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    Volume 48, Issue 6 (November 2020)

    Discrimination and Prediction of Tool Wear State Based on Gray Theory

    (Received 16 November 2017; accepted 27 August 2018)

    Published Online: 2019

    CODEN: JTEVAB

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    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.

    Author Information:

    Li, Xiao-ru
    School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai,

    Zhu, Jian-min
    School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai,

    Tian, Feng-qing
    School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai,

    Pan, He-feng
    School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai,


    Stock #: JTE20180302

    ISSN:0090-3973

    DOI: 10.1520/JTE20180302

    Author
    Title Discrimination and Prediction of Tool Wear State Based on Gray Theory
    Symposium ,
    Committee G02