Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm

M. R. Razfar, M. Asadnia, M. Haghshenas, M. Farahnakian

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

This paper presents an approach to the determination of the optimal cutting parameters to create minimum surface roughness levels in the face milling of X20Cr13 stainless steel. The proposed approach is to use a particle swarm optimization (PSO)-based neural network to create a predictive model for the surface roughness level that is based on experimental data collected on X20Cr13. The optimization problem is then solved using a PSO-based neural network for optimization system (PSONNOS). A good agreement is observed between the predicted surface roughness values and those obtained in experimental measurements performed using the predicted optimal machine settings. The PSONNOS is compared to the genetic algorithm optimized neural network system (GONNS).

Original languageEnglish
Pages (from-to)1645-1653
Number of pages9
JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Volume224
Issue number11
DOIs
Publication statusPublished - 1 Nov 2010
Externally publishedYes

Keywords

  • artificial neural network
  • cutting parameters
  • face milling
  • particle swarm optimization
  • surface roughness

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