Hostname: page-component-76fb5796d-9pm4c Total loading time: 0 Render date: 2024-04-29T12:47:48.621Z Has data issue: false hasContentIssue false

Intelligent control of complex materials processes

Published online by Cambridge University Press:  27 February 2009

William J. Pardee
Affiliation:
Rockwell International Center, P.O. Box 1085, Thousand Oaks, CA 91360, U.S.A.
Michael A. Shaff
Affiliation:
Rockwell International Center, P.O. Box 1085, Thousand Oaks, CA 91360, U.S.A.
Barbara Hayes-Roth
Affiliation:
Knowledge System Laboratory, Stanford University, Stanford, CA, U.S.A.

Abstract

A blackboard based intelligent control system has been developed for a family of complex non-equilibrium materials processes. The system is being tested in the laboratory for control of a particular high risk, high value-added step in the manufacture of carbon-carbon composites. The system uses knowledge based methods in several fundamental ways to fill gaps left by control theory and process models. Most notable of these are (1) inferring from indirect measurements and history the process state at multiple, changing levels of abstraction, (2) anticipating problems and planning actions to reach goal (end of process) states, (3) selecting, executing and interpreting approximate models to predict process progression and (4) changing control objectives as the physical situation changes. The system has been demonstrated to substantially reduce processing time.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1990

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abrams, F. L. 1987. An expert system process controller for advanced composites. Proceedings of Detroit ReTec, SPE, pp. 117119. Brookfield Center, CT: Society of Plastics Engineers.Google Scholar
Allen, J. F. 1984. Towards a general theory of action and time. Artificial Intelligence 23, 123154.CrossRefGoogle Scholar
Corkill, D. D., Gallagher, K. Q. and Johnson, P. M. 1987. Achieving flexibility, efficiency, and generality in blackboard architectures. Proceedings of AAAI-87, 1823.Google Scholar
D'Ambrosio, B., Fehling, M. R., Forrest, S., Raulefs, P. and Wilber, B. M. 1987. IEEE Expert, Summer, 8093.Google Scholar
Engelmore, R. S. and Morgan, T. 1988. Blackboard Systems, pp. 122. Reading, MA: Addison-Wesley.Google Scholar
Hayes-Roth, B. 1985. A blackboard architecture for control. Artificial Intelligence Journal 26, 251321.CrossRefGoogle Scholar
Lesser, V. R., Pavlin, J. and Durfee, E. 1988. Al Magazine 9, 4961.Google Scholar
Leclair, S. R. 1986. Sensor Fusion: The Application of Artificial Intelligence to Process Control, Proceedings of Rochester FORTH Conference, pp. 187196.Google Scholar
LeClair, S. R., Abrams, F. L., Lagnese, T. J., Lee, C. W. and Parks, J. B. 1987. Qualitative Process Automation for Autoclave Cure of Composite Parts: AFWAL-TR-87–4083, AFWAL/MLTC, Wright-Patterson Air Force Base, OH.Google Scholar
Mourelatos, A. P. D. 1978. Events, processes and states. Linguistics and Philosophy 2, 415434.CrossRefGoogle Scholar
Nii, H. P. 1986. Blackboard systems: The blackboard model of problem solving and the evolution of blackboard architectures. AI Magazine 7, 3853; 82–106.Google Scholar
Pardee, W. J. and Hayes-Roth, B. 1987. Intelligent Control of Materials Processes, Rockwell Palo Alto Laboratory Technical Report 1.Google Scholar
Pardee, W. J. and Shaff, M. A. 1989. Intelligent real time carbonization control. In: Sohn, H. Y. and Geskin, E. S., Eds: Metallurgical Processes for the Year 2000 and Beyond, 1989 TMS Annual Meeting, pp. 7382. The Metallurgical Society.Google Scholar
Shaft, M. A. 1989. The generic blackboard virtual machine. Proceedings of the Third Annual Workshop on Blackboard Systems, pp. 2131.Google Scholar