Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718

J. S. Senthilkumaar, P. Selvarani, R. M. Arunachalam

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

The heat-resistant super alloy material like Inconel 718 machining is an inevitable and challenging task even in modern manufacturing processes. This paper describes the genetic algorithm coupled with artificial neural network (ANN) as an intelligent optimization technique for machining parameters optimization of Inconel 718. The machining experiments were conducted based on the design of experiments full-factorial type by varying the cutting speed, feed, and depth of cut as machining parameters against the responses of flank wear and surface roughness. The combined effects of cutting speed, feed, and depth of cut on the performance measures of surface roughness and flank wear were investigated by the analysis of variance. Using these experimental data, the mathematical model and ANN model were developed for constraints and fitness function evaluation in the intelligent optimization process. The optimization results were plotted as Pareto optimal front. Optimal machining parameters were obtained from the Pareto front graph. The confirmation experiments were conducted for the optimal machining parameters, and the betterment has been proved.

Original languageEnglish
Pages (from-to)885-894
Number of pages10
JournalInternational Journal of Advanced Manufacturing Technology
Volume58
Issue number9-12
DOIs
Publication statusPublished - Feb 2012

Keywords

  • ANOVA
  • Artificial neural network
  • Facing
  • Flank wear
  • Inconel 718
  • Optimization
  • Surface roughness
  • Turning

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software
  • Mechanical Engineering

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