A new neighborhood function for discrete manufacturing process design optimization using generalized hill climbing algorithms

Diane E. Vaughan, Sheldon H. Jacobson, Derek E. Armstrong

Research output: Contribution to journalArticlepeer-review

Abstract

Discrete manufacturing process design optimization can be difficult, due to the large number of manufacturing process design sequences and associated input parameter setting combinations that exist. Generalized hill climbing algorithms have been introduced to address such manufacturing design problems. Initial results with generalized hill climbing algorithms required the manufacturing process design sequence to be fixed, with the generalized hill climbing algorithm used to identify optimal input parameter settings. This paper introduces a new neighborhood function that allows generalized hill climbing algorithms to be used to also identify the optimal discrete manufacturing process design sequence among a set of valid design sequences. The neighborhood function uses a switch function for all the input parameters, hence allows the generalized hill climbing algorithm to simultaneously optimize over both the design sequences and the inputs parameters. Computational results are reported with an integrated blade rotor discrete manufacturing process design problem under study at the Materials Process Design Branch of the Air Force Research Laboratory, Wright Patterson Air Force Base (Dayton, Ohio, USA).

Original languageEnglish (US)
Pages (from-to)164-171
Number of pages8
JournalJournal of Mechanical Design, Transactions of the ASME
Volume122
Issue number2
DOIs
StatePublished - Jun 2000

Keywords

  • Design
  • Hill climbing
  • Manufacturing
  • Optimization
  • Stochastic algorithms

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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