Research on wear state prediction of ball end milling cutter based on entropy measurement of tool mark texture images

Abstract

Efficient tool condition monitoring techniques help to realize intelligent management of tool life and reduce tool usage costs. In this paper, the influence of different wear degrees of ball-end milling cutters on the texture shape of machining tool marks is investigated, and a method is proposed for predicting the wear state (including the position and degree of tool wear) of ball-end milling cutters based on entropy measurement of tool mark texture images. Firstly, data samples are prepared through wear experiments, and the change law of the tool mark texture shape with the tool wear state is analyzed. Then, a two-dimensional sample entropy algorithm is developed to quantify the texture morphology. Finally, the processing parameters and tool attitude are integrated into the prediction process to predict the wear value and wear position of the ball end milling cutter. After testing, the correlation between the predicted value and the standard value of the proposed tool condition monitoring method reaches 95.32%, and the accuracy reaches 82.73%, indicating that the proposed method meets the requirement of tool condition monitoring.

Keywords

ball-end cutter wear / tool condition monitoring / surface texture / texture quantifier / sample entropy

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Central South University