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About

Hang Dong is a leading researcher in intelligent robotic manufacturing, with a focus on laser cladding additive manufacturing and robotic path planning. His work bridges the gap between digital modeling and physical production, particularly in optimizing complex robotic trajectories for high-precision additive processes. Dong’s most cited paper, “CAD-based automatic path generation and optimization for laser cladding robot in additive manufacturing” (2017, 51 citations), introduces a pioneering method for automatically generating and optimizing robot paths directly from CAD models, significantly improving efficiency and accuracy in laser cladding. He further advanced real-time process control through “Modeling and real-time prediction for complex welding process based on weld pool” (2018, 38 citations), enabling adaptive adjustments during fabrication. Dong has also contributed to novel optimization strategies, including reinforcement learning and elitist genetic algorithms for dual-robot trajectory planning and time-optimal motion planning. His research on STL model optimization and layer shape for laser cladding forming (2018, 20 citations) addresses critical challenges in part quality and surface finish. With a growing citation impact, Hang Dong’s work is essential for students and researchers exploring automation, robotics, and advanced manufacturing, offering practical solutions for next-generation, robot-driven additive manufacturing systems.