Prediction of surface roughness in turning operations by computer vision using neural network trained by differential evolution algorithm


In recent years, the measurement of surface roughness of a workpiece plays a vital role since the roughness of a surface has a considerable influence on the product quality and the functional aspects. In this work, a differential evolution algorithm (DEA)-based artificial neural network (ANN) has been used for the prediction of surface roughness in turning operations. Cutting speed, feed rate, depth of cut, and average gray level of the surface image of workpiece, acquired by computer vision, were taken as the input parameters and surface roughness as the output parameter. The results obtained from the DEA-based ANN model were compared with the backpropagation (BP)-based ANN. It is found that the error percentage is very close, and it is also observed that the convergence speed for the DEA-based ANN is higher than the BP-based ANN.