A neural network model for selecting machining parameters in fixture design

Utpal Roy, Jianmin Liao

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


This paper describes the training and implementation of an artificial neural network model (a multilayer feedforward backpropagated neural network) for the real-time, online estimation of key machining parameters. The neural network model is customized, configured, and trained off-line and then integrated with the automated fixture design (AFD) system for on-line prediction of the machining parameters (according to given workpiece representations and required processing information) as needed in the fixture design synthesis process. The paper reports the implementation of a prototype system and discusses several issues regarding the integration aspects in a case study.

Original languageEnglish (US)
Pages (from-to)149-157
Number of pages9
JournalIntegrated Computer-Aided Engineering
Issue number3
StatePublished - 1996

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Artificial Intelligence


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