Prediction of the Average Surface Roughness in Dry Turning of Cold Rolled Alloy Steel by Artificial Neural Network

The surface quality of the machined parts is one of the most important product quality characteristics and one of the most frequent customer requirements. In this study, an artificial neural network (ANN) approach for modeling of surface roughness in dry single-point turning of an alloy steel using coated tungsten carbide inserts is presented. The three main cutting parameters consisting of cutting speed, feed rate, and depth of cut are varied in the experiment. Each of the other parameters is treated as constant. The average surface roughness (Ra) is chosen as a measure of surface quality. The data set from major experiment is employed for training a feed-forward three-layer backpropagation ANN. The developed ANN model is tested on the other combinations of the cutting parameters in the given ranges, which are not included in the training process. The results of calculations are in good agreement with the experimental data confirming the effectiveness of ANN approach in modeling of surface roughness in turning process.