Peiwen Yang
Papers
2
Total Citations
37
H-Index
2
About
Peiwen Yang is a leading researcher in intelligent robotic welding systems, with a focus on laser vision sensing and automated calibration. His work addresses critical challenges in industrial automation, particularly the time-consuming manual teaching processes that limit welding robot efficiency. Yang's most impactful contribution is a teaching-free welding position guidance method for fillet welds, published in 2022 and cited 27 times, which leverages laser vision sensing and EGM technology to eliminate the need for manual path programming. He further advanced the field with a fast calibration method for laser vision robotic welding systems (LVRWSs), cited 10 times, that overcomes the limitations of traditional calibration—such as local optimal solutions and complex manual teaching—by using automatic path planning. This innovation significantly reduces setup time and improves accuracy in real-world applications. Yang's work is pivotal for industries seeking to deploy flexible, autonomous welding robots, and his methods are foundational for next-generation smart manufacturing systems. His research continues to shape the future of robotic welding by making systems more adaptive and less reliant on human intervention.
Research Focus
Key Achievements
Top Papers
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