PROCASE: A Case-Based Process Planning System for Machining of Rotational Parts
Abstract
This paper introduces a case-based process planning system PROCASE which generates new process routines through learning from existing process routines. In contrast to traditional rule-based systems, the process planning knowledge of the PROCASE is represented in terms of cases instead of production rules. The planning basically comprises case retrieving and case adaptation rather than chaining applicable rules together to form process plans. The advantages are, first, the system is cheaper to build as it saves the expense of knowledge acquisition. Second, the system is able to advance its knowledge automatically through planning practice. Third, it is robust, because the reasoning is not based on pattern matching but similarity comparison. PROCASE has three modules: the retriever, the adapter and the simulator. It is supported by a feature-based representation scheme which naturally serves as the case indices for case retrieving and adaptation. The retriever uses a similarity metric to retrieve an old case which is the most similar case, among all old ones, to the new case. The adapter is then activated to adapt the process plan of the retrieved case to fit the needs for the new case. The simulator is used to verify the feasibility of the adapted plan. PROCASE is implemented on a Silicon Graphics IRIS workstation using C++ . An example is given to demonstrate how the process routine is generated by the system proposed by the authors. © 1994 Chapman & Hall.
Recommended Citation
H. Yang et al., "PROCASE: A Case-Based Process Planning System for Machining of Rotational Parts," Journal of Intelligent Manufacturing, vol. 5, no. 6, pp. 411 - 430, Springer, Dec 1994.
The definitive version is available at https://doi.org/10.1007/BF00123660
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
case-based reasoning; feature-based representation; Process planning; rotational parts
International Standard Serial Number (ISSN)
1572-8145; 0956-5515
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Dec 1994