Designing an Artificial Neural Network Based Model for Online Prediction of Tool Life in Turning

Artificial neural network is one of the most robust and reliable methods for online prediction of nonlinear incidents in machining. Tool flank wear as a tool life criterion is an important task which is needed to be predicted during machining processes to establish an online tool life estimation system. In this study, an artificial neural network model was developed to predict the tool wear and tool life in turning process. Cutting parameters and cutting forces were used as input and tool flank wear rates were regarded as target data for creating the online prediction system. SIMULINK and neural network tool boxes in MATLAB software were used for establishing a reliable online monitoring model. For generalizing the model, full factorial method was used to design the experiments. Predicted results were compared with the test results and a full confirmation of the model was reached.