Tool wear estimation using an analytic fuzzy classifier and support vector machines

Authors:
Danko Brezak;Dubravko Majetic;Toma Udiljak;Josip Kasac
Affiliations:
Department of Robotics and Production Systems Automation, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, Croatia;Department of Robotics and Production Systems Automation, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, Croatia;Department of Technology, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, Croatia;Department of Robotics and Production Systems Automation, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, Croatia
Venue:
Journal of Intelligent Manufacturing
Year:
2012

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Abstract

A new type of continuous hybrid tool wear estimator is proposed in this paper. It is structured in the form of two modules for classification and estimation. The classification module is designed by using an analytic fuzzy logic concept without a rule base. Thereby, it is possible to utilize fuzzy logic decision-making without any constraints in the number of tool wear features in order to enhance the module robustness and accuracy. The final estimated tool wear parameter value is obtained from the estimation module. It is structured by using a support vector machine nonlinear regression algorithm. The proposed estimator implies the usage of a larger number and various types of features, which is in line with the concept of a closer integration between machine tools and different types of sensors for tool condition monitoring.