Identification of tool wear states with fuzzy classification
A new on-line tool wear states detecting method, with spindle and feed current signal in boring, is presented. By analyzing the effects of tool wear, as well as the cutting parameters on the current signals, the models of the relationship between the current signals and the cutting parameters are established under different tool wear states with partial experimental design and regression analysis. Fuzzy classification method is then used to obtain the membership degree of each tool wear classification with measured spindle and feed current values. Finally, the membership results of the spindle current and feed current are fused by the fuzzy inference method, and the tool wear state may be detected effectively. The validity and reliability of the method are verified by experimental results. The method can be effectively employed in practice.
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