Role of temperature and surface finish in predicting tool wear using neural network and design of experiments

Abstract

The present work focuses on the two of the techniques, namely design of experiments and the neural network for predicting tool wear. In the present work, flank wear, surface finish and cutting zone temperature were taken as response (output) variables measured during turning and cutting speed, feed and depth of cut were taken as input parameters. Predictions for all the three response variables were obtained with the help of empirical relation between different responses and input variables using design of experiments (DOE) and also through neural network (NN) program. Predicted values of the responses by both techniques, i.e. DOE and NN were compared with the experimental values and their closeness with the experimental values was determined. Relationship between the surface roughness and the flank wear and also between the temperature and the flank wear were found out for indirect measurement of the flank wear through surface roughness and cutting zone temperature.