Zipeng Wang
Papers
2
Total Citations
22
H-Index
2
About
Zipeng Wang is a researcher at the forefront of intelligent manufacturing and human-robot collaboration. His work centers on developing advanced control systems and optimization methods to make industrial robots more adaptive, efficient, and safe. Wang’s most significant contributions include a multi-scale control and action recognition framework for human-robot collaboration, designed to meet the demands of next-generation smart factories. This work, published in 2024, has already garnered 11 citations for its practical approach to integrating human oversight with robotic precision. Additionally, he has pioneered a multi-objective optimization technique for grinding robots—critical in automotive and aerospace industries—using a hybrid LSTM-MLP-NSGAII model. This 2023 study, also with 11 citations, addresses how process parameters like speed and force directly impact grinding quality and efficiency. By combining deep learning with evolutionary algorithms, Wang provides a data-driven solution to a longstanding manufacturing challenge. His research is notable for bridging theoretical optimization with real-world industrial application, positioning him as an emerging leader in smart robotics and process engineering.
Research Focus
Key Achievements
Top Papers
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- 2