Identification of tool wear states with fuzzy classification

A new on-line tool wear states detecting method, with spindle and feed curr ent 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 es tablished under different tool wear states with partial experimental design and regression analysis. Fuzzy classification method is then used to obtai n the membership degree of each tool wear classification with measured spin dle 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 o f the method are verified by experimental results. The method can be effect ively employed in practice.