A machine learning approach for optimal disassembly planning

Authors:
D. E. Grochowski;Y. Tang
Affiliations:
Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA;Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA
Venue:
International Journal of Computer Integrated Manufacturing - THE CHALLENGES OF MANUFACTURING IN THE GLOBALLY INTEGRATED ECONOMY. GUEST EDITOR: ROBIN G. QIU
Year:
2009

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Abstract

With the vast amounts of environmental waste being created on a daily basis, many companies are trying to find ways optimally to reuse and recycle obsolete products. Owing to tedious and intensive nature of optimal disassembly planning, expert systems which ease the decision making process are becoming much more prevalent. This paper discusses one such system where a machine learning approach based on a disassembly Petri net (DPN) and a hybrid Bayesian network (HBN) is used. In particular, this method models the disassembly process and predicts the outcome of each disassembly action by examining the probabilistic relationships between the different aspects of the disassembly process. An overall view of the disassembly process and a simple, specific case are provided to illustrate the operation of this expert system.