Surface Roughness Prediction using Artificial Neural Network in Hard Turning of AISI H13 Steel with Minimal Cutting Fluid Application

Surface roughness plays an important role in manufacturing process and is a factor of great significance in the evaluation of cutting performance. In this paper an attempt was made to develop a model based on Artificial Neural Network to simulate hard turning of AISI H13 steel with minimal cutting fluid application. This model is expected to predict the surface roughness in terms of cutting parameters. Networks with different architecture were trained using a set of training data for a fixed number of cycles and were tested using a set of input / output data reserved for this purpose. The root mean square error was determined for the selected architectures. The model with 3-7-7-1 architecture gave the minimum RMSE value. The ability of ANN model to predict surface roughness (R-a) was analyzed. It was found that the predictions made by the ANN model matched well with the experimental results. (C) 2014 The Authors. Published by Elsevier Ltd.