Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuz

Abstract On-line tool wear estimation plays a critical role in industrial automation for higher productivity and product quality. In addition, an appropriate and timely decision for tool change is required in machining systems. Thus, this paper develops an estimation system through integration of two promising technologies, artificial neural networks (ANNs) and fuzzy logic. The proposed system consists of five components: (1) data collection, (2) feature extraction, (3) pattern recognition, (4) multi-sensor integration, and (5) tool/work distance compensation. Two different networks, a feedforward neural network with an error backpropagation learning algorithm and a counterpropagation neural network, are employed to recognize the extracted features and provide a comparison of these two networks based on accuracy and speed. Meanwhile, in order to enhance the accuracy of the estimation result, this research work applies multiple sensors for detection. The data from multiple sensors are integrated through the proposed fuzzy logic model. Such a model is self-organizing and self-adjusting, learning from experience. Physical experiments of the metal cutting process are implemented to evaluate the proposed system. The results showed that the proposed system can significantly increase the accuracy of the product profile when compared to the conventional approaches, like multiple regression and a single ANN.

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