Mohammad Mahruf Mahdi , Mahdi Sadeqi Bajestani , Sang Do Noh , Duck Bong Kim
{"title":"基于OPC UA的线弧增材制造数字双生结构","authors":"Mohammad Mahruf Mahdi , Mahdi Sadeqi Bajestani , Sang Do Noh , Duck Bong Kim","doi":"10.1016/j.rcim.2024.102944","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a digital twin (DT)-based architecture for wire arc additive manufacturing (WAAM) utilizing Open Platform Communications Unified Architecture (OPC UA) for enhanced communication, security, and real-time control. DT is explored at both enterprise management and individual asset scales, providing a comprehensive framework for process optimization. The proposed architecture integrates advanced 3D visualization, real-time defect prediction using convolutional neural networks (CNNs), and structured data management. A practical case study involving a 6-degree-of-freedom (DOF) industrial robotic arm demonstrates the application of the architecture in a WAAM deposition scenario. The architecture's effectiveness is evaluated, focusing on anomaly detection, joint angle accuracy, and communication reliability, highlighting the integration of computer vision and cloud-based data storage. The results indicate significant improvements in defect detection, process monitoring, and real-time interaction between the physical entity and the DT, underscoring the potential of the proposed DT architecture.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102944"},"PeriodicalIF":9.1000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Digital twin-based architecture for wire arc additive manufacturing using OPC UA\",\"authors\":\"Mohammad Mahruf Mahdi , Mahdi Sadeqi Bajestani , Sang Do Noh , Duck Bong Kim\",\"doi\":\"10.1016/j.rcim.2024.102944\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents a digital twin (DT)-based architecture for wire arc additive manufacturing (WAAM) utilizing Open Platform Communications Unified Architecture (OPC UA) for enhanced communication, security, and real-time control. DT is explored at both enterprise management and individual asset scales, providing a comprehensive framework for process optimization. The proposed architecture integrates advanced 3D visualization, real-time defect prediction using convolutional neural networks (CNNs), and structured data management. A practical case study involving a 6-degree-of-freedom (DOF) industrial robotic arm demonstrates the application of the architecture in a WAAM deposition scenario. The architecture's effectiveness is evaluated, focusing on anomaly detection, joint angle accuracy, and communication reliability, highlighting the integration of computer vision and cloud-based data storage. The results indicate significant improvements in defect detection, process monitoring, and real-time interaction between the physical entity and the DT, underscoring the potential of the proposed DT architecture.</div></div>\",\"PeriodicalId\":21452,\"journal\":{\"name\":\"Robotics and Computer-integrated Manufacturing\",\"volume\":\"94 \",\"pages\":\"Article 102944\"},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2025-01-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Robotics and Computer-integrated Manufacturing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S073658452400231X\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S073658452400231X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Digital twin-based architecture for wire arc additive manufacturing using OPC UA
This paper presents a digital twin (DT)-based architecture for wire arc additive manufacturing (WAAM) utilizing Open Platform Communications Unified Architecture (OPC UA) for enhanced communication, security, and real-time control. DT is explored at both enterprise management and individual asset scales, providing a comprehensive framework for process optimization. The proposed architecture integrates advanced 3D visualization, real-time defect prediction using convolutional neural networks (CNNs), and structured data management. A practical case study involving a 6-degree-of-freedom (DOF) industrial robotic arm demonstrates the application of the architecture in a WAAM deposition scenario. The architecture's effectiveness is evaluated, focusing on anomaly detection, joint angle accuracy, and communication reliability, highlighting the integration of computer vision and cloud-based data storage. The results indicate significant improvements in defect detection, process monitoring, and real-time interaction between the physical entity and the DT, underscoring the potential of the proposed DT architecture.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.