Papers

6

Total Citations

73

H-Index

3

About

Liu Wei’s research centers on modular robotics, smart product-service systems, and digital twin frameworks, with a focus on transforming traditional robotic design through function-oriented optimization. His most impactful work, “A Function-Oriented Optimising Approach for Smart Product Service Systems at the Conceptual Design Stage: A Perspective from the Digital Twin Framework” (2021, 56 citations), pioneers a digital twin-driven methodology that integrates conceptual design with service-oriented architectures—critical for advancing Industry 4.0. In modular robotics, Wei systematically categorized seven modularizing and three configuring methods, offering a foundational taxonomy that distinguishes modular from conventional robot design. His bottom-up design approach, detailed in “Design Method to Modular Robot System” (2009), enables functional decomposition of non-modular robots to identify shared and personalized modules, facilitating scalable, reconfigurable systems. Wei also contributed to mobile robot path planning by merging extension theory with robotic control, modeling robotic matter-elements to enhance navigation safety. With over 70 cumulative citations, his work bridges theoretical frameworks and practical implementation, notably in parallel robot accuracy and extension-based controllers. Wei’s research is essential reading for engineers and scholars seeking to understand how digital twins and modularity can reshape intelligent, adaptive robotic systems.

Research Focus

Key Achievements

3
H-Index
6
Papers
73
Total Citations
12
Avg Citations/Paper
🏆 Most Cited Paper
A function-oriented optimising approach for smart product service systems at the conceptual design stage: A perspective from the digital twin framework
56 citations · 2021
📈 Most Prolific Year: 2008 (3 Papers)
🤝 Key Collaborators: 8
🏛 Institutions: Hebei University of Technology, Beihang University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago