Shenglin Wang
Papers
2
Total Citations
147
H-Index
2
About
Dr. Shenglin Wang is a leading researcher at the intersection of smart manufacturing, robotics, and artificial intelligence, with a primary focus on enhancing safety, reliability, and security in human–robot collaborative environments. His most influential work introduces a deep learning-enhanced Digital Twin framework for Industry 5.0, a contribution that has garnered 144 citations and is reshaping how flexible and efficient smart manufacturing systems are designed. By integrating advanced AI with real-time virtual replicas, Dr. Wang’s framework significantly improves the safety and reliability of collaborative tasks involving humans and robots. In parallel, he has addressed the critical challenge of cybersecurity in robotic systems, demonstrating how Digital Twins themselves can be attacked to compromise both security and safety—a vital insight for autonomous robots and manufacturing cells. This dual focus on performance and protection marks Dr. Wang as a forward-thinking scholar whose work not only advances manufacturing efficiency but also safeguards the human-centric future of industrial automation. His research is essential reading for engineers and researchers working toward trustworthy, intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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