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NBSIR 87-3544

April, 1987

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

NIST

PUBLICATIONS

A National rorum on

The Future of Automated

Materials Processing

in U.S. Industry

The Role of Process Models,

Artificial Intelligence and

Computer Integration

Sponsored by:

Industrial Research Institute and

White House Office of Science and Technology

Policy, Committee on Materials, Working Group

on Automation of Materials Processing

In Cooperation with:

General Electric Company and

National Bureau of Standards

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Report of

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May 19-20, 1986

Edited by:

Howard M. Bloom

Norman R. Kuchar

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cover One of several possible models of the liquid jet break-up

process that takes place during the production of rapidly

solidified metal powders by the inert gas atomization of

liquid metal. Process models of this kind constitute an important part of the knowledge base which is needed in

the development of an automated materials processing

facility.
A NATIONAL FORUM ON THE FUTURE OF

AUTOMATED MATERIALS PROCESSING

IN U.S. INDUSTRY

The Role of Process Models, Artificial Intelligence

and Computer Integration

Report of Workshop

NATIONAL BUREAU OF STANDARDS

GAITHERSBURG, MARYLAND

May 19-20, 1986

Sponsored by:

Industrial Research Institute and White House Office of Science and Technology Policy

Committee on Materials, Working Group on

Automation of Materials Processing

In cooperation with: General Electric Company

and

National Bureau of Standards

TABLE OF CONTENTS

COMMITTEE ON MATERIALS, AD HOC WORKING GROUP ON AUTOMATION

OF MATERIALS PROCESSING ii WORKSHOP ORGANIZING COMMITTEE iv

INTRODUCTION . 1 WORKSHOP CONCLUSIONS 3

WORKSHOP REPORTS ON PROCESS MODELING AND ARTIFICIAL INTELLIGENCE: USES AND NEEDS 4

LIMITATIONS AND NEEDS OF PROCESS MODELS 5

LIMITATION AND NEEDS OF AI FOR MATERIALS PROCESSING 7

NEEDS FOR PROCESS DEVELOPMENT 9

WORKSHOP ON AI FOR PROCESS PLANNING AND CONTROL 11 NEEDS FOR PROCESS QUALITY ASSURANCE AND DIAGNOSTICS .... 13

SESSION SUMMARY ON PROCESS MODELING AND ARTIFICIAL INTELLIGENCE: USES AND NEEDS 14

WORKSHOP REPORTS ON COMPUTER INTEGRATION ISSUES 16

COMPLEX MATERIALS PROCESSING AS A SYSTEM 17

LINKAGES BETWEEN PROCESS MODELS AND ARTIFICIAL INTELLIGENCE 20

LINKAGES BETWEEN PROCESS MODELS AND ARTIFICIAL INTELLIGENCE 22

SUMMARY OF CONSENSUS VIEWS 24

INDUSTRY 25

UNIVERSITY 27

GOVERNMENT 29

SUMMARY OF CONCLUSIONS RAISED AT THE LAST SESSION 31

APPENDIX A: WORKSHOP PROGRAM 32

APPENDIX B: LIST OF WORKSHOP ATTENDEES 38
COMMITTEE ON MATERIALS, AD HOC WORKING GROUP ON AUTOMATION OF

MATERIALS PROCESSING

Chairman:

Dr. H. Thomas Yolken

Chief, Office of Nondestructive Evaluation

National Bureau of Standards

B344 Materials Building

Gaithersburg, MD 20899

Members:

Dr. Peter Bridenbaugh

Vice President, Research & Development

Aluminum Company of America

Alcoa Technical Center

Alcoa Center, Pennsylvania 15069

Dr. Peter Cannon

Vice President - Research

Rockwell International Science Center

1049 Camino Dos Rios

Thousand Oaks, California 91360

Dr. Robert Mehrabian

Dean, College of Engineering

University of California

Santa Barbara, California 93106

Dr. Bryon Pipes

Dean, College of Engineering

University of Delaware

208 Evans Hall

Newark, Delaware 19711

Mr. Jerome H. Schlensker

Vice President, Manufacturing,

Planning, and Support

Cummins Engine Co., Inc. Mail Code 60202

Box 3005

Columbus, Indiana 47202

Dr. Nam Suh

Assistant Director for Engineering

National Science Foundation

1800 G Street N.W.

Washington, D.C. 20550

li
Dr. Benjamin Wilcox

Assistant Director, Materials

Science Division

DARPA/DSO/MSD

1400 Wilson Boulevard

Arlington, Virginia 22209-2308
WORKSHOP ORGANIZING COMMITTEE

Co-chairmen:

Mr. Howard M. Bloom

Chief, Factory Automation Systems Division

National Bureau of Standards

Building 220, Room A 129

Gaithersburg, MD 20899

Dr. Norman R. Kuchar

Manager, Process Technology Branch

Corporate Research and Development

General Electric Company

1 River Road

Schenectady, NY 12345

Members:

Dr. Marvin Denicoff

Thinking Machines Corporation

245 First Street

Cambridge, MA 02142

Mr. James Dowd

Technical Director, Advanced Manufacturing Technology

Aluminum Company of America

Alcoa Technical Center

Alcoa Center, Pa 15069

Dr. Robert Hartzell

Texas Instruments, Inc.

P. O. Box 225936

Dallas, TX 75265

Dr. Rodney M. Panos

Robotics and Intelligent Machines

Rockwell International Science Center

1049 Camino Dos Rios

P. O. Box 1085

Thousand Oaks, CA 91360

Dr. Phillip A. Parrish

Materials Science Division

Defense Advanced Research Projects Agency

1400 Wilson Boulevard

Arlington, VA 22209

Dr. Andrew Pettifor

Director, Research Programs

Rockwell International Science Center

1049 Camino Dos Rios

Thousand Oaks, CA 91360

IV
Dr. Charles L. Tucker, III Associate Professor, Mechanical Engineering

University of Illinois

144 Mechanical Engineering Building

1206 West Green

Urbana, IL 61801

Dr. Benjamin Wilcox

Assistant Director - Materials Sciences Division

Defense Advanced Research Projects Agency

1400 Wilson Boulevard

Arlington, VA 22209

Dr. Bevan P. E. Wu

Program Manager

Manufacturing Research

IBM Research Division

1530 Page Mill Road

Palo Alto, CA 94304

v

INTRODUCTION

This workshop, "A National Forum on the Future of Automated Materials Processing in U.S. Industry - The Roles of Process Models, Artificial Intelligence and Computer

Integration", was the second of two workshops sponsored by the Industrial Research

Institute and the White House Office of Science and Technology Policy, Committee on

Materials, Working Group on Automation of Materials Processing. The first workshop,

held in December, 1985 was devoted to the role of process sensors.

Together, the workshops addressed several technologies which can play important roles

in improving the competitiveness of U.S. process industries through automation. The

goals of the workshops were threefold: to assess the state of the art in key technologies needed for process automation, including sensors, process modeling, artificial intelligence and computer integration; to identify broad issues and generic needs for

advancing these technologies and applying them in U.S. industry; and to develop

information and recommendations for National direction aimed at enhancing the competitiveness of U.S. process industries.

One of the consequences of the rapid pace of technology is the introduction of a wide

variety of new materials into engineered systems. Composites are replacing metals in

weight sensitive applications; single crystals of electronic and optical materials are being

grown for use in computer chips and solid-state lasers; and rapidly-solidified metal

powders are being compacted into complex shapes.

These new materials may have desirable properties, but often their processing is far

from mature. In many instances, these materials are only one step from the research

laboratory, so that the production facility becomes, in effect, a process development

laboratory. The process data base is sparse; few experts exist; and process sensors and

controls are rudimentary. Not surprisingly, variabilities in the output are common,

leading to problems of low yields, high costs, failures, and long-term lags in the

engineered systems.

But, such problems are not confined to exotic, new materials in advanced systems.

Many of the same problems are found throughout U.S. materials processing industries, in

the old-line, basic industries as well as in the newer, high-tech areas. Too often, steel and castings and diecast aluminum parts contain porosity; the yields of some pressed

glass lamp lenses are disappointingly low; and the pacing factor in getting a new TV

design into production is often the many iterations required to get the molds for the

plastic case to perform adequately. Such problems drive up costs and the reduce the

ability of American industries to compete in both domestic and foreign markets.

The two-day workshop was meant, by its size and structure, to encourage a discussion

of these competitive problems as well as of some potential solutions by representatives

of a broad cross-section of process industries, academic institutions, and government

agencies.

In the workshop, plenary sessions were held each morning; in these sessions, experts

addressed the state-of-the-art and needs in the technical areas of process modeling,

artificial intelligence, and computer integration as applied to materials processing.

Following these presentations, parallel workshops, each focussing on a specific topic and

under the direction of a discussion leader, were held. The discussion leaders then

1
prepared summary reports and recommendations, which were presented in plenary

sessions at the end of each day. At the end of the second day, a panel of representatives from industry, academia and government summarized the consensus views of the

entire workshop.

This report includes the written reports of the workshop discussion leaders and panel

members, as well as a section on workshop conclusions prepared by the workshop co- chairmen.

The workshop had 75 participants, with 49 being from industry, 12 from academia, and

14 from government.

2
WORKSHOP CONCLUSIONS

1. There is a need for enhanced and more standardized interfaces between elements

of a materials processing system: databases, artificial intelligence (AI) tools,

process equipment, process controllers, modeling and simulation tools. There needs

to be a reference model that is generic for materials processing systems.

2. There is a need to educate the manufacturing community of how expert systems

solutions to real world problems have been successfully delivered and implemented.

3. There is a need to clarify the role of AI methods in relationship to the process

model approach and to understand how to select the best method or combination of

methods for a particular problem.

4. There is a need for increased training at the university level to provide future

practitioners in the fields of factory automation, expert systems, and process

modeling.

5. Opportunities for "joint-venturing" and cooperation must be sought and maintained

among industry, government, and academia.

6. Strong emphasis should be placed on the identification of process models as the

first step towards automation.

7. There is a pressing need to close the loop of factory automation by sensors, process models, and control, with artificial intelligence as an agent of implementation.

8. The issue of real-time control of materials processing nef*ds to be addressed from a viewpoint of systems integration and fault-tolerant operation.

Howard M. Bloom

Norman R. Kuchar

3
WORKSHOP REPORTS ON PROCESS MODELING AND ARTIFICIAL INTELLIGENCE:

USES AND NEEDS
LIMITATIONS AND NEEDS OF PROCESS MODELS

Discussion Leader: Charles L. Tucker, III University of Illinois

I. Current Capabilities

A wide variety of materials processing operations can be modeled as problems involving

solid and fluid mechanics, transport of heat and matter and other field problems. This

produces a mathematical model which is a set of partial differential equations. Numerical methods such as the finite element method can solve these sets of equations for

realistic geometries and calculate fields of velocity, temperature, stress, etc. Problems

with free or moving boundaries are much harder to deal with, and three-dimensional

problems can be handled at the cost of substantial computer time. Most computer

models of this type require an expert to set up the problem and interpret the results.

II. Needs

A. Relate Process Model Results to the Properties of the Product.

Some process models predict the microstructure of the finished product or

identify the occurrence of manufacturing defects. These are the exception;

they should become the norm. Research needs to accomplish this are:

1. Gather data for material properties under processing conditions.

(Example: metal solidification requires the heat capacity and thermal

conductivity of the liquid metal, but data is normally available only for

the solid.)

2. Build quantitative models of the effects of processing on material

structure. We often know qualitatively what the effects are (faster

cooling produces finer microstructure; flow aligns polymer molecules,

etc.). Process models demand quantitative relationships which are both

accurate and simple enough to include in calculations. Such models may

be correlations of experimental data or be constructed from theories of

structure development.

3. Construct intelligent postprocessors. It seems possible that AI techniques could aid in interpreting the large amounts of data produced by a process model. This is a relatively difficult AI problem, and the AI

program must incorporate knowledge about both the manufacturing

process and the process model.

B. Connect Process Models to Process Control

Relatively complete process models take longer to compute than the real

process takes to run, and so cannot be used inside a control loop. They can be used to identify viable operating set points for the process. They can also

be used to tell what sensors will be most useful and where to place them.

The research need is:

5
h Develop closed-loop process models. By this we mean process models

which compute fast enough to use inside a control loop. Normally these

would be simplified or extracted from the more complete off-line process

model.

C Make Process Models Into Better Design Tools

Process models not only require experts to use them, they are analysis tools

rather than design tools. One can turn an analysis into a design tool by

making it interactive and inserting a human into the feedback loop. However,

other avenues should be explored:

1. Construct intelligent preprocessors. This software would enable the

casual user to set up runs of process model software. This is a relatively easy AI problem as the domain (a piece of software) is well

defined.

2. Build, design and optimization into process models. There are several

ways one might do this. For example, a sensitivity analysis can easily

be incorporated into a finite element program that uses Newton methods.

This can provide direction as to how to change the parameters to move

in the desired direction. When AI techniques are used instead it will

probably be necessary to redesign the software; one cannot simply "add

AI" to existing programs.

6
LIMITATION AND NEEDS OF AI FOR MATERIALS PROCESSING

Discussion Leader: Donald M. Esterling

George Washington University

I. AI as an Aid to Material Processing

Materials is an inexact science. The relations between process variables and the

resulting and/or underlying microstructure are only incompletely characterized. This can

be an obstacle to the deterministic modeling of processes, but present an opportunity

for the heuristic modeling available through AI techniques.

Classical expert systems approaches should be of value across the entire spectrum of

materials processing including: materials selections, part design, process planning,

scheduling, on-line control, quality control and maintenance. Design and selection

involve accessing and evaluating often large data bases. Process planning, scheduling,

and maintenance involve a number of decision steps, with the requisite knowledge often

vested in relatively few individuals. The sensors used for on-line control may, at times,

provide conflicting data and an interpretation is required. Quality control requires a pass/no pass decision usually in the face of incomplete knowledge. All of these problem

areas can, in principal, be addressed by AI techniques.

There is often a need to make a decision regarding materials processing in a relatively

short time. While a large-scale computational model might provide a satisfactory answer

the compute time may not always correspond to the real time available. A heuristic

model may provide a less exact answer, but one that is available in time to be of use.

The ability of an AI system to acquire new knowledge or learn will be of benefit to existing AI implementations when and as materials processes are changed. The AI

system should assist that updating process.

Much of the evaluation of a materials process and its attendant failure modes requires an analysis of microstructure. The interpretation of the patterns contained in a micrograph requires a skilled materials scientist. This is an interesting areas to investigate the application of AI techniques, but may prove to be too complex for

current methodology.

Finally, just the process of implementing an AI system can be valuable in itself as an

aid to thinking carefully about the problem at hand.

II. AI Needs And Limitations

There was a strong concern regarding the lack of "user-friendliness" in existing AI

systems. Currently they require an expert to create/implement such a system. AI,

which has a goal the facilitating of user interfaces, should consider application of AI

techniques to itself to provide an expert systems shell which does not require special

skills (e.g. in LISP or PROLOG) to implement. The need for such an interface has been

addressed by at least one vendor (Interface Corporation), who was represented.

7
The successful application, particularly beyond the prototype stage, of AI can be very

expensive. One estimate was a sizeable fraction of a million dollars and several manyears. Small to medium sized companies do not have such resources available to commit

to this project, making AI only available to large corporations. While small prototyping can be of manageable expense, the total integration/application onto the factory floor is not inexpensive.

Given the expense associated with implementing AI systems, a metric to gauge AI

performance or enhancement would be of use.

Better methods need to be developed to acquire knowledge from experts. Currently this

is a somewhat inexact science, and successful approaches need to be disseminated.

AI is only one tool. A successful implementation of AI into materials processing

requires an integration of these techniques with the entire materials processing system.

Vendors of AI are encouraged to work closely with the users of AI to achieve this

integration rather than viewing AI as something apart from the system itself. Further,

the users may wish to move from one expert system to another, depending upon the

problem at hand or the degree of satisfaction with the existing system. Interfaces/integration between different experts systems need to be developed to facilitate moving

from one expert system to another.

One should not expect too much from AI. When the user has a very focused problem

and limited knowledge domain, the chances for success are enhanced. AI should not be

viewed as a panacea, but as one important strategic tool that can be applied to materials processing.

8
NEEDS FOR PROCESS DEVELOPMENT

Discussion Leader: Ralph T. Wood

General Electric Company

I. Introduction

A "process" can be defined as a method for reproducible transforming a material from

one form to another (in composition, state, geometrical shape, etc.) so as to achieve a desired result. One of the major problems of the process industries is that many

processes are incompletely understood, so that reproducible results are difficult to obtain. This leads to production problems such as low yields, high levels of rework,

continuing efforts on process redesign, and an inability to scale the process rates or equipment sizes to meet changing production needs.

II. Process Development Today

Today, new processes often have their origins in laboratory experiments, divorced from

the production floor and conducted by scientists and technicians. Success on this

laboratory scale is thought to prove feasibility of a production process.

From the laboratory, the process then is transferred to another technical group,

responsible for pilot scale development. This is a production-oriented task and carries

with it implications for maximizing yield and minimizing costs. However, this process

design is usually divorced from the product which the process will serve.

In many cases, even partial success at the pilot level is the signal to proceed to full production. The scale-up is highly empirical and carries high risk. Usually, many

technical problems remain unsolved, leading to continuing efforts, over many years, to

try to control the process and its output. These efforts are made more difficult by a lack of qualitative and/or quantitative process models which might form a basis for

guiding the work.

Process development as a technical area faces many problems. Traditionally, production

research has represented a relatively small effort, so that basic theoretical and technical

underpinnings are immature or lacking. Generic methods for addressing process

development problems do not exist. There has been little emphasis on achieving a quantitative understanding of the process and its parameters, i.e., through the kind of

models which have served product design so well. Variabilities in the properties of the

initial and processed materials exacerbate process development. The rudimentary nature

of many process sensors makes data collection and analysis difficult.

III. Needs

One of the basic needs of process development is management understanding of the

problem. Management’s focus too often is on the product, but process problems have

significant impact on production cost and quality. Solution of process development

problems can have a major long-term business benefits.

9
On a technical level, process understanding is the key to reducing the duration and

costs of process development. Understanding of the significant process variables and

parameters leads to an understanding of the methods needed to control the process.

Process understanding can be achieved through several technologies, including:

Process models (quantitative and qualitative)

Supporting empirical data for model development and validation

Artificial intelligence (expert systems)

Process sensors

Improved understanding of material properties and their variabilities, for specification and control purposes.

Many of these elements can be combined, together with the knowledge of process

experts, with the aid of presently available expert-system shells to achieve new levels

of process understanding.

Better integration of process design and development with product design and development is also needed. The relationships among the material properties, the process

parameters and control limits, and the product performance must be better understood.

Once process understanding is improved, better process controls can be developed.

Control strategies can be formulated based on the process models and on system control

simulations. Often, multi-variable rather than single-variable controls will be needed.

Despite the need for analytic process models, process development will always require

facilities for process testing. However, pilot facilities must become highly instrumented,

and this instrumentation must be carried forward into the full-scale production facility

if a high level of process monitoring and control is to be achieved.

10
WORKSHOP ON AI FOR PROCESS PLANNING AND CONTROL

Discussion Leader: Dr. Robert L. Moore

LISP Machine Inc.

The workshop was formed by 14 members representing a wide variety of industrial and

government organizations. The general conclusions are summarized as follows:

I. Current Capabilities

On-line expert systems with response time in seconds or longer. Whether these

are real-time is a matter of required response speed in the domain of application.

Single loop and simple multi-variable control.

Adaptive (learning) with parameter identification.

Model-based control in some applications.

Negative trend - process engineering staff in industry have been trimmed from

large companies.

II. Desired Capabilities

Complex multi-variable control.

Adaptive learning - non model based.

Integration of models, sensors and expert systems.

More easily installed expert system tools.

Use of existing computers for expert systems if possible.

Technical bridges between disciplines.

Closed loop expert systems (vs. advice).

Expert systems for sensors (integrity etc.).

Expert systems to adjust models.

A national effort at technology sharing.

III. Where to use Expert Systems

Use models if have good analytical models.

Use expert systems where models are heuristic rather than analytic.

11
S uggestions

marksfdefii^genrtc approaches.^

tandart* S Cooperative effort set guidelines, bench

12
NEEDS FOR PROCESS QUALITY ASSURANCE AND DIAGNOSTICS

Discussion Leader: Robert C. Frimodig

Pratt & Whitney Aircraft

I. Needs

In both basic and advanced materials processing industries, there is substantial need to expand and improve the use of process modeling and artificial intelligence for quality assurance and diagnostics. These needs are expected to grow and will be largely driven

by competitive market forces and increased recognition of the major role that quality

excellence plays in the overall improvement of productivity and profitability.

It was agreed to center discussion on addressing in generic terms, the kinds of general

actions and philosophies relating to the use of process modeling and AI that are needed

to help achieve the dramatic quality improvement desired within U.S. industry. Consensus of the group was that these initiatives must be focused on building quality into the

process such that the resulting part or product is "made right the first time".

II. Uses

While many different aspects of this overall need were discussed, the key areas where

modeling and artificial intelligence will contribute are felt to be:

identification of critical parameters

improved understanding of process and product for entire life cycle,

process control on a "real time" basis

integration of design, manufacturing and vendors

process refinement and optimization

improved diagnostic to predict and prevent system errors, establish preventative

maintenance, etc.

support of sensor technology - application, feedback, calibration systems, and

adaptive control

capacity planning and economic studies

In summary, it was felt that use of process modeling and artificial intelligence technology for quality improvement represents an area where the United States has both the

unique opportunity and the wherewithal to excel. This very important issue deserves

priority and will require collaboration of government, industry and the academic

community.

13
SESSION SUMMARY ON PROCESS MODELING AND ARTIFICIAL

INTELLIGENCE: USES AND NEEDS

Session Chairman: Herbert B. Voelcker

National Science Foundation

I. Current Capabilities

Pose a wide variety of processing problems as solid mechanics, heat transfer, fluid

mechanics, etc. (PDE’s).

Solve on realistic domain shapes

(moving boundaries hardener)

(3-D takes much longer)

Find fields of:

- velocity

- temperature

- stress

- strain

II. Needs

A. Relate process model results to the properties of the product (microstructure,

final shape, defects, etc.).

1. Need better material models (quantitative)

- experimental data at process conditions

- understand micromechanical behavior

- models simple enough to use in calculation

2. Build sensitivity analysis into model

- measure sensitivity of output to inputs

- operate in stable/insensitive region

- possible in current FE techniques

3. Intelligent postprocessing

- interpret reams of data

- difficult AI problem

- must incorporate knowledge built into model (currently hidden in code)

B. Connect Process Models to Process Control

1. Models tell what/where to sense

2. Off-line control - model finds set points, program for control inputs

3. Closed-loop models

- fast enough to go inside loop

14
- usually simplified/extracted from complete (but slow) off-line model

C. Make Models into Better Design Tools

1 . Integrate with CAD (esp. geometry)

2. Intelligent preprocessing

- help set up simulation

- good AI problem: well defined domain

3. Keep models interactive

- develop simpler models

- faster computation

4. Make design/optimization easier

- frame model and develop code with optimization in mind

- best results may require rebuilding software: "can’t just ’add AI’"

15
WORKSHOP REPORTS ON COMPUTER INTEGRATION ISSUES
COMPLEX MATERIALS PROCESSING AS A SYSTEM

Discussion Leader: Lewis D. Roth

Inference Corporation

I. Session Focus and Approach

This workshop was not preceded by a plenum session address on the state of the art. As a result, the initial discussions aimed to focus the workshop on an appropriate

target. The overall focus was on where expert- systems and CIM tie in to what we

already can do in material processing.

"Complex materials processing" meant different things to the workshop participants.

Three points of view surfaced:

A. That complexity referred to the material system; eg. multi-constituent alloys,

micro-alloyed systems, composites, laminates, complex temperature environmental stress-state interactions, meta-stable systems, etc. The technological

issues included: functional structure-property-processing models; database

availability, quality, and interfaces; knowledge representation for utilization by

expert systems.

B. That complexity, was related to the number and type of tasks involved in

designing and developing a unit process, ensemble of processes, or manufacturing cell. The issue of process control was raised. Questions on whether

processes could be controlled purely by heuristics were also advanced. The

ability to merge process model analytical data with heuristic controls also

needs study. The issue of functional knowledge representation was raised

again.

C. That complexity referred to the integration of the technological issues as defined in definitions 1) and 2) with business issues, such as data processing,

and management information systems.

The workshop participants had diverse expertise and backgrounds (aluminum, steel,

composites, electronic components, industrial hardware, plant management, AI tools).

This precluded a focus on problems related to individual material systems or processes.

The ensuing discourse dealt with the complexity issue as described in item 3). Solutions

to issues raised by 1) and 2) were beyond the capability of a short workshop. Nevertheless, they are important and need to be addressed by those specifically knowledgeable

in those technical domains.

II. Where Are We Today?

Material processing systems are uniquely engineered to meet the requirements of the case. There is no generic approach to complex material processing system design. It varies from industry to industry.

17
III. Group Observations. Experience. Thoughts

Whatever "system" proposes a solution to the material processing question, it must

address both business and technological issues and constraints.

No one expects that a single, large expert system could be devised that could encompass

all the possibilities that could be encountered. The emphasis of solving smaller problems and successfully delivering them was endorsed. The concept of a meta- system

(expert- system overseer) was considered to have potential.

No one in the workshop had ever successfully connected an expert system with large

external databases. The integration, communication and interfacing of AI tools to Computer Integrated Manufacturing (CIM) systems was a major concern. Little was

known about how different AI tools approach this problem.

Robust planning and scheduling expert- systems ranging from top management tasks to unit processes was pointed out as having a large immediate benefit to CIM development.

Shop-floor schedules have significant impact on which process, sub-process, or manufacture line is to be used for a particular work order.

It is easier to install new technology and methodologies at green field sites. It was felt that only at green field sites would a top-down approach to implementation of an

integrated business and technological CIM system be viable. It was also felt that this is

still a formidable task even for large organization with a lot of capital. Such an approach may be valid for non-applications specific cases such as a Manufacturing

Automation Protocol (MAP) but not for more specific processes. The bottom up

approach (unit process by unit process) is expected to prevail for the foreseeable future.

IV. What We Would Like To See

A. Connect Shop Floor With Management Information Systems/Data Processing

Management information systems (MIS) and data processing (DP) cannot take

advantage of the huge amount of information available from distributed

computers and computer networks on the shop floor. Conventional programming does not provide enough software flexibility to take advantage of this

information. MIS/DP has little or no integration with software development

capabilities back to MIS/DP, since the AI approach provides for incremental

software development.

B. Designer as Expert Information Managers

Customer design requirements drive product, component, and processes

specifications. Backward integrated approach needs to be considered at the

design phase. Materials processing and material selection are not usually

designed along with part, either due to lack of expertise by the designer, or lack of getting that expert information to the designer in a form that can be

used. The related models, AI tools, simulations, which aid the designer in

dealing with customer requirements need to be applied at the design phase.

The designer is an information manager. Information can be provided through

expert-systems. This information is equally useful to management personnel

as well.

18
C. Shared Databases

Databases are often unavailable. Where they are available, there are questions about qualification of data, database maintenance, and format of data.

AI tools which provide features to access real world databases are needed.

Expert systems will need to take advantage of these databases in real-time.

D. Trained Professionals

Lack of accredited manufacturing systems curricula in universities and job

esteem in industry guarantees future shortage of manufacturing systems

professionals.

V. Needs For Improved Development

Specific models describing trade offs between processes and properties, for different

materials systems. Embedded economic models within process models and vice versa.

Enhanced and more standardized interfaces between and within the following categories: Databases, Database Management Systems, AI Tools and Shells, Process Equipment,

Process Controllers, Modelling and Simulation tools. Additional standards were recom- mended in the area of interfaces for: hardware, software, and communications protocols.

Professionals who have educational background which is synthesis of the above technological and business areas.

Education of manufacturing culture to inform them of how expert system solutions to real world problem have been successfully delivered and implemented. These applications

could be used to ease the transition of the manufacturing culture to new technologies

and approaches. There is a lot of AI technology developed that is not fully exploited

because of wait-and-see attitudes.

19
LINKAGES BETWEEN PROCESS MODELS AND ARTIFICIAL INTELLIGENCE

(Workshop Group I)

Discussion Leader: Daniel E. Whitney

Charles Stark Draper Laboratory

L Summary

Of approximately twenty workshop participants, only two had direct experience implementing AI systems of any kind. Thus the discussion reflected the views of industry

people, the group that might need such systems. These views can be characterized by a mixture of

interest

puzzlement

skepticism

That is, in general the participants felt that before the promise of AI can be realized,

much additional progress must be made. Such progress should include

clarifying and expanding the concept of "process model"

clarifying the role of AI methods in relation to other possible modeling techniques

clearly differentiating AI methods from others

understanding how to select the best method or combination of methods for a

particular problem

The Workshop’s contributions to these challenges are as follows:

II. Possible Definitions of "Process Model" - not mutually exclusive

A. a parameter description of process behavior, usually mathematical, capable of

relating sometimes unmeasurable performance variables to control variables

and disturbances.

B. a description of the process that allows one to trace relationships between

variables and to deduce behavioral consequences of changes in variables.

C. a description of the process that either approximates its true behavior or represents part of it.

Comments: Definition 1 is usually a typical physically based mathematical model.

Definition 2 may be a non-physically based input/output or black box model

containing numerical curve fits as well as quasi-verbal statements or graphical

representations. Definition 3 expresses the fact that no model can be correct or complete. Artificial intelligence is probably included within definition 3.

III. Range of Possible Modeling Techniques

The group identified three main classes of models, each with a set of possible uses:

20
Model Class Possible Uses

Equations, closed form

analytical expressions,

such as partial differential

equations or finite element

models

Product design

Process design

Process control,

including modelin-the-loop

Statistical, experimental,

operations research,

statistical decision theory

Data analysis

Decision support

Strategic planning

Heuristic, approximate,fuzzy Fill gaps in other models and modeling

methods

Save time or money

Help formulate problems

Make interpretations or judgments

Comments: These methods, like the definitions, are not mutually exclusive. When

the available equations are too approximate or too incomplete, they might be

augmented by statistical data or fuzzy set representations of heuristics. For some

situations we have a good model, for others we do not, and for a third class of

problems we are blocked from formulating a traditional model by factors such as

cost, time, cost/benefit, signal/noise, lack of a sensor to make a needed variable

observable, and so on. An important issue is to identify which class a particular

process really belongs to. This will help identify cases where AI or other heuristic

methods can contribute.

IV. Differentiate AI from Other Methods

The group identified the following generic uses for AI methods:

A tool for dealing with heuristics

A way to represent non-numeric ideas

A way to represent discrete, qualitative, or "type" distinctions

A procedure for codifying and solidifying knowledge or identifying gaps in

knowledge

An aid in selecting what model to use

Comments: Other techniques can provide similar services in some cases.

Not discussed deeply but on participants’ minds are several issues common to AI and

other model types. These include:

systematic methods of formulating models

model verification methods

interfacing model types

obtaining real time response

Of these, only the First two are priority, long-term issues. The others are secondary,

will be solved in the course of creating real applications, or should be postponed until

more fundamental issues are resolved.

21
LINKAGES BETWEEN PROCESS MODELS AND ARTIFICIAL INTELLIGENCE

(Workshop Group II)

Discussion Leader: Bevan P. F. Wu

IBM Research Division

L Summary

Presently most micro and macro conventional process models are analytical in nature. They are not built for interactive usage. The data generated by these models usually

requires interpretation from experts to be useful for average users. Furthermore, few

of these models have explicit knowledge built in to help average users in model

applications, such as real time process control which requires reduced form of output

within a specified time interval. The above characteristics of the conventional process

model make the task of establishment of solid correlations between process models and

product properties difficult, if not impossible.

One potential solution to the above mentioned problem is the application of AI technology as a bridge to link the present conventional process models with related knowledge

bases. By doing so one makes the conventional process models " intelligent", i.e., there are explicit knowledge of performing both analytical and synthesis activities in a

relatively easy manner, we are reaching the goal of integrated intelligent process

modeling. The goal of integrated intelligent process modeling appeared to be needed by

the majority of the discussion participants.

The term "bridge" or "linkage" does not have to be a physical item, in many cases it is just a concept. A concept of integrating necessary knowledge with any conventional

process model with both explicit and implicit knowledge. The integrated intelligent

models will have both analytic and synthetic capabilities. There is no need for linkage

between process models and AI.

II. Functional Requirements

For the sake of completeness a set of functional requirements of the linkage between

conventional process models and relevant knowledge bases are given as follows:

A. It is a high speed communication link.

B. It can convert conventional process model output data into easily understandable knowledge for users.

C. It can convert user decisions through models into necessary input data for

conventional process models.

D. A figure of merit for the so called "linkage" should be measured by its knowledge transmission efficiency, KTE, which is defined as

Usable knowledge transmitted in bytes per second

KTE = — - - Total bytes transmitted by the linkage per second

22
E. The AI technology, software and hardware used to achieve the above requirements should be transparent to users.

23
SUMMARY OF CONSENSUS VIEWS
SUMMARY OF CONSENSUS VIEWS: INDUSTRY

Donald E. Koontz

AT&T Bell Laboratories

During the last two days, we have heard excellent talks on a wide range of topics

closely related to factory automation. The Workshop sessions have been demanding of

the participants. The leaders managed to galvanize the diverse backgrounds of ex- periences of the participants into informed, consensus views of the background issues

and problems which need to be addressed.

The reports of this conference, and those developed at the "Sensors for Automated

Materials Processing", University of Santa Barbara, California, December 26-17, 1985,

provide an excellent comprehensive view of the background material and issues which

need to be addressed for increasing the vitality and competitive stance of U.S. Industry

in the national interest.

At this juncture, we, as managers, engineers, academicians and scientists, are in

agreement. The converts are talking to the converts! The consensus view is that there

are many problems. A lot of effort will be required to provide solutions. Everything

looks go! Full speed ahead.

There certainly is an alternate view which must be considered. How will management of

U.S. Industry respond? Most certainly, the response will be mixed. Some companies are moving aggressively to modernize their manufacturing capabilities, many others appear

to be assuming a wait and see posture. Unfortunately, there will be organizations vital to U.S. Industry which will have great difficulties adapting to improved manufacturing

capabilities because of financial limitations or cultural barriers.

One need, therefore, is to take the next step and assess the response of industrial

management to the issues and problems which have been delineated in these two

conferences. It is important for key management personnel to understand the potential

capabilities and benefits versus cost. The potential for improvement includes new

materials, new products, better materials and products, safer materials and products, and

the opportunities to design and manufacture at lower cost to enjoy the benefit of

greater flexibility and bargaining power in the marketplace. The cost will ultimately be

greater than anticipated. It is also important for the same Captains of Industry to consider the costs and penalties of not adapting and improving their manufacturing

postures.

Development and application of process models, process control instrumentations,

sensors, computer technology and artificial intelligence will not be a magic elixir to cure all ailments. These items are tools. They are necessary attributes to wellcontrolled manufacture, but they are not sufficient!

The work at the Bureau on automation is impressive. The initiative on the part of

Bureau personnel to establish IGES, PDES, and TASK as voluntary national and international computer interface standards is laudatory. The posture of the military and

Department of Defense in advocating the use of these voluntary standards by their

25
contractors, and in encouraging contractors to automate and improve manufacturing

capabilities, are a very positive and powerful influence.
SUMMARY OF CONSENSUS VIEWS: UNIVERSITY

Yong W. Kim

Lehigh University

Two days of concentrated discussions, both in formal presentations and in workshop

groups, have brought out several consensus points of significant interest. They are:

I. Strong emphasis is placed on the need to identify and state process models as the

first and foremost important step towards automation of materials processing

factories. A process model means different things depending on the nature of a given processing activity and on the level at which an individual is operating. It may be as simple as a sequence of manufacturing steps in a factory or as complex

as predicting the product performance, such as structural strength, wear and

corrosion resistance, crystalline defect density and other such bulk properties, from

the knowledge of ray material properties and of the thermal, fluid dynamical and

mechanical cycles of materials processing by means of a system of partial differential equations. In whatever form, such a process model serves as a road map

toward automation and helps define the roles of sensors, logistics of control and

the place and level of involvement of artificial intelligence tools.

II. Artificial intelligence, as visualized in the context of factory automation at present, means an expert system assigned to a narrowly defined task which

normally would require one half to two hours of an expert’s time. Implementation

of such an AI device should require about $500K or less. This definition simply is a reflection on the industry’s assessment of the technically feasible and economically viable avenue to factory automation.

III. Artificial intelligence has many niches in the process model- sensors-control triad of

automation, ranging from digestion of vast amounts of empirical process data into

simple rules of thumb, arbitration of conflicting sensor outputs, reductions of

numerical results from process models into simplified look-up tables to control

decision making, system integration and process development.

IV. A strong consensus exists of the need to develop more basic experimental as well

as theoretical data connecting the microscopic properties of materials to bulk

product properties. Especially, physical properties of materials in extreme thermal

states, such as in molten metals, and of those undergoing phase transitions are scarce and yet of critical importance in charting the optimum process control

route.

There are several points of observation and comments that are worth making of the

issue of factory automation and the Forum.

I. Given that the driving force of the two forums, one earlier at UC-Santa Barbara

and the present one at NBS-Gaithersburg, are to give the U.S. industries an advantage in worldwide competition, the questions to ask would be: Are we doing

everything necessary for that edge? What is so unique about the present scenario?

27
II. It is important to recognize American impatience as a key strength of U.S.

industry and take a step back from trying to emulate the mental and cultural

patterns in Japan or other foreign countries. Strong, result-oriented plans should

be put forth for factory automation with three to five year time tables, rather

than seeking much longer term solutions.

III. Artificial intelligence as visualized in the form of expert systems is intrinsically

unsatisfactory in the sense that an expertise is non-linear in character and yet it is being machine-adapted through a linear process of interrogation. The second

generation approach of linear process of interpolation into the expert data base

should provide some improvement. New developments in non-linear dynamics,

fractal geometry, renormalization group and other physics tools of studying nonlinear phenomena need to be brought into the AI research.

IV. Factory automation must succeed and succeed soon, regardless of the future

breakthroughs in the AI research. It is therefore prudent to chart the strategy of

factory automation without being contingent upon any future AI developments.

Artificial intelligence has a certain frantic personality with a large unresolved

agendum, namely, the ability to learn, and the goals which extend far beyond the

scope of factory automation. On the other hand, artificial intelligence has a strong appeal to the talented intellectual audience whose attention is needed for

the successful transformation of the U.S. industries. Universities can contribute

constructively in maintaining a mutually supportive connection between the two

thrusts, provided that the relationship of the industry and government to the

universities is one of upholding each other’s interests. Funding agencies can help

through a hierarchy of sensible funding priorities in this regard.

V. Finally, there is a pressing need to close soon the loop of factory automation by

sensors, process models and control with artificial intelligence as an agent of

implementation. We heard at the Santa Barbara Forum that sensor development

needed good process models and here there are cries for better mathematical

models (and more computing power) for implementation of artificial intelligence.

However, the most successful way of enlarging the body of knowledge and making

progress is to look at the total problem iteratively. Sensors are essential in

process model development and successful models can show how to manage with a minimum number of sensors. The same is true of artificial intelligence and control.

It is therefore more productive to go through the loop many times imperfectly,

rather than postpone the progress until a perfect solution comes about.

28
SUMMARY OF CONSENSUS VIEWS: GOVERNMENT

Ralph P. I. Adler

Army Materials Technology Laboratory

These views and comments regarding topics covered in this Automated Materials

Processing workshop hopefully represent those of my colleagues in the community of

government engineers and scientists with perhaps a slight bias toward DoD interests.

First of all, measured by our interest and the amount of DoD sponsorship, it is obvious

that we are encouraging the utilization of computer augmented methodologies by the

engineering and manufacturing disciplines to increase our industrial processing productivity and product reliability. There are many forms of computer augmentation such as CAD/CAM, high speed computation of complex representations of process models, and

the exploitation of Artificial Intelligence/Expert Systems - AI/ES — (for design,

diagnosis, testing, or control). Whatever the specific approach chosen, the benefit will

result in a better quality, perhaps lower cost product for the consumer. From a national perspective we will markedly increase the competitiveness of our American

manufacturing industry at the same time raising their productive capacity without being

capital intensive.

Our motivation for encouraging computer augmentation is to help all our producers

consistently make better quality products right the first time. With less iterations

during the product development cycle and better process control, less material will be

diverted to the scrap pile; this natural rise in productivity of those plants using

computer augmentation should be readily quantifiable and act as a cost effective

stimulus to the remaining plants to follow suit. The payoff for the DoD is an effectively modernized vendor industry capable of producing more complex, higher quality

components and systems with significantly reduced procurement lead times. This DoDwide thrust intends to develop technology that will be integrated into a Manufacturing

Sciences Program package that has already been started by the Air Force. All this

work will have to be of a high quality and be generic enough for useful technology

exchange. We do not intend to sponsor work to help individual organizations get a leg

up but to provide information and software capable of intra-industry technology

transfer. This is already happening for larger organizations with adequate resources. However we do have some concern as to how to implement similar computer enhancements within smaller companies which lack or have limited access to computer facilities

in order to properly utilize much of this generic technology that is being developed.

Another concept that we are wrestling with is how to best use selected domains of AI

as productivity enhancing tools. From the conversations I have participated in it appears that the ES domain should receive more emphasis since it is most adaptable to our objectives. Furthermore within the ES domain, there is a real question as to what

level of man-machine interface should be located. Should we have a higher level expert

advisor system that might be easier to set up initially; should we have a real expert

system where a well trained but not totally experienced person can be made into a pseudo-expert supported by the expert system? Clearly some tradeoffs will have to be

made. Since it would require less effort to build an expert advisor system, perhaps this

should be the first demonstration phase but with the software architecture being generic

enough for the system to be expandable to all user levels at later phases. Specifically

29
for materials processing we look at ES as a flexible tool for design and control — even

adaptive process control.

Another area that needs addressing is the shortage of qualified personnel especially in

the AI arena. For the long term this must be accomplished by increasing the number of

faculty/departments teaching AI curricula since given the opportunity I believe that

many students would be attracted to this field. For the short term, means of retraining

some of our engineering work force could also be planned by industry. To date both

the Army and the Air Force have already initiated programs to train government

personnel in AI.

Another area that came up here, and with which there is probably a bit of controversy,

is in process modeling. Whether one uses the microscopic or macroscopic approach, we

need to get some resolution soon. The macroscopic approach seems to be working, but

there are still some open questions to be answered. Another related issue is that better

materials property values are needed as parameters in the solution of the analytical

representation of processing systems. Often these values are extracted or even extrapolated from handbooks or must be experimentally determined hopefully, but not always,

by standardized procedures to assure that values are reliable and consistent between

various organizations. Even if people were willing to publish their experimentally

measured property values, at present no common repository exists to store such valuable

information. An independent organization with appropriate instrumentation and using

widely accepted techniques and procedures could fulfill such needed data generation

and/or storage functions. NBS would be a logical choice; there must also be professional societies or trade institutions that could provide these services; if not, DoD

agencies such as mine might be convinced to perform such functions using the precedent

that they already perform analogous services by maintaining engineering standards and

military specifications.

Overall we in the government sector are encouraged by the progress that we see here

both from industry and academia. Despite funding uncertainties we intend to keep on

sponsoring worthwhile Automated Materials Processing Programs with well defined

objectives because we know that such investments will have a high payoff.

30
SUMMARY OF CONCLUSIONS RAISED AT THE LAST SESSION

Eliot Steinberg

Industrial Research Institute, Inc.

The closing session was a presentation of overview comments by a university spokesman,

government spokesman and an industry representative. Respectively, comments were

offered by Dr. Yong W. Kim, Professor and Department Chairman, Physics Department,

Lehigh University; Dr. Ralph Adler, Army Materials Technology Laboratory; and Dr.

Donald Koontz, Head, Chemical Process Control & Automation, AT&T Bell Laboratories.

All of the panelists emphasized the need for training at the university level to provide

future practitioners in the fields of factory automation, expert systems, artificial intelligence and process modeling. It will indeed take a true partnership of each

element — industry, government and academia — if we are to use these new tools to make the U.S more competitive on a global scale. Opportunities for "joint-venturing"

and cooperation must be sought and maintained by the three sectors. The Conference

provided unique opportunities to expose knowledge-gaps and areas needing cooperative

efforts.

31
APPENDIX A: WORKSHOP PROGRAM
WORKSHOP PROGRAM

8:00 am

I

8:45

9:00

9:15

II

9:30

10:15

10:40

Monday, May 19

Registration

National Bureau of Standards

Green Auditorium, Adminstration Building

Coffee/juice/danish

OVERVIEW

Session Chairman:

Theodore H. Hopp

Leader, Machine Intelligence Group

National Bureau of Standards

Welcome/Introduction

George Sinnott

Associate Director, National Engineering Laboratory

National Bureau of Standards

Workshop Goals

Norman R. Kuchar

Manager, Process Technology Branch

General Electric Corporate Research and Development

Summary of Workshop on Process Sensors

H. Thomas Yolken

Chief, Office of Nondestructive Evaluation

National Bureau of Standards

MODELING AND AI FOR MATERIALS PROCESSING: STATE OF THE ART

AND TECHNOLOGY NEEDS

Session Chairman:

Vincent V. Horvath

Manager, Advanced Manufacturing Systems

Bethlehem Steel Corporation

Keynote Address: A User’s View of the Issues and Needs in Materials

Processing Technologies

Peter R. Bridenbaugh

Vice President, Research and Development

Aluminum Company of America

Break

Process Modeling Overview

Robert A. Brown

Professor, Department of Chemical Engineering

Massachusetts Institute of Technology

33
11:15 Artificial Intelligence Overview

Thomas Lozano Perez

Associate Professor, Computer Science

Artificial Intelligence Laboratory

Massaschusetts Institute of Technology

1 1 :50 Process and Tooling Design Overview

Harold Gegel

Senior Scientist, Materials Laboratory

Air Force Wright Aeronautical Laboratories

12:25 Process Control Overview

David Hardt

Professor, Department of Mechanical Engineering

Massachusetts Institute of Technology

1 :00 Lunch

III SIMULTANEOUS WORKSHOPS ON PROCESS MODELS AND AI: USES AND

NEEDS

1:45 Limitations and Needs of AI for Materials

Processing Discussion Leader:

Charles L. Tucker III Associate Professor, Mechanical Engineering

University of Illinois

Limitations and Needs of AI for Materials Processing

Discussion Leader:

Donald Esterling

Professor, Mechanical Engineering

George Washington University

Needs for Process Development

Discussion Leader:

Ralph T. Wood

Manager, Face-Pumped Laser Project

General Electric Corporate Research and Development

Needs for Process Control

Discussion Leader:

Robert Moore

LISP Machine, Inc.

Needs for Process Quality Assurance and Diagnostics

Discussion Leader:

Robert C. Frimodig

Manager, Quality Integration

Pratt & Whitney Aircraft

3:45 Break

34
IV WORKSHOP REPORTS ON PROCESS MODELS AND AI: USES AND NEEDS

Session Chairman:

Herbert B. Voelcker

Deputy Director

National Science Foundation

4:00 Limitations and Needs of Process Models

Limitations and Needs of AI for Material Processing

Needs for Process Development

Needs for Process Control

Needs for Process Quality Assurance and Diagnostics

5:00 Adjourn

7:00 Dinner

35
Tuesday, May 20

V COMPUTERINTEGRATIONISSUESINAUTOMATEDMATERIALSPROCESSING

Session Chairman:

Robert Hartzel

Texas Instruments, Inc.

8:45 Keynote Address: The Automated Manufacturing Research Facility as a Model

for Computer-Integrated Manufacturing

Howard M, Bloom

Chief, Factory Automation Systems Division

National Bureau of Standards

10:05 Break

10:25 Data Exchange in Materials Processing Systems

Bradford M. Smith

National Bureau of Standards

1 1 :00 Linkages Between Process Models and AI

Melvin K. Simmons

Knowledge Based Systems Branch

General Electric Corporate Research and Development

VI SIMULTANEOUS WORKSHOPS ON COMPUTER INTEGRATION ISSUES

1 1:35 Complex Materials Processing as a System

Discussion Leader:

Lewis D. Roth

Inference Corporation

Linkages Between Process Models and AI

Discussion Leader, Workshop Group I Daniel Whitney

Charles Stark Draper Laboratory

Discussion Leader: Workshop Group II Bevan P.F. Wu

Program Manager, Manufacturing Research

IBM Research Division

1:00 Lunch

1 :45 Session VI Continues

VII WORKSHOP REPORTS ON COMPUTER INTEGRATION ISSUES

Session Chairman:

David Rogers

United States Steel Corporation

36
2:35 Complex Materials Processing as a System

Linkages Between Process Models and AI

VIII SUMMARY OF CONSENSUS VIEWS

Session Chairman:

Eliot Steinberg

Industrial Research Institute

3:35 Panel Discussion By Representative From:

Industry

Donald E. Koontz

Head, Chemical Process Control & Automation

AT&T Bell Laboratories

University

Yong W. Kim

Professor and Chairman, Physics Department

Lehigh University

Government

Ralph P.I. Adler

Army Material Technology Laboratory

37
APPENDIX B: LIST OF WORKSHOP ATTENDEES
Ligh Abts, Rexnord Inc. Laszlo Alder, Ohio State University

Ralph P. I. Adler, Army Materials Technology Laboratory

Dan Backman, General Electric Company

George Bimbaum, National Bureau of Standards

Kenneth C. Blaisdell, Johns Hopkins University

Howard M. Bloom, National Bureau of Standards

Irvin M. Bohr, Black & Decker

Peter R. Bridenbaugh, Aluminum Company of America

Robert A. Brown, Massachusetts Institute of Technology

Leo Buchakjian, General Electric Company

James R. Cook, ARMCO Inc. Ron Crowe, Honeywell Inc. Marvin Denicoff, Thinking Machines Corporation

J. Duane Dunlap, Aluminum Company of America

Dayton Edon, LTV Aerospace and Defense Company

Friedrich T. Elliot, LTV Aerospace and Defense Company

Richard K. Elsley, Rockwell International Corp.

Robert W. Elwood, Naval Supply Systems Command

Donald Esterling, George Washington University

Robert Fanning, Aluminum Company of America

James K. Fisher

Robert C. Frimodig, Pratt & Whitney Aircraft

Gary Garitson, Cummins Engine Company

Harold Gegel, Air Force Wright Aeronautical Laboratories

R.G. Gilliland, Aluminum Company of America

Robert E. Green, Jr., Johns Hopkins University

Thomas W. Gurley, Goodyear Tire & Rubber Company

Gary Hagnauer, Army Materials Technology Laboratory

David Hardt, Massachusetts Institute of Technology

Robert Hartzell, Texas Instruments, Inc. Larry Hollingshead, Cincinnati Milacron

Theodore H. Hopp, National Bureau of Standards

Vincent V. Horvath, Bethlehem Steel Corporation

John R. Howell, John Deere and Company

D. F. Hubbard, Carnegie Federal Systems Corporation

L. Hulthage, Camegie-Mellon University

Robert L. Jenkins, David Taylor Naval Ship Research and Development Center

Albert Jones, National Bureau of Standards

Yong W. Kim, Lehigh University

Donald E. Koontz, AT & T Bell Laboratories

Norman R. Kuchar, General Electric Company

Joseph R. Lane, National Materials Advisory Board

Norman Lillybeck, John Deere and Company

Stephen C-Y Lu, University of Illinois

John F. Maguire, General Electric Company

Samar Marwaha, Honeywell Inc. Annop Mathur, Honeywell Inc. Donald McMahon, Allegheny Ludlum Steel Corporation

Ron Miller, Aluminum Company of America

John C. Murphy, Johns Hopkins Applied Physics Laboratory

James. L. Nevins, The Charles Stark Draper Laboratory

39
William T. O’Hara, Air Force Wright Aeronautical Laboratories

Donald H. Petersen, LTV Aerospace and Defense Company

Andrew Pettifor, Rockwell International

David Price, Pratt and Whitney Aircraft

Yapa Rajapakse, Office of Naval Research

C. David Rogers, United States Steel Corporation

Lewis D. Roth, Inference Corporation

Ron Schmid, 3M Company

William Setzer, Aluminum Company of America

Melvin K. Simmons, General Electric Company

Eliot Steinberg, Industrial Research Institute

C. W. Taylor, General Motors Corporation

R. B. Thompson, Iowa State University/Ames Laboratory

Charles L. Tucker III, University of Illinois

Terry Vanderloop, 3M Company

Herbert B. Voelcker, National Science Foundation

Daniel E. Whitney, The Charles Stark Draper Laboratory

B P. Wilkenson, Aluminum Company of America

Richard Williams, United Technologies Research Center

Ralph T. Wood, General Electric Company

William E. Woolam, Southwest Research Institute

Bevan P. F. Wu, International Business Machines

H. Thomas Yolken, National Bureau of Standards

40
NBS-1 14A IREV. 2 -ac) U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

SHEET (See instructions)

1. PUBLICATION OR

REPORT NO.

NBSIR 87-3544

2. Performing Organ. Report No. 3. Publication Date

APRIL 1987

4. TITLE AND SUBTITLE

A National Forum on The Future of Automated Materials Processing in

U.S. Industry

The Role of Process Models, Artificial Intelligence and Computer Integ.

5. AUTHOR(S)

Editors- Howard M. Bloom Norman R. Kuchar

6. PERFORMING ORGANIZATION (If joint or other than NBS, see instructions) 7. Contract/Grant No. NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE 8. Type of Report & Period Covered

WASHINGTON, D.C. 20234

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS ( Street, City. State , ZIP)

Industrial Research Institute

100 Park Ave

New York, NY

10. SUPPLEMENTARY NOTES

| | Document describes a computer program; SF-185. FIPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual Summary of most significant in formation. I f document includes a significant

bi bl iography or literature survey, mention it here) This workshop was the second of two workshops sponsored by the Industri

Research Institute and the White House Office of Science, and Technology

Policy, Committee on Materials, Working Group on Automation of Material

Processing. The first workshop, held in December, 1985 was devoted to

the role of process sensors. Together, the workshops addressed several

technologies which can play important roles in improving the competitiveness of U.S. process industries through automation. The goals of

the workshops were threefold: to assess the state of the art in key

technologies needed for process automation, including sensors, process

modeling, artificial intelligence and computer integration; to identify

broad issues and generic needs for advancing these technologies and

applying them in U.S. industry; and to develop information and recommendations for National direction aimed at enhancing the competitivenes

of U.S. process industries. The workshop was organized to encourage a discussion of competitiveness issues and of potential solutions by

representatives of a broad cross-section of process industries, academi

institutions, and government agencies. This report includes the writte

reports discussion leaders and panel members, together with a section o workshop conclusions prepared by the workshop co-chairmen.

al

li

.1

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolons)

artificial intelligence, automation, competitiveness, computer integrati

materials processing, process models, process industries, r & a policy

13. AVAILABILITY 14. NO. OF

PRINTED PAGES

[~y Uni imited

j ; For Official Distribution. Do Not Release to NTIS 48

H Order From Superintendent of Documents, U.S. Government Printing Office, Washington. D.C. 20402. 15. Price

(X]j Order From National Technical Information Service (NTIS), Springfield, VA. 22161 $11.95

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