Discrimination and Prediction of Tool Wear State Based on Gray Theory

Abstract

Abstract In allusion to the problems that random noise causes that interfere with the extraction of wear characteristics from cutting vibration signals and cause low accuracy of lathe tool wear state discrimination and prediction, a new method was proposed in this study. The proposed method extracted the wear time-domain characteristics of tool vibration signals through wavelet packet transform and the correlation coefficient method. Next, noises in wear time-domain characteristics were reduced by singular-value decomposition. The gray proximity correlation between the characteristic data series corresponding to the initial cutting and current cutting was calculated and used to represent the characteristic of tool wearing and discriminate the wear state. The gray decision-making model of metabolism GM(1,1) was established based on the wear characteristic series and was used to predict the variation trend of tool wear state, thus deciding the necessity of cutting tool changing. Cutting wear experiments and wear state predictions were carried out on the ZCK20 numerical control lathe (Tuoman, Zhejiang, China) by using three pieces of WNMG080408-TM T9125 lathe tools (Tungaloy Corporation, Iwaki, Japan). Experimental results demonstrated that the proposed method could eliminate noise effectively, acquire the optimal wear characteristics of tools, and discriminate and predict the wear state of tools accurately.