A learning strategy for disassembly process planning

David E. Grochowski, Ying Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
Pages489-494
Number of pages6
DOIs
StatePublished - Oct 1 2007
Event2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 - London, United Kingdom
Duration: Apr 15 2007Apr 17 2007

Publication series

Name2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

Other

Other2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
CountryUnited Kingdom
CityLondon
Period4/15/074/17/07

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

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