Investigated iterative convergences of neural network for prediction turning tool wear

This study investigates the iterative convergences of neural network for prediction turning tool wear. For the smart manufacturing, the intelligent prediction systems have been gradually developing for processing of CNC machine tools. Recently, many artificial intelligent algorithms of machine learning have been widely applied for forecasting and decision making in intelligent manufacturing. In general, the cutting tool wear in manufacturing of CNC machine tool plays a major role for a high quality and an efficient operation, but it is very difficult to diagnose and prognoses the tool wear for tool life due to many cutting parameters. Therefore, the study investigates the iterative gradient convergences of backpropagation neural network (BNN) algorithm for prediction tool life with analytics of its convergence and stability. The estimative methods of iterative convergences include stochastic gradient descent (SGD), momentum, adaptive gradient (Adagrad), adaptive delta (Adadelta), and adaptive moment (ADAM) algorithms. In BNN prediction model, the data inputs are the cutting speed, feed rate, and totalmaterial removal volume and data output is tool wear measured from the microscope. Results showed that the tool wear curves at different cutting conditions can be predicted and trained using BNN model for intelligent manufacturing. In addition, the convergence of ADAM gradient for the tool wear in all cases is the best prediction for the BNN model. However, it is worth to notice that the momentum gradient is faster training speed to converge to a constant error at fewer iteration numbers.