A machine learning approach for optimal disassembly planning

D. E. Grochowski, Y. Tang

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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
Issue number4
StatePublished - Jan 1 2009

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'A machine learning approach for optimal disassembly planning'. Together they form a unique fingerprint.

Cite this