Neural network process modelling for turning of steel parts using conventional and wiper inserts

Tugrul Özel, A. Esteves Correia, J. Paulo Davim

Research output: Contribution to journalArticle

11 Scopus citations


In this paper, the effects of insert design in turning of steel parts are presented. Surface finishing has been investigated in finish turning of AISI 1045 steel using conventional and wiper design inserts. Regression models and neural network models are developed for predicting surface roughness, mean force and cutting power. Experimental results indicate that lower surface roughness values are attainable with wiper tools. Neural network based predictions of surface roughness are carried out and compared with non-training experimental data. These results show that neural network models are suitable for predicting surface roughness patterns for a range of cutting conditions in turning.

Original languageEnglish (US)
Pages (from-to)246-258
Number of pages13
JournalInternational Journal of Materials and Product Technology
Issue number1-2
Publication statusPublished - May 1 2009


All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


  • Cutting forces
  • Neural network models
  • Regression models
  • Turning
  • Wiper inserts
  • surface roughness

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