On modeling of tool wear using sensor fusion and polynomial classifiers

With increased global competition, the manufacturing sector is vigorously working on enhancing the efficiency of manufacturing processes in terms of cost, quality, and environmental impact. This work presents a novel approach to model and predict cutting tool wear using statistical signal analysis, pattern recognition, and sensor fusion. The data are acquired from two sources: an acoustic emission sensor (AE) and a tool post dynamometer. The pattern recognition used here is based on two methods: Artificial Neural Networks (ANN) and Polynomial Classifiers (PC). Cutting tool wear values predicted by neural network (ANN) and polynomial classifiers (PC) are compared. For the case study presented, PC proved to significantly reduce the required training time compared to that required by an ANN without compromising the prediction accuracy. The predicted results compared well with the measured tool wear values.