Multi-objective optimization of CNC turning parameters using genetic algorithm and performance evaluation of nanocomposite coated carbide inserts

Kumar, M.; Saravanakumar, K.; Balakrishnan, S.; Saravanan, R.

Growing Science » International Journal of Data and Network Science » Multi-objective optimization of CNC turning parameters using genetic algorithm and performance evaluation of nanocomposite coated carbide inserts

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print) Quarterly Publication Volume 2 Issue 4 pp. 99-108 , 2018

Multi-objective optimization of CNC turning parameters using genetic algorithm and performance evaluation of nanocomposite coated carbide inserts Pages 99-108 Download PDF

Authors: M. R. Pratheesh Kumar, K. Saravanakumar, S. Balakrishnan, R. Saravanan

DOI: 10.5267/j.ijdns.2018.9.002

Keywords: Multi-objective optimization, Genetic algorithm, Inconel 600, ANOVA, Coated carbide insert

Abstract: Inconel 600 is a super alloy known for its properties like low thermal conductivity and work hard-ening. The work hardening property of this alloy makes it harder and harder during successive passes of the tool during machining. Therefore, machining of this type of material demands inno-vation in tool material, selection of proper combination of parameters and their levels for economical machining. Coated carbide tool inserts are most widely used for machining Inconel alloys. These inserts are coated with special materials by PVD or CVD technique to reduce flank wear, improve surface finish of machined components and increase the material removal rate (MRR). In this work carbide insert coated with nanocomposite coatings like AlTiN and TiAlSiN commercially known as Hyperlox and HSN2 were used and their performance during machining of Inconel 600 was studied. As improper selection of process parameter influences on the quality of products and productivity, it is important to identify the optimum combination of input process parameters. Most of the time the influence of the input process parameters on the output parameters like MRR, surface roughness and flank wear is studied independently. Information obtained through single objective optimization may not be sufficient because industries desire to optimize all the output parameters, simultaneously. Multi-objective optimization is the only solution to satisfy the requirements of industries and genetic algorithm based multi-objective optimization is adopted in this work in order to get the optimum combination of input process parameters to obtain maximum material removal rate, minimum surface roughness and minimum flank wear simultaneously.

How to cite this paper

 Kumar, M., Saravanakumar, K., Balakrishnan, S & Saravanan, R. (2018). Multi-objective optimization of CNC turning parameters using genetic algorithm and performance evaluation of nanocomposite coated carbide inserts.International Journal of Data and Network Science, 2(4), 99-108.

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