Article: Multi-objective machining parameter optimisation of aluminium alloy 6063 by the Taguchi-artificial neural network/genetic algorithm approach Journal: International Journal of Experimental Design and Process Optimisation (IJEDPO) 2019 Vol.6 No.2 pp.146 - 166 Abstract: This study investigates the turning of aluminium alloy 6063 to optimise the material removal rate (MRR) and surface roughness (Ra) 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 Ra (1.298 μm) were in agreement with the confirmatory tests. Regression models showed that the optimal points for MRR and Ra 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. Inderscience Publishers - linking academia, business and industry through research

Title: Multi-objective machining parameter optimisation of aluminium alloy 6063 by the Taguchi-artificial neural network/genetic algorithm approach

Authors: Babafemi O. Malomo; Kolawole A. Oladejo; Adebayo A. Fadairo; Olusola A. Oladosu; Temitayo I. Jose

Addresses: Department of Mechanical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria ' Department of Mechanical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria ' Department of Mechanical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria ' Department of Mechanical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria ' Department of Mechanical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

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.

Keywords: machining parameters; material removal rate; MRR; surface roughness; artificial neural network; genetic algorithm.

DOI: 10.1504/IJEDPO.2019.101720

International Journal of Experimental Design and Process Optimisation, 2019 Vol.6 No.2, pp.146 - 166

Received: 17 Oct 2018
Accepted: 01 Apr 2019
Published online: 22 Aug 2019 *

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Title: Multi-objective machining parameter optimisation of aluminium alloy 6063 by the Taguchi-artificial neural network/genetic algorithm approach

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