A study on the optimization of process parameters for enhancing machinability and sustainability in turning of Al 6063 alloy through response surface methodology

An integrated Response Surface Methodology associated with the role of desirability function was employed in this study to predict the machining characteristics and sustainability indicators during the dry turning of Al 6063 alloy. The novelty of the present study is that it evaluates the machining characteristics and sustainable indicators. Experiments were designed concerning the Taguchi L27 orthogonal array. The model's statistical significance was assessed, and the parameters' influences were studied using variance analysis. The optimal parameter set is v1 (200 m/min), f1 (0.05 mm/rev), and d1 (0.25 mm) as determined by response surface methods. Based on RSM optimization, it was discovered that constructed regression models accurately anticipated performance outcomes. At optimal conditions, the dry turning operation reduced surface roughness by 6.83%, cutting force by 59.29%, energy consumption by 72.82%, cutting power by 18.58%, and carbon emission by 72.84%, leading to environment-friendly machining and also production cost. From ANOVA results, it was found that feed rate was identified as a significant parameter for surface roughness at 75.86%, depth of cut as a significant parameter with 45.32% for energy consumption, 44.72% for cutting power, and 45.34% for carbon emission in obtaining the sustainable turning of Al 6063 alloy. Higher R2 values for all output responses show that the mathematical model established is relatively effective in assessing the corresponding response values.