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

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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.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Podhigai College of Engineering and Technology, Tirupattur, Vellore, Tamil Nadu, India

    J. S. Senthilkumaar

  2. Department of Mechanical Engineering, Government Polytechnic College, Dharmapuri, Tamil Nadu, India

    P. Selvarani

  3. Department of Mechanical Engineering, Sona College of Technology, Salem, Tamil Nadu, India

    R. M. Arunachalam

Authors
  1. J. S. Senthilkumaar
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  2. P. Selvarani
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  3. R. M. Arunachalam
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Correspondence to J. S. Senthilkumaar.

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Senthilkumaar, J.S., Selvarani, P. & Arunachalam, R.M. Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718. Int J Adv Manuf Technol 58, 885–894 (2012). https://doi.org/10.1007/s00170-011-3455-7

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  • DOI: https://doi.org/10.1007/s00170-011-3455-7

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