary units)

49 hours

30 hours

25 hours

18 hours

8 hours

Frequency (kHz)

0 5 10 15 20

0.4 hour

Workshop Slides Page 71

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Monitoring of Fluctuations in

Plasma Spraying

Signals correlated to the arc root movement:

Advantages (specially voltage signatures):

Disadvantages:

voltage fluctuations

acoustic emission

high speed imaging

plasma light intensity fluctuations

easy to implement

low cost

how to react?

Sensing Techniques

Zone 1: Heat Generation

Techniques for monitoring the temperature,

velocity and composition of the hot gas jets:

Difficult to use in a production environment

oxygen sensors

emission spectroscopy

Rayleigh spectroscopy

coherent anti-Stockes spectroscopy (CARS)

enthalpy probe

Page 72 Workshop Slides

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Zone 2: Particle Heating and

Acceleration

The structure and properties of the sprayed

coatings depend directly on the temperature and

velocity of the particles before impact

Various techniques were developed for particle

diagnosis in laboratory:

Concerns:

complexity, fragility, high technical skill and safety

Two-color pyrometry

laser Two-focus

laser Doppler anemometry (phase)

streak camera

Particle Diagnosis Techniques:

Simplified Approaches

Use of the thermal radiation emitted by the hot

particles

Linear camera for monitoring the orientation, width

and intensity of the particle jet

Commercial systems for monitoring the particle

temperature and velocity

Workshop Slides Page 73

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
On-line Particle Monitoring

During Thermal Spraying

DPV2000 Tecnar Automation Ltee

The Sensor Head

two-slit mask

optical fiber lens

bundle sensor

field of view

particle

Page 74 Workshop Slides

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
3DUWLFOH 'LDJQRVLV 6\VWHP

'395333

On-line Monitoring:

Particles:

Particle Jet:

Tem perature

Intensity

Velocity

Orientation

Diameter

W idth

37 hours

-10

-5

0

5

1 0

1 5

-15

2 0

X (m m )

3400

3200

3000

2800

2600

2400 1 0 5 0 -5 -10 -15 -20 Y (mm )

1 hour

3400

3200

3000

2800

2600

2400 1 0 5 0 -5 -10 -15 -20 -10

-5

0

5

1 0

1 5

2 0

X (m m )

Y (mm )

Particle temperature ( C) o

/RQJ 7HUP 6WDELOLW\ RI WKH

3ODVPD 6SUD\ 3URFHVV

Workshop Slides Page 75

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Evolution of Coating

Microstructures

Coatings sprayed after 3 hours and with new spray

parameters are very similar (microstructures, deposition

efficiencies).

After 3 h

Power 30 kW

Deposition efficiency: 55%

New spray parameters

53%

After 37 h

41%

28.5 kW 35 kW

Time Evolution of the Particle

Temperature

Yittria-zirconia powders

Low power plasma (20 kW)

Radial injection of powders

Constant power control

3 Different Torches

GUN 1

Time (hours)

GUN 2

GUN 3

3100

Temperature (°C)

3000

2900

2800

2700

2600

3100

Temperature (°C)

3000

2900

2800

2700

2600

3100

Temperature (°C)

3000

2900

2800

2700

2600

0 10 20 30 40 50 60

Page 76 Workshop Slides

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Zone 3: Coating Build-up

Monitoring of the substrate and coating

temperature during spraying

Characterization of the substrate preparation

Coating characteristics and NDT

Influence on:

Measuring techniques:

the interface quality between lamellae

residual stresses

crack formation

thermal conductivity

elastic modulus, etc.

one- or two-color pyrometry

infrared camera

Substrate and Coating

Temperature

Workshop Slides Page 77

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Various techniques (with and without contact):

Properties to measure:

triangulation

eddy current

thermal wave

laser-ultrasonics

thickness

porosity

defects

Coating Characteristics and

Nondestructive Testing

thermal conductivity

elastic moduli

high resolution camera

Laser Ultrasonic Principle

CO 2laser generating 120 ns duration pulses

No coupling medium

Detection using a Nd:YAG laser (50 μs pulse

 duration)

Ultrasound

Generating laser

Receiving laser

Substrate ZrO 

2 coating

Page 78 Workshop Slides

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Thickness and Propagation

Delay Vs Position

Position (mm)

5 10 15 20 25 30

280

T

hic

k

n

e

s

s ( m) μ

300

320

340

Pro

p

a

g

atio

n

d

ela

y ( s) μ

0.32

0.34

0.36

0.38

Effect of Spray Conditions on

Sound Velocity

2200

1400

V (m/s)

L

V (m/s)

L

2400

1600

2600

1800

2800

Zr1

Zr2

Zr3

Zr4

Gas: Gas:

Subs. T°: Power:

Ar/He Ar/H2 Ar/H2

Zr4

Zr5

2000

Substrate Temperature (°C)

200 300 400

Power (kW)

20 30 40

2200

150-200°C 35 kW

Workshop Slides Page 79

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Ultrasonic Imaging of Artificial

Defects

Fingerprint 20mm φ (delay)

Silicone 22mm φ (amplitude)

Grease 20mm φ (delay)

Laser scanning

Feedback Strategy

Complex task because the process involves many

parameters influencing the coating properties

Monitoring spray parameters is important but

having a means to react to correct any detected

drift is better

Page 80 Workshop Slides

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Control of Particle

Parameters

Ar- 33% He

Yttria-stabilized zirconia

Velocity (m/sec)

current

increase

gas flow

increase 2900

2950

3000

3050

Temperature (°C)

2850

2800

2750

2700

2650

220 240 260 280 300 320

Conclusion

To reach this goal, one needs:

The objective of developing advanced controls is

to produce coatings having the same properties

day after day or whose properties are within a

range of values acceptable for a specific

application

reliable spray equipment

consistent feed materials

adapted sensing and nondestructive evaluation

techniques

efficient controllers

Workshop Slides Page 81

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Conclusion

A good understanding of the physical and

metallurgical processes involved in thermal

spraying is mandatory to implement adequate

control strategy

Controlling key physical parameters during

spraying should make it possible the transport of

the spraying parameters from one booth to

another (booth equivalency)

Page 82 Workshop Slides

Sensors for

Controlling

Thermal Spray

Processes (cont.)

C. Moreau (NRCCNRC)
Effects of The Cold Gas

Boundary Layer on

Arc Fluctuations

1

University of Minnesota

111 Church St. SE

Minneapolis, MN 55455

USA

Z. D uan , K . W ittm ann , J. F. C oudert ,

, and P. F auchais

1

J. Heberlein

2 2

1 2

Thermal Spray Coatings W orkshop

National Institute of Standards and Technology

Gaithersburg, MD 20899

November 19, 1998

2

University of Limoges

123, Avenue Albert-Thomas

Limoges 87060

France

Time (ms)

Voltage (V)

0 0.5 1.0 1.5 2.0

20

100

30

40

50

60

70

80

90

restrike

takeover

steady

Workshop Slides Page 83

Measurement of DC Plasma Arc Fluctuations

J. Heberlein (U. of Minnesota)

Three different arc operating modes in a

DC plasma torch have been identified

through arc voltage measurements, the

restrike mode, the takeover mode and the

steady mode. These operating modes

strongly influence plasma processes such as

plasma spraying. The occurrence of the arc

operating modes depends on the torch

operating parameters, arc current, plasma

gas composition and plasma gas flow rate. 

The end-zone images of the arc inside the

anode-nozzle have been captured by a CCD

video system with a high shutter speed. 

The end-zone images suggest that the

thickness of the cold gas boundary layer

between the arc column and the anode

surface is the most important variable

influencing the arc mode occurrence and

transition.

From the characteristics of the voltage

waveforms, we can define three basic arc

operating modes as shown in this slide. The

first one is called the "restrike" mode,

which is represented with a saw-tooth

shape waveform and a large fluctuating

amplitude. The second one is the

"takeover" mode, which has an

approximately sinusoidal or triangle shape

waveform with a relative low fluctuating

amplitude. With the same torch

configuration and arc gas mixtures, the

restrike mode is related to a high mean

voltage and the takeover mode is related to

a low mean voltage. The last basic mode is

the "steady" mode, which is identified with

a nearly flat profile and mostly a very low

mean voltage.
Time (ms)

0 0.5 1.0 1.5 2.0

Voltage (V)

25

45

29

31

33

35

39

41

43

37

27

Mixed mode A

Mixed mode B

Nozzle wall

Boundary layer

25

254

0 Pixels 167

Page 84 Workshop Slides

Measurement of DC Plasma Arc Fluctuations (cont.)

J. Heberlein (U. of Minnesota)

The voltage traces obtained over an

extensive space of operating parameters

show the arc, in the most cases, not in a

perfectly distinct mode but with a mixed

characteristic. The mixed characteristics

could be a combination of the restrike and

the takeover modes or a combination of

the takeover and the steady modes, as

shown in this slide. In order to present

results easily and clearly, the arc

instability characteristic is assigned a

numerical value (referring to "mode

value") in this presentation. The "restrike"

mode is equal to 2, the "takeover" equal to

1, and the "steady" equal to 0. While a

perfect mode is assigned to an integer, a

decimal number between 0 and 2 specifies

a mixed voltage fluctuation character, i.e.

"mode = 1.2" represents a voltage mode

consisting of an 80% takeover

characteristic and a 20% restrike

characteristic.

This slide shows a typical end-zone

image, and an intensity profile along

the line which crosses the arc column

center. We define the edge of the cold

gas boundary layer to be located at the

point where the intensity is half of the

highest intensity inside the nozzle

channel. Since the radiative energy has

a rapid rise between 3,000 K to 6,000 K

for an argon based plasma, the above

definition will locate the boundary layer

edge at a point where the temperature is

about 4,500 K. The thickness of the

boundary layer, which is the distance from

the edge to the nozzle wall, is then

converted from a number of pixels to a

physical dimension in mm. Since the arc is

in a highly fluctuating state, the thickness

of the boundary layer has been measured

10 times for each individual experimental

condition to obtain an average value

and reduce the error. The position of

the anode attachment is also identified

from the end-zone image if it is

visible.
0

2.0

0.5

1.0

1.5

argon 60 slm

Current (A)

200 900

Mode Value

300 400 500 600 700 800

argon 100 slm

Ar/He 98/20 slm

0

2.0

0.5

1.0

1.5

Current (A)

200 900

Mode Value

300 400 500 600 700 800

argon 60 slm

argon 100 slm

Ar/He 98/20 slm

Workshop Slides Page 85

Measurement of DC Plasma Arc Fluctuations (cont.)

J. Heberlein (U. of Minnesota)

The next two slides show the arc

operating mode varying with the arc

current and the gas flow rate for the

straight and swirling arc gas injections,

respectively. The results are obtained with

argon/helium mixtures. The arc operating

mode values decrease with increasing arc

current, decreasing mass flow rate, and

decreasing secondary gas fraction. For the

arc gas injection with swirling flow, there

is no steady mode (mode = 0) occurring

even with a very large current and a low

gas mass flow rate. This is due to the

tangential component in the swirling flow

which increases the heat transfer from the

arc leading to a constriction of the arc

column, therefore increasing the thickness

of the boundary layer. However, the swirl

flow can also randomize the anode

attachment by introducing a tangential

drag force, which will drive the mode

value close to 1 from both directions, even

with a relative large mean voltage and

fluctuation amplitudes.
0.6

0.8

1.0

1.5

Current (A)

200 900

Thickness of boundary layer (mm)

300 400 500 600 700 800

argon 60 slm

argon 58/20 slm

Ar/He 98/20 slm

0.7

0.9

1.1

1.2

1.3

1.4

a b

Page 86 Workshop Slides

Measurement of DC Plasma Arc Fluctuations (cont.)

J. Heberlein (U. of Minnesota)

These explanations can be confirmed

by results obtained with the end-zone

image observations. This slide shows

the thickness of the cold gas boundary

layer changing with current and gas

flow rate for swirl injection of the arc

gas. The boundary layer thickness 

increases with decreasing current,

increasing gas flow rate and increasing

secondary gas fraction. Although

occurrence and behavior of an electric

breakdown depends on many physical

and chemical factors in the anode

channel, the thickness of the cold gas

boundary layer as we define it using an

approximate temperature value could

be a good indicator for the

characteristics of the arc instability.

 This slide shows two end-zone

images obtained for the arc

operating in a restrike dominant

mode and in a takeover dominant

mode, respectively. The arc

operating in a restrike dominant

mode presents a very clearly

defined anode attachment, while the

arc in a takeover dominant mode

shows a more gradual decline of the

radiation intensity with an

non-distinguishable anode attachment.

This difference in the arc cross-section

clearly indicates the effect of the

thickness variation of the cold gas

boundary layer and the associated

changes in the gas properties. A thin

boundary layer might produce a diffuse

anode attachment rather than a

constricted anode attachment.

Conclusions Three arc operating modes - "restrike", "takeover" and "steady" have been identified and characterized. Their

dependencies on various operating parameters have been presented. The cold gas boundary layer between the arc column and the

anode wall is considered to be the most important variable to influence the arc instability modes. The boundary layer thickness

has been observed and measured with an end-zone imaging system. The arc operating in the restrike mode has a well-defined

anode attachment, while the anode attachment is hardly distinguishable with the takeover and steady modes. The change in

operating parameters which results in a decrease in the thickness of the boundary layer will lead to a change of the arc operating

from a restrike to a takeover mode, then to a steady mode. However, an increasing fraction of the secondary gas, which usually

has a high thermal conductivity, will drag the arc to the restrike mode. The swirl flow component in the arc gas will drive the arc

to takeover-like characteristics.

Acknowledgment This work has been supported in part by NSF through the ERC for Plasma-Aided Manufacturing grant

EEC-8721545 and through an international collaboration grant NSF/INT-9415715.
Workshop Slides Page 87

Enthalpy Probe

M. Boulos (U. of

Sherbrooke)
Page 88 Workshop Slides

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Workshop Slides Page 89

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Page 90 Workshop Slides

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Workshop Slides Page 91

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Page 92 Workshop Slides

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Workshop Slides Page 93

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Page 94 Workshop Slides

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Workshop Slides Page 95

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Page 96 Workshop Slides

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Workshop Slides Page 97

Enthalpy Probe

(cont.)

M. Boulos (U. of

Sherbrooke)
Page 98 Workshop Slides
IMPACT AND SOLIDIFICATION OF

MOLTEN NICKEL DROPLETS

William Hofmeister

Vanderbilt University

Material in this presentation was published in Solidification 1998, 

(eds. S. Marsh, et al., TMS, Warrendale, PA, 1998), entitled

“Observation of Thermal Profiles during Impact and Solidification of 

Nickel Drops,” by W.H. Hofmeister, R.J. Bayuzick, G. Trapaga, 

D.M. Matson, and M.C. Flemings, pp. 375-387. Acknowledgments are

due to NASA Office of Microgravity Sciences, John Lum, Bob Hyers,

Pedro Bastias, James Olive, Prasart Juntawongso, and Alex Altgilbers.

Experiment Schematic

The top chamber is fitted with an

electromagnetic levitation coil, a sample

exchange carrousel, and optical pyrometer. The

tube was evacuated and then backfilled with

400 torr ultra high purity helium gas. After

melting the temperature of the drop in the coil

was regulated by a flow of helium gas over the

sample. When the desired temperature was

achieved, the levitation power was turned off

and the sample allowed to free fall

approximately 3.5 meters to the bottom of the

tube. The catch chamber at the bottom of the

tube was fitted with a 10x10x0.3 cm optically

flat quartz plate at the impact site. Below the

quartz plate a first surface aluminized mirror

was positioned to allow viewing of the splat

interface through an 8 inch viewport at the end

of the catch chamber. The two thermal

imaging systems were positioned to

simultaneously view the splat interface via a

beam splitting cube. A Kodak Ektapro scanning

array camera was operated at 64,000 frames

per second and the HSDA96 parallel tapped

thermal imaging system was operated at

250,000 frames per second. Both systems were

fitted with narrow band pass optical filters

centered at 900 nm, and calibrated for

temperature measurement using a NIST

standard tungsten strip lamp.

Workshop Slides Page 99

Impact and

Solidification of

Molten Nickel

Droplets

W. H. Hofmeister

(Vanderbilt U.)
Superheated Ni splat

This movie is a temperature corrected, colorized

movie of sample 12 which impacted the plate with

175 K superheat. The movie is generated from the

Kodak imager.

Time temperature curves from Kodak imager

(sample 12)

Colored cursors on images correspond to location of time temperature curves.

Page 100 Workshop Slides

Impact and

Solidification of

Molten Nickel

Droplets (cont.)

W. H. Hofmeister

(Vanderbilt U.)
Time

temperature

curves from

HSDA96

thermal imager

(sample 12)

The cooling rates on

contact were as high as

2.0x106 Ks-1. The sample

spread, then undercooled

70 K before solidification

proceeded from the edge

inward.

Splat

at Tm

Sample 8

Kodak imager

slightly

superheated at

impact (<50K)

Workshop Slides Page 101

Impact and

Solidification of

Molten Nickel

Droplets (cont.)

W. H. Hofmeister

(Vanderbilt U.)
Time temperature for Tm splat

Kodak imager (sample 8)

Time temperature for Tm splat

HSDA96 imager

Slight

superheat on

impact.

Negligible

undercooling

before

solidification.

(sample 8)

Page 102 Workshop Slides

Impact and

Solidification of

Molten Nickel

Droplets (cont.)

W. H. Hofmeister

(Vanderbilt U.)
Side view of undercooled splat

Sample 10

100 K

undercooled

Time temperature from HSDA96

Sample 10 was undercooled close to 100K and

solidified on impact with no additional

undercooling.

Workshop Slides Page 103

Impact and

Solidification of

Molten Nickel

Droplets (cont.)

W. H. Hofmeister

(Vanderbilt U.)
Structure of samples 8 and 12

Photos a & c are

from sample 8,

b & d are from

sample 12.

Droplet spreading

Page 104 Workshop Slides

Impact and

Solidification of

Molten Nickel

Droplets (cont.)

W. H. Hofmeister

(Vanderbilt U.)
High Speed Thermal Imaging for

LENS Process Development and Control

http://www.tms.org/pubs/journals/JOM/9907/Hofmeister/Hofmeister-9907.html

An electronic paper on this

subject was published 

in JOM-e under the title:

“Investigation of Solidification 

in the Laser Engineered Net 

Shaping (LENSTM) Process”

by William Hofmeister, Melissa Wert,

John Smugeresky, Joel A. Philliber, 

Michelle Griffith, and Mark Ensz, 

July, 1999. 

T h i s wo rk was sponso red by Sand i a Nat i ona l Labo r a tory, a mu lti p r ogra m l abo r a t o r y

ope r a t ed by Sand i a Co r porat i o n , a Lockheed Mart i n Co mpany , f or the U. S. Depar t ment

o f Energy under contrac t n u mber DE- AC04- 94AL85000 .

Experimental Set-up

Workshop Slides Page 105

Impact and

Solidification of

Molten Nickel

Droplets (cont.)

W. H. Hofmeister

(Vanderbilt U.)
Side view experiments

Movie at 700

frames/sec.

Temperature in K

Isotherm summary from 

five files at two clock speeds

Temperature Profile from

Center of Melt Pool (Coarse Powder)

• SS316 powder,

-100/+325 mesh

• Stable pool over

all powers

• Pool size

increases with

power

• Superheat lower

1550 for coarse powder

1650

1750

1850

1950

0 0.5 1 1.5

Distance from center of pool (mm)

Temperature (K)

410 W

345 W

275 W

200 W

165 W

Page 106 Workshop Slides

Impact and

Solidification of

Molten Nickel

Droplets (cont.)

W. H. Hofmeister

(Vanderbilt U.)
Temperature Gradient from

Center of Melt Pool (Coarse Powder)

• SS316 powder,

-100/+325 mesh

• 275W cooling rate

is two times 

410W cooling rate

outside the melt

pool

Cooling Rate dK/dt (+325/-100 mesh)

0

500

1000

1500

2000

2500

3000

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Distance from center (mm)

Cooling Rate (K/s)

410 W

275 W

200 W

Workshop Slides Page 107

Impact and

Solidification of

Molten Nickel

Droplets (cont.)

W. H. Hofmeister

(Vanderbilt U.)
Page 108 Workshop Slides
NIST Thermal Spray Research Program Page 109

NIST THERMAL SPRAY RESEARCH PROGRAMS

Project Title: Ceramic Coatings Program (see page 13)

Project Title: Sensors and Diagnostics for Thermal Spray Processes

Investigators: S. D. Ridder and F. S. Biancaniello

Objectives:

The primary focus of this project is to develop tools for the measurement and control of process conditions

for thermal spray systems. This includes off-line analysis tools (e.g. high-speed cinematography, imaging

thermography and holography) and real-time sensors suitable for process control. In addition, mathematical

modeling techniques will be used to provide predictive calculations of process variables and product

characteristics. Appropriate process sensors and controls will then be incorporated into an expert system

driven process controller with generic applicability to a wide range of metal processing equipment and

computer platforms.

Technical Description:

The focus of the thermal spray project is the development of measurement tools to provide diagnostic and

control capabilities for the production of reproducible industrially important spray coatings such as ceramicbased Thermal Barrier Coatings (TBC’s) and metallic based diffusion barriers, corrosion protection coatings

and wear reducing layers with predictable properties.

The intended expert-system-driven or intelligent process controller requires the acquisition of an extensive

data base that maps the effects of all the process variables or parameters on the resulting coating

characteristics. Process parameters must be measured, identified as either dependent or independent variables

and reduced using dimensional analysis. A process model must be determined that provides a mapping of the

process parameter space to the resulting coating properties and process efficiency. Finally, a control system is

developed incorporating the process model, sensors and actuators that provides the necessary heuristics and

response time for achieving the product goal. This will ultimately allow US industry to produce the advanced

materials that this process can provide with reliable performance and acceptable cost.

In the NIST thermal spray system, independent programmable manipulators are used to move the plasma

gun, the substrate and the process sensor. These “robots” provide adequate flexibility for the production and

diagnostic monitoring of reproducible coatings on two-dimensional test coupons measuring up to 1 m . High- 2

speed cinematography, schlieren gas flow imaging , multi-exposure laser holography, and high-speed video

cameras are currently available and will be further developed to provide diagnostic tools for thermal spray

systems (see page 23.) A new Near Infra Red (NIR) spectrometer has been purchased and is currently in use

to measure the emission spectrum from the DC plasma thermal spray gun (see page 29.) CFD modeling

capabilities are available to evaluate gas and liquid flow systems. Previous studies of the supersonic flow in

gas/metal atomizers have provided new capabilities that enhance the viscous dissipation model within the

CFD code (see page 47.) This tool could be employed to study fluid flow in thermal spray equipment.

NIST Small Business Innovative Research (SBIR) funds were previously used to develop several of the

diagnostic and sensor systems that are now being applied to thermal spray research. SBIR funds are currently

sponsoring the development of two new sensor technologies for this program. A new Infra-Red (IR) thermal

imaging sensor, currently capable of measuring the temperature of rough, variable emissivity surfaces, is now

being modified to provide, in addition, on-line measurement of particle temperature and velocity (see page

39.) Another SBIR funded project is investigating emissivity and reflectometry sensors. This research is

aimed at providing tools to quantify substrate and coating surface qualities.
Wire Arc Spray (ZMOLD, schlieren optics)

A

gun deposit

C D

B

Page 110 NIST Thermal Spray Research Program

Frame sequence from schlieren strobe video of twin wire arc spray.

Task Outline:

A) Test methods: Develop Tools and Procedures for Reproducible Quantitative Analysis

1) particle size analysis: sonic sieving of thermal spray powder

2) surface roughness measurements: profilometry and confocal metallography

3) adhesion testing: 4-point bend testing of coating and substrate

4) metallography: specimen preparation of thermal spray coatings

B) Diagnostic tools: Develop Tools for Qualitative and Quantitative Measurements of Dynamic Phenomena

1) schlieren strobe video of thermal

spray plumes: evaluate particle flow

and plasma energy fluctuations (see

figure on right)

2) NIR spectrometry: measure spectrum

of thermal spray plasma, use to

calibrate imaging pyrometer and to

study particle chemistry changes in

spray plume

3) modulation reflectometry: develop

technique to evaluate surface quality

before and after coating with thermal

spray

4) thermal imaging of particles and

coatings: validate imaging pyrometer

and use to study thermal spray plume and coating

5) holography to study impact phenomena: provide data for modeling of particle impacts

C) Sensors: Develop Tools for Quantitative Measurements in Real-time for Feedback Control

1) modulation reflectometry: provide real-time sensor for measuring substrate and coating surface

quality

2) thermal imaging of particles and coatings: provide real-time sensor for measuring thermal spray

plume and coating characteristics for use in advanced process control system

3) anode wear sensor: use time resolved voltage and/or acoustic measurements to monitor anode

wear

D) Processing: Develop And/or Implement Thermal Spray Equipment to Provide Reproducible Coatings

1) radial and axial feed DC plasma thermal spray gun

2) wire feed DC arc spray gun

3) RF plasma spray gun

4) process parameter studies: effects of anode wear and powder size on temperature and velocity

and, therefore, coating properties

5) pure DC powered plasma spray: study effect of battery supplied current on plasma characteristics

6) thermal spray process controller: use new sensors to provide feedback information to control

thermal spray gun power, powder feed, and motion. 

E) Modeling: Develop Tools for Modeling Fluid Flow of Thermal Spray Systems

1) CFD of radial and axial powder feed plasma spray guns: study effects of gun geometry, gas

flow rates, arc power, and powder feed rate on particle temperature and velocity

2) CFD of particle impact: provide particle impact diagnostic data for collaborative work with INEL