Modeling of Tool Life and Cutting Force in Turning Using a Combined Neural Networks and Fuzzy Inference System

This paper illustrates the application of combined neural networks and fuzzy inference system for modeling tool life and cutting force in turning operation for set of given cutting parameters, namely cutting speed, feed and depth of cut. The proposed methodology uses a hybrid-learning algorithm i.e., combination of the backpropagation gradient descent method and least squares method, to identify premise and consequent parameters of the first-order Sugeno-fuzzy inference system. The results obtained from proposed method are compared with the experimental results. The comparison indicates that the proposed method can produce efficient knowledge base of fuzzy inference system for modeling the tool life and cutting force in turning operation.