Title: Machining of aluminium-based metal matrix composite - a particle swarm optimisation approach

Authors: Diptikanta Das; Vivek Chakraborty; Bijaya Bijeta Nayak; Mantra Prasad Satpathy; Chandrika Samal

Addresses: School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar-751024, Odisha, India ' School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar-751024, Odisha, India ' School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar-751024, Odisha, India ' School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar-751024, Odisha, India ' Department of Mechanical Engineering, GITA, Bhubaneswar-752054, Odisha, India

Abstract: Machining performance of 5 wt.% silicon carbide particulate reinforced Al 7075 matrix composite was investigated in terms of cutting tool temperature (T), average surface roughness (Ra) and tool flank wear (VBc) during turning in pollution-free air water spray cooling environment. Metal was removed by multiple layers of TiN coated carbide inserts during turning. Nonlinear regression models were developed and their adequacies were verified. Significance of process parameters on the responses was investigated through analysis of variance. The responses were optimized individually using Taguchi technique and then simultaneously through particle swarm optimisation technique. The proposed multi-objective algorithm outperformed the traditional Taguchi approach and effectively resulted to a group of non-dominated solutions. Pareto optimal fronts were compiled and plotted for T, Ra and VBc, which can be selected according to the production requirements.

Keywords: metal matrix composite; MMC; turning; regression; analysis of variance; particle swarm optimisation.

DOI: 10.1504/IJMMM.2020.104013

International Journal of Machining and Machinability of Materials, 2020 Vol.22 No.1, pp.79 - 97

Received: 12 Jun 2018
Accepted: 31 Dec 2018
Published online: 06 Dec 2019 *

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