{"title":"基于多传感器信号的线材电火花加工过程性能监测与故障预测系统","authors":"P. Abhilash, D. Chakradhar","doi":"10.1080/10910344.2022.2044856","DOIUrl":null,"url":null,"abstract":"Abstract This study aims to develop a pulse classification algorithm to understand wire electric discharge machining (wire EDM) process stability and performance based on the discharge pulse characteristics. Also, a process data driven failure prediction system is proposed. The wire EDM monitoring system includes high sampling rate differential probes and current probes. The features extracted through pulse train analysis were spark discharge energy, ignition delay time, spark frequency and proportion of various discharges. A pulse discrimination algorithm was proposed, which classifies the discharges into open circuit sparks, arc discharges, short circuit sparks and normal sparks. It was observed that higher proportions of short circuit pulses resulted in inferior part quality. The differences in the pulse cycle during stable and unstable machining were studied based on the extracted features. It was found that the discharge frequency and the proportion of arc and short circuit pulses were extremely high before the wire breakages. An artificial neural network (ANN) model was developed to predict the process responses, like cutting speed and surface roughness, from the process data. Also, an intelligent algorithm was developed based on the extracted in-process data to predict the unstable conditions, leading to machining failures. The accuracy of the algorithm was confirmed to be very high by conducting confirmation tests.","PeriodicalId":51109,"journal":{"name":"Machining Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Performance monitoring and failure prediction system for wire electric discharge machining process through multiple sensor signals\",\"authors\":\"P. Abhilash, D. Chakradhar\",\"doi\":\"10.1080/10910344.2022.2044856\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This study aims to develop a pulse classification algorithm to understand wire electric discharge machining (wire EDM) process stability and performance based on the discharge pulse characteristics. Also, a process data driven failure prediction system is proposed. The wire EDM monitoring system includes high sampling rate differential probes and current probes. The features extracted through pulse train analysis were spark discharge energy, ignition delay time, spark frequency and proportion of various discharges. A pulse discrimination algorithm was proposed, which classifies the discharges into open circuit sparks, arc discharges, short circuit sparks and normal sparks. It was observed that higher proportions of short circuit pulses resulted in inferior part quality. The differences in the pulse cycle during stable and unstable machining were studied based on the extracted features. It was found that the discharge frequency and the proportion of arc and short circuit pulses were extremely high before the wire breakages. An artificial neural network (ANN) model was developed to predict the process responses, like cutting speed and surface roughness, from the process data. Also, an intelligent algorithm was developed based on the extracted in-process data to predict the unstable conditions, leading to machining failures. The accuracy of the algorithm was confirmed to be very high by conducting confirmation tests.\",\"PeriodicalId\":51109,\"journal\":{\"name\":\"Machining Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2022-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machining Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/10910344.2022.2044856\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machining Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10910344.2022.2044856","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
Performance monitoring and failure prediction system for wire electric discharge machining process through multiple sensor signals
Abstract This study aims to develop a pulse classification algorithm to understand wire electric discharge machining (wire EDM) process stability and performance based on the discharge pulse characteristics. Also, a process data driven failure prediction system is proposed. The wire EDM monitoring system includes high sampling rate differential probes and current probes. The features extracted through pulse train analysis were spark discharge energy, ignition delay time, spark frequency and proportion of various discharges. A pulse discrimination algorithm was proposed, which classifies the discharges into open circuit sparks, arc discharges, short circuit sparks and normal sparks. It was observed that higher proportions of short circuit pulses resulted in inferior part quality. The differences in the pulse cycle during stable and unstable machining were studied based on the extracted features. It was found that the discharge frequency and the proportion of arc and short circuit pulses were extremely high before the wire breakages. An artificial neural network (ANN) model was developed to predict the process responses, like cutting speed and surface roughness, from the process data. Also, an intelligent algorithm was developed based on the extracted in-process data to predict the unstable conditions, leading to machining failures. The accuracy of the algorithm was confirmed to be very high by conducting confirmation tests.
期刊介绍:
Machining Science and Technology publishes original scientific and technical papers and review articles on topics related to traditional and nontraditional machining processes performed on all materials—metals and advanced alloys, polymers, ceramics, composites, and biomaterials.
Topics covered include:
-machining performance of all materials, including lightweight materials-
coated and special cutting tools: design and machining performance evaluation-
predictive models for machining performance and optimization, including machining dynamics-
measurement and analysis of machined surfaces-
sustainable machining: dry, near-dry, or Minimum Quantity Lubrication (MQL) and cryogenic machining processes
precision and micro/nano machining-
design and implementation of in-process sensors for monitoring and control of machining performance-
surface integrity in machining processes, including detection and characterization of machining damage-
new and advanced abrasive machining processes: design and performance analysis-
cutting fluids and special coolants/lubricants-
nontraditional and hybrid machining processes, including EDM, ECM, laser and plasma-assisted machining, waterjet and abrasive waterjet machining