Hybrid neural network and genetic algorithm based machining feature recognition

Ozturk N., Ozturk F.

JOURNAL OF INTELLIGENT MANUFACTURING, vol.15, no.3, pp.287-298, 2004 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 3
  • Publication Date: 2004
  • Doi Number: 10.1023/b:jims.0000026567.63397.d5
  • Journal Name: JOURNAL OF INTELLIGENT MANUFACTURING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.287-298
  • Keywords: feature recognition, neural networks, genetic input selection, MANUFACTURING FEATURES, DESIGN, CLASSIFICATION, SYSTEM, SEARCH, MODEL
  • Bursa Uludag University Affiliated: Yes

Abstract

In this research, neural networks (NNs) and genetic algorithms (GAs) are used together in a hybrid approach to reduce the computational complexity of feature recognition problem. The proposed approach combines the characteristics of evolutionary technique and NN to overcome the shortcomings of feature recognition problem. Consideration is given to reduce the computational complexity of network with specific interest to design the optimum network architecture using GA input selection approach. In order to evaluate the performance of the proposed system, experimental results are compared with previous NN based feature recognition research.