Modeling of Machining Force in Hard Turning Process

In this work, we develop a modeling based on an Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) to predict the machining force components generated during hard turning of a bearing steel with CBN cutting tool. The inputs of the ANN model were the cutting parameters (cutting speed, feed and depth-of-cut) and the workpiece hardness. The network training is performed by using experimental data. The optimal network architecture is determined after several simulations by Matlab Neural Network Toolbox. Back-propagation by Bayesian Regularization in combination with Levenberg-Marquardt algorithm is employed for training. The ANN approach is suitable to estimate the machining force components such as feed-force, radial-force and tangential-force; for this purpose, the results are compared to those obtained by experiment, and the maximum average MAPE value of 4.58% was obtained for the machining force prediction. Also, the ANN results were compared to those obtained by MLR model. It was shown that the ANN model produced more successful results.