A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding

S. L. Mok, Chun Kit Kwong, W. S. Lau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

44 Citations (Scopus)

Abstract

Determination of the initial process parameters for injection moulding is a highly skilled task and is based on a skilled operator's "know-how" and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach, In the face of global competition, the current trial-and-error practice is inadequate. In this paper, a hybrid neural network and genetic algorithm approach is described to determine a set of initial process parameters for injection moulding. A hybrid neural network and genetic algorithm system for the determination of initial process parameter settings for injection moulding based on the proposed approach was developed and validated. The preliminary validation test of the system has indicated that the system can determine a set of initial process parameters for injection moulding quickly, from which good quality moulded parts can be produced without relying on experienced moulding personnel.
Original languageEnglish
Pages (from-to)404-409
Number of pages6
JournalInternational Journal of Advanced Manufacturing Technology
Volume18
Issue number6
DOIs
Publication statusPublished - 23 Oct 2001

Keywords

  • Genetic algorithm
  • Hybrid system
  • Initial process parameter setting
  • Injection moulding
  • Neural network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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