The present study aimed to develop the predictive models of average tool-workpiece interface temperature in hard turning of AISI 1060 steels by coated carbide insert. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to predict the temperature in respect of cutting speed, feed rate and material hardness. The number and orientation of the experimental trials, conducted in both dry and high pressure coolant (HPC) nvironments, were planned using full factorial design. The temperature was measured by using the tool-work thermocouple. In RSM model, two quadratic equations of temperature were derived from experimental data. The analysis of variance (ANOVA) and mean absolute percentage error (MAPE) were performed to suffice the adequacy of the models. In ANN model, 80% data were used to train and 20% data were employed for testing. Like RSM, herein, the error analysis was also conducted. | Response surface and neural network based predictive models of cutting temperature in hard turning
