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ORIGINAL PAPER
Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy
1
Laboratory of Mechanics, Chaabet-Ersas Campus, Mechanical Eng. Dept., Université Frères Mentouri, 25000, Constantine -1, Algeria
2
Mechanics and Structures Research Laboratory (LMS), Mechanical Eng. Dept., Université 8 Mai 1945 Guelma, BP 401, 24000, Guelma, Algeria
3
Mechanics of Materials and Industrial Maintenance Research Laboratory (LR3MI), Mechanical Eng. Dept., Badji Mokhtar University, PO Box 12, 23052, Annaba, Algeria
4
Applied Mechanics for New Materials Laboratory (LMANM), Mechanical Eng. Dept., Université 8 Mai 1945 Guelma, BP 401, 24000, Guelma, Algeria
Online publication date: 2022-06-14
Publication date: 2022-06-01
International Journal of Applied Mechanics and Engineering 2022;27(2):124-142
KEYWORDS
ABSTRACT
An experimental investigation is carried out to examine the effects of various cutting parameters on the response criteria when turning EN-AW-1350 aluminum alloy under dry cutting conditions. The experiments related to the analysis of the influence of turning parameters on the surface roughness (Ra) and material removal rate (MRR) were carried out according to the Taguchi L27 orthogonal array (313) approach. The analysis of variance (ANOVA) was applied to characterizing the main elements affecting response parameters. Finally, the desirability function (DP) was applied for a bi-objective optimization of the machining parameters with the objective of achieving a better surface finish (Ra) and a higher productivity (MRR). The results showed that the cutting speed is the most dominant factor affecting Ra followed by the feed rate and the depth of cut. Moreover, the Artificial Neural Network (ANN) approach is found to be more reliable and accurate than its Response Surface methodology (RSM) counterpart in terms of predicting and detecting the non-linearity of the surface roughness and material removal rate mathematical models. ANN provided prediction models with a precision benefit of 8.21% more than those determined by RSM. The latter is easier to use, and provides more information than ANN in terms of the impacts and contributions of the model terms.
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