Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy

Abstract An experimental investigation is carried out to examine the effects of various cutting parameters on response criteria when turning EN-AW-1350 aluminum alloy under dry conditions. The experiments related analysis influence surface roughness ( Ra ) and material removal rate MRR were according Taguchi L27 orthogonal array 3 13 approach. variance (ANOVA) was applied characterizing main elements affecting parameters. Finally, desirability function (DP) for a bi-objective optimization machining with objective achieving better finish higher productivity ). results showed that speed most dominant factor followed by feed depth cut. Moreover, Artificial Neural Network (ANN) approach found be more reliable accurate than its Response Surface methodology RSM counterpart in terms predicting detecting non-linearity mathematical models. ANN provided prediction models precision benefit 8.21 % those determined . latter easier use, provides information impacts contributions model terms.