Multi-objective machining parameter optimisation of aluminium alloy 6063 by the Taguchi-artificial neural network/genetic algorithm approach
by Babafemi O. Malomo; Kolawole A. Oladejo; Adebayo A. Fadairo; Olusola A. Oladosu; Temitayo I. Jose
International Journal of Experimental Design and Process Optimisation (IJEDPO), Vol. 6, No. 2, 2019

Abstract: This study investigates the turning of aluminium alloy 6063 to optimise the material removal rate (MRR) and surface roughness (R_a) simultaneously. L₂₇ Taguchi's orthogonal experiments were conducted by incorporating machining parameters of speed (260, 470, 840 rev/min), feed (0.2, 0.3, 0.4 mm/rev) and depth of cut (0.5, 1.0, 1.5). Analysis of variance (ANOVA) and signal-to-noise ratio were applied to determine the optimal control settings and validated by confirmatory tests. The performance characteristics were modelled by second-order regression, artificial neural network (ANN) and genetic algorithm (GA). The results indicate that the optimal conditions for MRR (375 mm³/min) and R_a (1.298 μm) were in agreement with the confirmatory tests. Regression models showed that the optimal points for MRR and R_a can be enhanced by the effect of interactions, but the ANN predicted the experimental data with better accuracy. The GA further elicited a set of optimal solutions for improving machining performance.

Online publication date: Thu, 22-Aug-2019

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