Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-04T18:32:09.244Z Has data issue: false hasContentIssue false

Case adaptation in PROCASE: A case-based process planning system for machining of rotational parts

Published online by Cambridge University Press:  27 February 2009

Hao Yang
Affiliation:
Department of Mechanical and Aerospace Engineering, University of Missouri-Rolla, Rolla, MO 65409-0500, U.S.A.
Wen F. Lu
Affiliation:
Department of Mechanical and Aerospace Engineering, University of Missouri-Rolla, Rolla, MO 65409-0500, U.S.A.

Abstract

This paper presents an approach for the case adaptation, especially case repairing, in a case-based process planning system: PROCASE, for machining of rotational parts. In PROCASE, a new process plan is generated by adapting an existing similar process plan from its case library. Case adaptation is a crucial issue in achieving an automated case-based process planning system. This is because, usually, an existing process plan cannot necessarily produce an exact identical part as of the desired part. Adaptation is essential to tailor this existing plan to generate a new process plan for the new part. The case adaptation in this paper comprises case modification, case simulation, and case repairing. The modifier uses the knowledge extracted from case library to edit the retrieved similar plan. The simulator plays an important role in verifying the adapted plan as well as in directing the plan repairing. The repairing rules are indexed by the error messages obtained from the simulation. With the proposed case adaptation, the system will have the capability to repair the erroneous plans to achieve an automated and intelligent process planning system. This paper will first briefly introduce the case representation and case retrieval in PROCASE. Then the rest of the paper is dedicated to the case adaptation.

Type
Articles
Copyright
Copyright © Cambridge University Press 1996

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

REFERENCES

Atling, L., & Zhang, H. (1989). Computer aided process planning: The state-of-the-art. Int. J. Production Res. 27(4), 553585.Google Scholar
Bain, W. (1986). Case-based reasoning: A computer model of subjective assessment. Ph.D. Dissertation, Yale University.Google Scholar
Birnbaum, L., Collins, G., Brand, M., Freed, M., Krulwich, B., & Pryor, L. (1991). A model-based approach to the construction of adaptive case-based planning systems. Proc. Case-based Reasoning Workshop, 215224.Google Scholar
Brandau, R., Lemmon, A., & Lafond, C. (1991). Experience with extended expisodes: Cases with complex temporal structure. Proc. Case-based Reasoning Workshop, 112.Google Scholar
Callan, J.P., Fawcett, T.E., & Rissland, E.L. (1991). Adaptive case-based reasoning. Proc. Case-Based Reasoning Workshop, 179190.Google Scholar
Chen, C.L.P., & Yan, Q-W. (1991). A memory associative approach for assembly planning systems. Proc. Int. Conf. Arti. Neural Networks in Eng., 757762.Google Scholar
Feghhi, S.J. (1989). A learning approach toward integration of design and process planning. Masters Thesis, School of Electrical Engineering, Purdue University.Google Scholar
Geol, A.K., & Zimring, C. (1990). Towards a case-based tool for aiding conceptual design problem solving. Proc. DARPA Workshop on Case-based Reasoning, 109120.Google Scholar
Goodman, M. (1989). CBR in battle planning. Proc. DARPA Workshop on Case-based Reasoning, 363373.Google Scholar
Hammond, K.J. (1989). Case-based planning. Academic Press Inc., New York.CrossRefGoogle Scholar
Hennessy, D., & Hinkle, D. (1991). Initial results from clavier: A case-based autoclave loading assistant. Proc. Case-based Reasoning Workshop, 225232.Google Scholar
Hinrichs, T., & Kolodner, J.L. (1991). The roles of adaptation in case-based design. Proc. Case-Based Reasoning Workshop, 121132.Google Scholar
Hirschberg, D.H. (1975). A linear space algorithm for computing maximal common sub sequence. Commun. ACM 18(6), 341353.CrossRefGoogle Scholar
Kolodner, J.L. (1993). Case-based reasoning. Morgan Kaufmann Publishers, Inc., Los Altos, California.CrossRefGoogle Scholar
Koton, P. (1988). Reasoning about evidence in causal explanation. Proc. AAAI–88, 256261.Google Scholar
Link, C.H. (1976). CAPP, CAM-I Automated process planning system. Proc. the 1976 NC Conf., CAM-1. Inc.Google Scholar
Nilsson, N.J. (1980). Principles of Artificial Intelligence. Tioga Publishing Co., Palo Alto, California.Google Scholar
Pao, Y-H., Komeyli, K., Tanvirand LeClair, S. Goraya(1991). A computerbased adaptive associative memory in support of design and planning.Proc. IEEE Int. Conf. Syst. Eng. 4955.Google Scholar
Pu, P., & Reschberger, M. (1991). Case-based assembly planning. Proc. Case-based Reasoning Workshop, 245254.Google Scholar
Schank, R.C. (1982). Dynamic memory: A theory of learning in computers and people. Cambridge University Press, Cambridge.Google Scholar
Sycara, K., & Chandra, D.N. (1991). Index transformation techniques for facilitation creative use of multiple cases. Proc. 12th Int. Joint Conf. Artif. Intell.Google Scholar
Tsatsoulis, C. (1987) Using dynamic memory structures in planning and its application to manufacturing. Ph.D. Dissertation, Purdue University.Google Scholar
Yang, H. (1994). Intelligent process planning system with learning capability—A case-based reasoning approach. Ph.D. Dissertation, University of Missouri-Rolla.Google Scholar
Yang, H., & Lu, W.F. (1993). PROCASE: A prototype of intelligent case-based process planning system with simulation environment. Comput. Eng., 571577.CrossRefGoogle Scholar
Yang, H., Lu, W.F., & Lin, A. (1994). PROCASE: A case-based process planning system for machining of rotational parts. J. Intell. Manufacturing 5, 411430.CrossRefGoogle Scholar
Zarley, D.K. (1991). A case-based process planner for small assemblies. Proc. Case-based Reasoning Workshop, 363373.Google Scholar