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Using manufacturing process representations

Published online by Cambridge University Press:  27 February 2009

Lee A. Becker ,
Ron Bartlett ,
Arno Kinigadner  and
Mark Roy
Lee A. Becker
Affiliation:
Artificial Intelligence Research Group, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.
Ron Bartlett
Affiliation:
Artificial Intelligence Research Group, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.
Arno Kinigadner
Affiliation:
Artificial Intelligence Research Group, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.
Mark Roy
Affiliation:
Digital Equipment Corporation, Hudson, MA 01749, U.S.A.
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Abstract

An MPR is a deep model of a manufacturing process. It is claimed to be easier to acquire than experiential diagnostic rules, and the acquisition of an MPR can be done either by a knowledge engineer or an intelligent interrogator program. An MPR can be used for simulating processes, for centralized or distributed model-based diagnosis of problems with processes, for designing the processes themselves, for determining the need for quality control testing and sensor checks, for determining when knowledge about the process is incomplete and additional knowledge needs to be acquired, and for compiling diagnostic rules.

Information

Type
Research Article
Information
AI EDAM , Volume 3 , Issue 1 , February 1989 , pp. 23 - 34
Copyright
Copyright © Cambridge University Press 1989

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References

Abrett, G. and Burstein, M. H. 1986. The KREME knowledge-editing environment. Proceedings of the Knowledge Acquisition for Knowledge-based Systems Workshop, pp. 1–0–1–19.Google Scholar
Aneke, N. A. G. and Carrie, A. S. 1984. A comprehensive flowline classification scheme. International Journal of Production Research 22, 281–297.CrossRefGoogle Scholar
Becker, L. A. and Kinigadner, A. 1987. Distributed diagnosis of execution errors in a manufacturing system. In: Sriram, D. and Adey, R. A., Eds Knowledge Based Systems for Engineering: Classification, Education and Control. Boston, MA: Computational Mechanics Publications, pp. 83–93.Google Scholar
Becker, L. A., Bartlett, R. and Roy, M. 1988. Compiling diagnostic rules from a manufacturing process representation. In: Gero, J., Ed. Artificial Intelligence in Engineering: Diagnosis and Learning. Southampton: Computational Mechanics Publications, pp. 285–305.Google Scholar
Bobrow, D. G. (Ed.) 1985. Qualitative Reasoning about Physical Systems. Cambridge, MA: The MIT Press.Google Scholar
Chandrasekaran, B. and Milne, R. (Eds) 1985. Special section on reasoning about structure, behavior, and function. SIGART Newsletter, No. 93, 5–54.Google Scholar
Davis, R. 1980. Applications of meta-level knowledge to the construction, maintenance and use of large knowledge bases. In: Davis, R. and Lenat, D. B. (Eds) Knowledge-based Systems in Artificial Intelligence. New York, NY: McGraw-Hill.Google Scholar
Davis, R. 1984. Diagnostic reasoning based on structure and behavior. Artificial Intelligence 24, 347–410.CrossRefGoogle Scholar
Fikes, R. E. and Nilsson, N. J. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2, 189–208.CrossRefGoogle Scholar
Forbus, K. D. 1984. Qualitative process theory. Artificial Intelligence 24, 85–168.CrossRefGoogle Scholar
Gini, M. and Gini, G. 1983. Towards automatic error recovery in robot programs. IJCAI 8, 2, 821–823.Google Scholar
Gomez, F. and Chandrasekaran, B. 1981. Knowledge organization and distribution for medical diagnosis. IEEE Transactions on Systems, Man, and Cybernetics SMC-11, 34–42.CrossRefGoogle Scholar
Harmon, P. and King, D. 1985. Expert Systems: Artificial Intelligence in Business. New York, NY: John Wiley.Google Scholar
Hayes-Roth, F.Waterman, D. A. and Lenat, D. B. (Eds.) 1983. Building Expert Systems. Reading, MA: Addison-Wesley.Google Scholar
Komoda, N., Kera, K. and Kubo, T. 1984. An autonomous, decentralized control system for factory automation. IEEE Computer 19, 73–83.CrossRefGoogle Scholar
Kuipers, B. 1986. Qualitative simulation. Artificial Intelligence 29, 289–338.CrossRefGoogle Scholar
Michalski, R. S., Mozetic, I., Hong, J. and Lavrac, N. 1986. The multi-purpose learning system AQ15 and its testing application to three medical domains. Proceedings of AAAI-86 2, 1041–1045.Google Scholar
Mohammed, J. and Simmons, R. 1986. Qualitative simulation of semiconductor fabrication. Proceedings of AAAI-86, 2, 794–799.Google Scholar
Pan, J. Y-C. and Tennenbaum, J. M. 1986. PIES: an engineer’s do-it-yourself system for interpretation of parametric test data. AI Magazine 7, No. 4, 62–69.Google Scholar
Pau, L. F. 1986. Survey of expert systems for fault detection, test generation and maintenance. Expert Systems, 3, 100–111.CrossRefGoogle Scholar
Sembugamoorthy, V. and Chandrasekaran, B. 1986. Functional representation of devices and compilation of diagnostic problem-solving systems. In: J., Kolodner and C., Riesbeck (Eds) Experience, Memory and Reasoning, pp. 47–73. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Simpson, J., Hocken, R. and Albus, J. 1982. The automated manufacturing research facility of the National Bureau of Standards. Journal of Manufacturing Systems 1, 201–215.CrossRefGoogle Scholar
Srivinas, S. 1978. Error recovery in robots through failure reason analysis. AFIPS NCC 1978, pp. 275–282.Google Scholar
Struss, P. 1987. Multiple representation of structure and function. In: Gero, J. S. (ed.) Expert Systems in Computer-Aided Design Amsterdam: North-Holland.Google Scholar