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 journalArticlepeer-review

11 Scopus citations

Abstract

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
Volume35
Issue number1-2
DOIs
StatePublished - May 2009

All Science Journal Classification (ASJC) codes

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

Keywords

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

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