NDLI: A Learning Strategy for Disassembly Process Planning

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A Learning Strategy for Disassembly Process Planning

Content Provider	IEEE Xplore Digital Library
Author	Grochowski, D.E. Ying Tang
Copyright Year	2007
Description	Author affiliation: Rowan Univ., Glassboro (Grochowski, D.E.; Ying Tang)
Abstract	To aid in the disassembly process of various obsolete products, expert systems can be used to model the process and provide valuable insight pertaining to the decisions made within the process. This paper discusses such a model that integrates a Disassembly Petri Net (DPN) with a Hybrid Bayesian Network (HBN). The rationale for this framework as well as its construction is presented in detail. With the incorporation of human factors and condition of disassembled units, this model proves to be more applicable to real industry setting. The suggestions for parameter learning are also discussed, allowing for the BN to give better results when many products have been disassembled.
Starting Page	489
Ending Page	494
File Size	2885502
Page Count	6
File Format	PDF
ISBN	1424410754
DOI	10.1109/ICNSC.2007.372827
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-15
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Process planning Bayesian methods Uncertainty Parameter estimation Expert systems Human factors Construction industry High level languages Equations Machine learning
Content Type	Text
Resource Type	Article

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