Boundary defect recognition using neural networks

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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
Issue number9
StatePublished - Sep 1997

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Boundary defect recognition using neural networks'. Together they form a unique fingerprint.

Cite this