Prediction and Optimization of Surface Roughness Using Artificial Neural Network and Taguchi Method

Abstract

The surface roughness of a machined workpiece is one of the most important product quality characteristics. It is a technical requirement for mechanical products in most cases but at the same time it is difficult to ensure that the surface characteristic requirement is met. As the functional behaviour of a part greatly depends on the surface quality, which in turn is dependent on numerous uncontrollable factors, it is important to find a precise surface roughness prediction model. In this study, an attempt was made to develop a model based on artificial neural network (ANN) for the prediction of surface roughness in a computer numerically controlled (CNC) lathe. The data used for the training and testing of the neural network was obtained by the turning of mild steel on CNC lathe. The parameters considered in the experiment are feed rate, cutting speed, depth of cut and the presence/absence of cutting fluid. Each of the other parameters such as tool nose radius, tool overhang, approach angle, workpiece length, workpiece diameter and workpiece material was taken as constant. The future scope of research in this area is also presented in the end.

Keywords: artificial neural network, surface roughness prediction, turning operations