A learning strategy for disassembly process planning

David E. Grochowski; Ying Tang

doi:10.1109/ICNSC.2007.372827

A learning strategy for disassembly process planning

David E. Grochowski, Ying Tang

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
Pages	489-494
Number of pages	6
DOIs	https://doi.org/10.1109/ICNSC.2007.372827
State	Published - 2007
Event	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 - London, United Kingdom Duration: Apr 15 2007 → Apr 17 2007

Publication series

Name	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

Other

Other	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
Country/Territory	United Kingdom
City	London
Period	4/15/07 → 4/17/07

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Control and Systems Engineering

Access to Document

10.1109/ICNSC.2007.372827

Cite this

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title = "A learning strategy for disassembly process planning",

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.",

author = "Grochowski, {David E.} and Ying Tang",

year = "2007",

doi = "10.1109/ICNSC.2007.372827",

language = "English (US)",

isbn = "1424410762",

series = "2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07",

pages = "489--494",

booktitle = "2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07",

note = "2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 ; Conference date: 15-04-2007 Through 17-04-2007",

}

Grochowski, DE & Tang, Y 2007, A learning strategy for disassembly process planning. in 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07., 4239040, 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07, pp. 489-494, 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07, London, United Kingdom, 4/15/07. https://doi.org/10.1109/ICNSC.2007.372827

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AU - Tang, Ying

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AB - 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.

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DO - 10.1109/ICNSC.2007.372827

M3 - Conference contribution

AN - SCOPUS:34748881776

SN - 1424410762

SN - 9781424410767

T3 - 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

SP - 489

EP - 494

BT - 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

T2 - 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

Y2 - 15 April 2007 through 17 April 2007

ER -

A learning strategy for disassembly process planning

Abstract

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All Science Journal Classification (ASJC) codes

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