Optimization and Prediction of Parameters in Face Milling of Al-6061 Using Taguchi and ANN Approach

Abstract

In this paper, Taguchi Method has been used to identify the optimal combination of influential factors in the milling process. Milling experiment has been performed on Al 6061 material, according to Taguchi orthogonal array (L16) for various combinations of controllable parameters viz. speed, feed and depth of cut. The surface roughness (Ra) is measured and recorded for each experimental run and analyzed using Taguchi S/N ratios and the optimum controllable parameter combination is identified. An Artificial neural network (ANN) model has been developed and trained with full factorial design experimental data and a combination of control parameters have been found from ANN for the surface roughness (Ra) value, obtained from confirmation test, for the optimum control parameters which are obtained from Taguchi S/N ratios analysis. Taguchi method and ANN found different sets of optimal combinations but the confirmation test revealed that both got almost same Ra values.