Hostname: page-component-76fb5796d-9pm4c Total loading time: 0 Render date: 2024-04-27T22:08:35.927Z Has data issue: false hasContentIssue false

Episodal associative memory approach for sequencing interactive features in process planning

Published online by Cambridge University Press:  27 February 2009

Tim E. Westhoven
Affiliation:
Wright Laboratory, Wright-Patterson Air Force Base, Ohio, OH 45433
C. L. Philip Chen
Affiliation:
Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435
Yoh-Han Pao
Affiliation:
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, U.S.A.
Steven R. LeClair
Affiliation:
Wright Laboratory, Wright-Patterson Air Force Base, Ohio, OH 45433

Abstract

Process planning is the function that converts an engineering design into a manufacturing plan. One of the problems in feature-based process planning is the sequencing of features. Features must be given an order for removal. This order, or sequence, is partially dependent on the geometric relationships between the features. If the geometric relationships between features are such that they dictate a particular sequence, the features are said to have an interaction. Identifying these interactions is an important first step in creating the process plan. An approach to solve this problem using constructive solid geometry operations and the Episodal Associative Memory (EAM) is demonstrated. The EAM is an associative memory that integrates dynamic memory organization and neural computing techniques. The geometric feature relationships can be represented by a pattern. This pattern captures very qualitative information about the geometric positions fo the features. The EAM can organize these patterns into groups of similar geometric relationships. A method for dealing with exceptions, and for retrieving and storing general machining problems associated with interacting features will be described. The system implemented is shown to correctly sequence several types of feature interactions.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1992

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

Carpenter, G. A. and Grossberg, S. 1987. ART2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics, 26, 49194930.CrossRefGoogle Scholar
Chang, T. C. 1990. Expert Process Planning for Manufacturing. Reading, MA: Addison-Wesley, 9497.Google Scholar
Hayes, C. 1990. Machining Planning: A Model of an Expert Level Planning Process. Ph.D. Dissertation, Carnegie Mellon University, December 31, 1990.Google Scholar
Hayes, C. and Wright, P. 1990. Automating process planning: using feature interactions to guide search. Journal of Manufacturing Systems, 8(1) 114.CrossRefGoogle Scholar
Kolodner, J. 1980. Retrieval and Organizational Strategies in Conceptual Memory: A Computer Model. Ph.D. Thesis, Yale University, New Haven, CT.Google Scholar
LeClair, S. R. 1991. The rapid design system: memory-driven feature-based design. Proceedings of the 1991 IEEE Conference on Systems Engineering, pp. 3537.Google Scholar
Minsky, M. 1980. K-lines: a theory of memory. Cognitive Science, 4, 117133.Google Scholar
Nau, D. and Karinthi, R. 1990. Handling feature interactions in concurrent design and manufacturing. Manufacturing International 90, Mar 25–28.Google Scholar
Pao, Y. H. 1989. Adaptive Pattern Recognition and Neural Networks. Reading, MA: Addison-Wesley.Google Scholar
Pao, Y. H., Kambiz, K., Goraya, T. and LeClair, S. R. 1991. A computer-based adaptive associative memory in support of design and planning. Proceedings of the 1991 IEEE Conference on Systems Engineering, pp. 4956.Google Scholar
Pao, Y. H., Komeyli, K., Shei, D., LeClair, S. and Winn, A. 1993. The episodal associative memory: managing manufacturing information on the basis of similarity and associativity. Journal of Intelligent Manufacturing, (in press).Google Scholar
Radack, G. M., Jacobsohn, J. F. and Merat, F. L. 1991. Positioning form features within the rapid design system. Proceedings of the 1991 IEEE Conference on Systems Engineering, pp. 3841.Google Scholar
Schaffer, G. H. 1980. GT via Automated Process Planning, American Machinist, May, 119122.Google Scholar
Schank, R. C. 1982. Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge: Cambridge University Press.Google Scholar
Winn, A. R. 1990. Conceptual Graphs and Semantic Nets Design Knowledge Representation for an Episodal Associative Memory in Computer Aided Manufacturing. Masters Thesis, Dept. of Computer Science and Engineering, Wright State University, Dayton, OH.Google Scholar
Wisdom System Inc. 1990. Concept Modeler Users Manual. Ohio: Pepper Pike.Google Scholar