Development of a neural network based surface roughness prediction system using cutting parameters and an accelerometer in turning

In this work, a technique is proposed to predict surface roughness by using neural network. Surface roughness could be predicted within a reasonable degree of accuracy by taking feed rate, cutting speed, depth of cut and three orthogonal axis (x, y, z) signals of vibrations of tool holder as input parameters. 27 experiments were performed by using a CNC lathe with a carbide cutting tool. Experimental data obtained from turning process were used for training and testing of neural network architecture based prediction system. When experimental and prediction results were compared, it has been seen that a mean accuracy of 91,17% was achieved.