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

基于刀痕纹理图像熵测度的球头铣刀磨损状态预测方法研究

作　　者：LI Mao-yue LU Xin-yuan LIU Ze-long ZHANG Ming-lei 李茂月;卢新元;刘泽隆;张明垒(Key Laboratory of Advanced Manufacturing and Intelligent Technology,Ministry of Education,Harbin University of Science and Technology,Harbin 150080,China)

机构地区：[1]Key Laboratory of Advanced Manufacturing and Intelligent Technology,Ministry of Education,Harbin University of Science and Technology,Harbin 150080,China

出　　处：《Journal of Central South University》2025年第1期174-188,共15页中南大学学报(英文版)

基　　金：Project(51975169)supported by the National Natural Science Foundation of China;Project(LH2022E085)supported by the Natural Science Foundation of Heilongjiang Province,China。

摘　　要：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.高效的刀具状态监测技术有助于实现刀具寿命的智能管理,优化刀具使用成本。本文研究了球头铣刀不同磨损程度对加工刀痕纹理形状的影响机理。首先,提出一种基于刀痕纹理图像熵测量的球头铣刀磨损状态预测方法,可以及时预测刀具磨损的位置和程度,并分析了刀痕纹理形状随刀具磨损状态的变化规律。然后,提出一种二维样本熵算法对纹理形态实现了量化。最后,将加工参数和刀具姿态加入到磨损量的预测过程中,实现了球头铣刀磨损量和磨损位置的预测。经测试,本文提出的刀具状态监测方法预测值与标准值相关性达到95.32%,准确率达到82.73%,满足刀具状态监测的需要。

关 键 词：ball-end cutter wear tool condition monitoring surface texture texture quantifier sample entropy 

分 类 号：TG1[金属学及工艺—金属学]