声发射熵分析刀具磨损故障预测研究  

Fault Prediction Research of Tool Wear of Entropy Analysis of Acoustic Emission

作　　者：席剑辉[1] 林琳[1] 

机构地区：[1]沈阳航空航天大学自动化学院,辽宁沈阳110136

出　　处：《机械设计与制造》2014年第5期109-112,共4页Machinery Design & Manufacture

基　　金：国家自然科学基金项目(60804025);(61074090);国家自然科学基金青年基金资助项目(60804025);航空科学基金项目(2011ZD54011)

摘　　要：刀具在加工过程中不断磨损,直接影响构件的加工精度。根据采集的刀具声发射信号,分析声发射序列熵值在不同切削阶段的概率分布特征,建立一种基于刀具磨损状态的重心熵值的阈值检测方法。同时采用最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)建立声发射序列的熵值预测模型,结合熵值检测实现刀具磨损状态的高精度监测。仿真结果表明:采用最小二乘支持向量机预测熵值能够达到较高的预测精度;刀具的阈值检测,能够及时发现刀具的磨损故障,提高加工效率;二者结合,能够满足实际生产加工需要。Tool wears constantly in the process of machining, which has an influence on the machining accuracy of artifacts directly. Probability distribution characteristics of acoustic emission sequences entropy values in different cutting stages are analyzed according to the acquired acoustic emission signals of tool, and then a method of threshold detection is established based on center of gravity entropy of the tool wear states. Meanwhile, entropy values prediction model is built with least squares support vector machine （LS-SVM）, and combined with entropy detection to achieve high precision monitoring of the tool wear states. Simulation results show that entropy values are predicted by IS-SVM and can get higher prediction precision. In addition, threshold detection of the tool can detect faults timely and improve the efficiency of machining. To combine them, it will satisfy the needs of actual production and processing.

关 键 词：刀具 声发射 熵 支持向量机 故障预测 阈值检测 

分 类 号：TH16[机械工程—机械制造及自动化] TH17