Christiand, Gandjar Kiswanto, Ario Sunar Baskoro, Zulhendri Hasymi, Tae Jo Ko
{"title":"Tool Wear Monitoring In Micro-Milling Based on Digital Twin Technology with an Extended Kalman Filter","authors":"Christiand, Gandjar Kiswanto, Ario Sunar Baskoro, Zulhendri Hasymi, Tae Jo Ko","doi":"10.3390/jmmp8030108","DOIUrl":null,"url":null,"abstract":"In order to avoid catastrophic events that degrade the quality of machined products, such as tool breakage, it is vital to have a prognostic system for monitoring tool wear during the micro-milling process. Despite the long history of the tool wear monitoring field, creating such a system to track, monitor, and foresee the rapid progression of tool wear still needs to be improved in the application of micro-milling. On the other hand, digital twin technology has recently become widely recognized as significant in manufacturing and, notably, within the Industry 4.0 ecosystem. Digital twin technology is considered a potential breakthrough in developing a prognostic tool wear monitoring system, as it enables the tracking, monitoring, and prediction of the dynamics of a twinned object, e.g., a CNC machine tool. However, few works have explored the digital twin technology for tool wear monitoring, particularly in the micro-milling field. This paper presents a novel tool wear monitoring system for micro-milling machining based on digital twin technology and an extended Kalman filter framework. The proposed system provides wear progression notifications to assist the user in making decisions related to the machining process. In an evaluation using four machining datasets of slot micro-milling, the proposed system achieved a maximum error mean of 0.038 mm from the actual wear value. The proposed system brings a promising opportunity to widen the utilization of digital twin technology with the extended Kalman filter framework for seamless data integration for wear monitoring service.","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"52 1","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/jmmp8030108","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In order to avoid catastrophic events that degrade the quality of machined products, such as tool breakage, it is vital to have a prognostic system for monitoring tool wear during the micro-milling process. Despite the long history of the tool wear monitoring field, creating such a system to track, monitor, and foresee the rapid progression of tool wear still needs to be improved in the application of micro-milling. On the other hand, digital twin technology has recently become widely recognized as significant in manufacturing and, notably, within the Industry 4.0 ecosystem. Digital twin technology is considered a potential breakthrough in developing a prognostic tool wear monitoring system, as it enables the tracking, monitoring, and prediction of the dynamics of a twinned object, e.g., a CNC machine tool. However, few works have explored the digital twin technology for tool wear monitoring, particularly in the micro-milling field. This paper presents a novel tool wear monitoring system for micro-milling machining based on digital twin technology and an extended Kalman filter framework. The proposed system provides wear progression notifications to assist the user in making decisions related to the machining process. In an evaluation using four machining datasets of slot micro-milling, the proposed system achieved a maximum error mean of 0.038 mm from the actual wear value. The proposed system brings a promising opportunity to widen the utilization of digital twin technology with the extended Kalman filter framework for seamless data integration for wear monitoring service.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
Indexed/Abstracted:
Web of Science SCIE
Scopus
CAS
INSPEC
Portico