Title: Multi-objective statistical analysis and optimisation in turning of aluminium matrix particulate composite using genetic algorithms

Authors: Nikolaos A. Fountas; Georgios V. Seretis; Dimitrios E. Manolakos; Christopher G. Provatidis; Nikolaos M. Vaxevanidis

Addresses: Laboratory of Manufacturing Processes and Machine Tools (LMProMaT), Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education (ASPETE), ASPETE Campus, N. Heraklion, GR 14121 Athens, Greece ' School of Mechanical Engineering, National Technical University of Athens (NTUA), Heroon Polytechniou 9, GR 15780 Athens, Greece ' School of Mechanical Engineering, National Technical University of Athens (NTUA), Heroon Polytechniou 9, GR 15780 Athens, Greece ' School of Mechanical Engineering, National Technical University of Athens (NTUA), Heroon Polytechniou 9, GR 15780 Athens, Greece ' Laboratory of Manufacturing Processes and Machine Tools (LMProMaT), Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education (ASPETE), ASPETE Campus, N. Heraklion, GR 14121 Athens, Greece

Abstract: Material removal processes are fundamental manufacturing operations from which high quality parts are produced and see service in a great variety of industrial applications. In this study, a multi-parameter design of experiments, using Taguchi method, has been conducted to investigate the optimum cutting conditions in turning of 316-L stainless steel flakes (SSF) reinforced aluminium matrix. Cutting speed and feed rate were treated as the independent variables in a L9 Taguchi orthogonal array addressing three levels each, while depth of cut was kept constant. Pre-selected quality objectives, reflecting surface quality and process productivity (arithmetic average roughness, Ra and machining time, Tm), were examined. Regression models were formulated to predict the aforementioned quality objectives and taken as a common fitness function for optimization through a genetic algorithm. The results obtained demonstrated that the application of the genetic algorithm used, is quite promising in identifying the optimal process parameters to effectively machine AMPCs.

Keywords: multi-objective optimisation; statistical analysis; aluminium matrix particulate composites; AMPCs; stainless steel flakes; SSF; turning; genetic algorithms.

DOI: 10.1504/IJMMM.2018.093546

International Journal of Machining and Machinability of Materials, 2018 Vol.20 No.3, pp.236 - 251

Available online: 11 Jul 2018 *

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