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Knowledge graph and function block based Digital Twin modeling for robotic machining of large-scale components
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipments, Ministry of Industry and Information Technology, Beijing 100191, China; Beijing Key Laboratory of digital design and manufacturing technology, Beijing 100191, China.
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipments, Ministry of Industry and Information Technology, Beijing 100191, China; Beijing Key Laboratory of digital design and manufacturing technology, Beijing 100191, China.
Sandvik Coromant, Stockholm 12679, Sweden.
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2024 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 85, p. 102609-, article id 102609Article in journal (Refereed) Published
Abstract [en]

Robotic machining is a potential method for machining large-scale components (LSCs) due to its low cost and high flexibility. However, the low stiffness of robots and complex machining process of LSCs result in a lack of alignment between the physical process and digital models, making it difficult to realize the robotic machining of LSCs. The recent Digital Twin (DT) concept shows potential in terms of representing and modeling physical processes. Therefore, this study proposes a robotic machining DT for LSCs. However, the current DT is not capable of knowledge representation, multi-source data integration, optimization algorithm implementation, and real-time control. To address these issues, Knowledge Graph (KG) and Function Block (FB) are employed in the proposed robotic machining DT. Here, robotic machining related information, such as the machining parameters and errors, is represented in the virtual space by building the KG, whereas the FBs are responsible for integrating and applying the algorithms for process execution and optimization based on real-world events. Moreover, a novel adaptive process adjustment strategy is proposed to improve the efficiency of the process execution. Finally, a prototype system of the robotic machining DT is developed and validated by an experiment on robotic milling of the assembly interface for an LSC. The results demonstrate that the robotic machining is successfully optimized and improved by the proposed method.

Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 85, p. 102609-, article id 102609
Keywords [en]
Digital Twin, Function block, Knowledge graph, Large-scale components, Process planning and execution, Robotic machining
National Category
Robotics Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-334956DOI: 10.1016/j.rcim.2023.102609ISI: 001048681300001Scopus ID: 2-s2.0-85167999619OAI: oai:DiVA.org:kth-334956DiVA, id: diva2:1792635
Note

QC 20230830

Available from: 2023-08-30 Created: 2023-08-30 Last updated: 2023-09-04Bibliographically approved

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Wang, Lihui

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