Multi-Objective Optimization of Surface Roughness and Tool Wear in Turning Inconel 718: A Desirability Analysis, Genetic Algorithm and Firefly Algorithm

Shiv Kumar Sharma; Muthumari Chandrasekaran; Ramanathan Thirumalai

doi:10.4028/www.scientific.net/AMM.592-594.545

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 592-594Multi-Objective Optimization of Surface Roughness...

Multi-Objective Optimization of Surface Roughness and Tool Wear in Turning Inconel 718: A Desirability Analysis, Genetic Algorithm and Firefly Algorithm

Abstract:

In this work, a parameter optimization in turning Inconel 718 for multiple performance characteristics has been attempted. The process parameters viz., cutting speed (v), feed (f) and depth of cut (d) is optimized that minimizes surface roughness (R_a) and tool wear (VB). Response surface methodology (RSM) employing CCD experimental design was used to develop predictive model for R_a and VB. The predictive capability of the model provides the average percentage error as 3.87 % and 5.10% for R_a and VB respectively with maximum percentage error limited to 14.67 %. The data are analysed to study the main effect and interaction effects of machining parameters through surface plot. Feed remains dominating factor. Process parameters are optimized for single and multiple objectives using three different techniques viz., statistical and mathematical approach based desirability analysis (DA) and soft computing based genetic algorithm (GA) and firefly algorithm (FA). The results are compared.

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Periodical:

Applied Mechanics and Materials (Volumes 592-594)

Pages:

545-549

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.592-594.545

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Online since:

July 2014

Authors:

Shiv Kumar Sharma, Muthumari Chandrasekaran*, Ramanathan Thirumalai

Keywords:

DA, Firefly Algorithm, GA, Inconel 718, Optimization, RSM

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