Boundary defect recognition using neural networks

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9 Scopus citations

Abstract

This research presents schemes for automated visual inspection for boundary defects and classification using neural networks. An efficient method for representing circular boundaries is proposed utilizing a curvature and circular fitting algorithm. For classification, two types of neural network modelling schemes are established. First, a multi-layer perceptron is discussed for defect classification problems. Second, a Hopfield network is modelled to be used for continuous-type variables by a minimizing energy function. Extensive tests are conducted on the casting parts, then the results of neural networks are compared with those of traditional pattern classifiers.

Original languageEnglish (US)
Pages (from-to)2397-2412
Number of pages16
JournalInternational Journal of Production Research
Volume35
Issue number9
DOIs
StatePublished - Jan 1 1997

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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