Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding

S. L. Mok, Chun Kit Kwong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

48 Citations (Scopus)

Abstract

Determination of initial process parameters for injection molding is a highly skilled job and based on skilled operator's "know-how" and intuitive sense acquired through long-term experience rather than a theoretical and analytical approach. Facing with the global competition, the current trial-and-error practice becomes inadequate. In this paper, application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding is described. Artificial neural network was introduced in the case adaptation while fuzzy logic was employed in the case indexing and similarity analysis. A computer-aided system for the determination of initial process parameter setting for injection molding based on the proposed techniques was developed and validated in a simulation environment. The preliminary validation tests of the system have indicated that the system can determine a set of initial process parameters for injection molding quickly without relying on experienced molding personnel, from which good quality molded parts can be produced.
Original languageEnglish
Pages (from-to)165-176
Number of pages12
JournalJournal of Intelligent Manufacturing
Volume13
Issue number3
DOIs
Publication statusPublished - 1 Jun 2002

Keywords

  • Artificial neural network
  • Case-based reasoning
  • Fuzzy logic
  • Initial process parameter setting of injection molding

ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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