Machinability investigation of Chromoly steel using TiN/Al₂O₃/TiCN/TiOCN multilayer coated tungsten carbide tool inserts with artificial neural network methodology

Abstract

Enhancing the machining efficiency of hardened steel using four‐layer coated tungsten carbide tools has gained a lot of interest among manufacturing engineers. This paper reports the machining performance of AISI 4140 steel using TiN/Al2O3/TiCN/TiOCN multilayer coated tungsten carbide tool inserts. The impact of cutting speed, depth of cut, and the nose radius on machining force, tool chip interface temperature, surface roughness, and chip forms was ascertained using full factorial machining experiments and artificial neural network (ANN) methodology. The machining force evaluated using Logistic Sigmoid activation function resulted in the highest root mean square error (RMSE) of 4.076 and regression value of 0.998. The maximum error between the ANN predicted results and experimental results for machining force, cutting temperature and surface roughness was about 2.4%, 5.3%, and 2.07%, respectively. The best architecture for ANN was “3‐6‐4” which had a coefficient of regression value of 0.97. However, in case of chip form, some of the ANN predicted values could not be mapped to ISO standard chip form and hence prediction accuracy of 80% was achieved.