Generalized part family formation using neural network techniques

Y. B. Moon, S. C. Chi

Research output: Contribution to journalArticle

90 Scopus citations

Abstract

The spontaneous generalization capability of neural network models has been exploited for solving the part family formation problem. Our approach combines the useful capacities of the neural network technique with the flexibility of the similarity coefficient method. Notably, the constraint satisfaction model of neural networks is adopted. In generalized part family formation, several practical factors such as sequence of operations, lot size, and multiple process plans are also considered. Our approach proves to be highly flexible in satisfying various requirements and efficient for integration with other manufacturing functions.

Original languageEnglish (US)
Pages (from-to)149-159
Number of pages11
JournalJournal of Manufacturing Systems
Volume11
Issue number3
DOIs
StatePublished - 1992

Keywords

  • Constraint Satisfaction
  • Group Technology
  • Neural Networks
  • Part Family Formation

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Hardware and Architecture
  • Industrial and Manufacturing Engineering

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