Digital Twin Smart Manufacturing
Aims & Scope
The progression of technologies -- including artificial intelligence (AI), big data analytics, the Internet of Things (IoT), and complex system modeling -- has continually opened fresh opportunities while posing critical challenges for the manufacturing industry. Digital twin technology serves as a unifying methodological framework for the integrated deployment of these technologies, enabling seamless cyber-physical integration.
A focal point of discussion for both researchers and industry professionals is: How can the conceptual strengths of digital twin be utilized to augment the deep cognitive capabilities of manufacturing systems, optimize production quality and throughput, and reduce operational costs?
Addressing this imperative, this session provides an open platform for interdisciplinary dialogue on innovative applications of digital twin in smart manufacturing.
The session topics include but are not limited to:
Digital Twin & Smart Manufacturing Equipment
Digital Twin & Smart Manufacturing Processes
Digital Twin & Smart Manufacturing Workshops
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For session presentation applications, please contact the session chairs below:
Tianliang Hu
Email: tlhu#sdu.edu.cn (replace # with @)
Giovanni Lugaresi
Email: giovanni.lugaresi#kuleuven.be(replace # with @)
Session Chairs
Presentations
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ProfessorHarbin University of Science and TechnologyDigital Twin Technology and Applications for Optimizing Machining ProcessesAbstract Coming soon
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ProfessorShandong University (China)Digital Twin based contour error prediction and compensation for machine tool and roboticsAbstract Coming soon
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ProfessorGuizhou University (China)Modeling and Intelligent Optimization of Manufacturing Processes Driven by Industrial Foundation Models and Digital TwinsAbstract Coming soon
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ProfessorShanghai Jiaotong University (China)Digital Twin for Manufacturing Execution Management and Control of Discrete Manufacturing SystemsAbstract Coming soon
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ProfessorNanhang University (China)Online inference of residual stress field in large-scale components based on deformation force monitoring dataAbstract Coming soon
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Associate ProfessorDalian University of Technology (China)Shape-performance multi-feature sensing technology for the assembly process supports aero-engine digital twinAbstract Coming soon
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Postdoctoral Research FellowHarbin Institute of Technology (China)Digital Twin-Enabled Coupled Error Modeling and Predictive Analysis for Assembly ProcessesAbstract Coming soon
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Assistant ProfessorKU Leuven (Belgium)Graph-model Based Online Calibration of Production System Digital TwinsAbstract Coming soon
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Ph. D StudentShandong University (China)Digital Twin Based Human-Machine CollaborationAbstract Coming soon