Lei Han, Yisheng Zou, Guofu Ding, Menghao Zhu, Lei Jiang, S. Qin, H. Liang
{"title":"Development of an Online Tool Condition Monitoring System for NC Machining Based on Spindle Power Signals","authors":"Lei Han, Yisheng Zou, Guofu Ding, Menghao Zhu, Lei Jiang, S. Qin, H. Liang","doi":"10.23919/IConAC.2018.8748978","DOIUrl":null,"url":null,"abstract":"This paper presents a new online Tool Condition Monitoring System (TCMS) based on Object Linking and Embedded (OLE) for Process Control (OPC) Automation Interface of Computer Numerical Control (CNC) system for shop floor applications. The developed TCMS is able to acquire, display and analyze the spindle power signals automatically from the Panel Control Unit (PCU) of a machine tool in real-time. Tool condition is remote monitored and automatically determined by using adaptive thresholds calculated through statistical method put forward. Experiments are carried out and verify the accuracy and utility of the developed system.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 24th International Conference on Automation and Computing (ICAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/IConAC.2018.8748978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents a new online Tool Condition Monitoring System (TCMS) based on Object Linking and Embedded (OLE) for Process Control (OPC) Automation Interface of Computer Numerical Control (CNC) system for shop floor applications. The developed TCMS is able to acquire, display and analyze the spindle power signals automatically from the Panel Control Unit (PCU) of a machine tool in real-time. Tool condition is remote monitored and automatically determined by using adaptive thresholds calculated through statistical method put forward. Experiments are carried out and verify the accuracy and utility of the developed system.