A machine learning approach for optimal disassembly planning

D. E. Grochowski, Y. Tang

    Research output: Contribution to journalArticlepeer-review

    27 Scopus citations

    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.

    Original languageEnglish (US)
    Pages (from-to)374-383
    Number of pages10
    JournalInternational Journal of Computer Integrated Manufacturing
    Volume22
    Issue number4
    DOIs
    StatePublished - Jan 1 2009

    All Science Journal Classification (ASJC) codes

    • Aerospace Engineering
    • Mechanical Engineering
    • Computer Science Applications
    • Electrical and Electronic Engineering

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