Multivariable optimization based on the Taguchi method to study the cutting conditions in aluminum turning

This study aims to determine mathematical models that predict surface roughness and cutting temperature and to select optimal cutting conditions that allow minimum roughness and cutting temperature values to be achieved. The study used the Taguchi method in the dry turning of 2017 A aluminium alloy using a carbide-cutting tool. The Taguchi Method is a technique that aims to achieve efficient and optimal turning results by identifying factors that affect surface quality and efficiency. The tests use a Taguchi L₁₆ orthogonal array with three factors and four levels. The study looks at several cutting conditions, including cutting speed (V_c), feed rate (V_f), and depth of cut (ap). An analysis is conducted to examine how these factors affect surface roughness (R_a) and cutting temperature (T_c) using the signal-to-noise (S/N) ratio, analysis of variance (ANOVA), and regression analysis. The study found that the depth of cut is the most effective cutting parameter, influencing both surface roughness and cutting temperature. In addition, the results also demonstrated the reliability of the developed mathematical models that can predict surface roughness and cutting temperature and select the most favourable cutting parameters.