The Influence of Cutting Parameters on the Surface Hardness in Turning of 6061 Aluminum Alloy

The primary design property necessary to ensure the longevity and durability of manufactured materials is the material hardness. The primary objective of this study was to investigate the effect of cutting parameters, namely feed rate, cutting speed, and depth of cut, on the surface hardness generated during the turning process of aluminum alloy 6061. The turning experiments were conducted using a Taguchi L27 orthogonal array arranged for three-level cutting parameters. The Analysis of Variance (ANOVA) was employed to determine the relative importance of each parameter on surface hardness. Additionally, an Artificial Nural Network (ANN) predictive model using the back-propagation learning algorithm was created to predict surface hardness levels at each level of the cutting parameters. The results revealed that increasing the values of all the turning parameters resulted in an increase in hardness, and it was concluded that the feed rate was the most critical factor (53.41%) in achieving high surface hardness, followed by the depth of cut (27.89%), whereas cutting speed had a lower impact (18.7%). This study also suggests a simple equation for estimating the surface hardness from the cutting parameters. The ANN model could accurately estimate the surface hardness with a coefficient of correlation (R) higher than 0.98 between the predicted and experimental values. The predicted values of hardness by ANN were more precise (R2 =0.973839) than those predicted by ANOVA (R2=0.893).