Influence of surface roughness in turning process — an analysis using artificial neural network

This paper presents methodology to identify the surface roughness value in CNC machining process using a soft computing approach. The aim of this paper is to achieve a roughness accuracy value above 95% and reduce the error rate to below 5% by using an artificial neural network. An artificial neural network method was selected to improve the time of inspection. Fourier transformation method will be used to extract the turning workpiece image, which is the squared value of the major frequency and principal component magnitude. Primary machining parameters such as feed rate, depth of cut, speed, frequency range, gray scale value, and conventional measurement value feed are used as the training input in the artificial neural network. Based on the training sample, the artificial neural network generates the vision measurement value for the testing samples that is compared to the stylus probe measurement value to predict the error rate and accuracy. The novelty of this work is to create an effective methodology using artificial neural network techniques to detect surface roughness errors of materials used in manufacturing industries.