Single Point Cutting Tool Fault Diagnosis in Turning Operation Using Reduced Error Pruning Tree Classifier

作     者：E.Akshay V.Sugumaran M.Elangovan 

作者机构：School of Mechanical Engineering(SMEC)VIT UniversityChennai600127India Department of Mechanical EngineeringSNS College of TechnologyCoimbatore641035India 

出 版 物：《Structural Durability & Health Monitoring》 (结构耐久性与健康监测（英文）)

年 卷 期：2022年第16卷第3期

页      面：255-270页

核心收录：

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

主　　题：Fault diagnosis tool condition monitoring REPTree decision tree statistical feature extraction 

摘      要：Tool wear is inevitable in daily machining process since metal cutting process involves the chip rubbing the tool surface after it has been cut by the tool *** wear dominantly influences the deterioration of surface finish,geometric and dimensional tolerances of the ***,for complete utilization of cutting tools and reduction of machine downtime during the machining process,it becomes necessary to understand the develop-ment of tool wear and predict its status before *** this study,tool condition monitoring system was used to monitor the behavior of a single point cutting tool to predict flank wear.A uniaxial accelerometer was attached to a single point cutting tool under *** accelerometer acquires vibrational signals during turning operation on a lathe *** acquired signals were then used to extract statistical features such as standard error,variance,skewness,*** substantial features were recognized to reduce the utilization of computing *** were used to classify the signals as good and three different measures of flank wear by a decision tree *** domain features were also extracted and shown to be less effective in classification in comparison to statistical ***(Reduced Error Pruning Tree)algorithm was used in this *** decision tree algorithm achieved a maximum classification accuracy of 72.77%for all signals *** spindle speed and feed rate are considered as the variables the accuracy is about 86.25%.When spindle speed is the only variable parameter the accuracy is about 82.71%.When depth of cut,feed rate and speed of the spindle are considered as variable parameters,the accuracy of the decision tree is around 93.51%.This study demonstrates the performance of REPTree classifier in tool condition *** can be utilized for tool wear identification and thus improve surface finish,dimensional accuracy of the work piece and reduce machine *** add