Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning

Abstract

This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature.