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Shengsun Hu is a leading researcher in intelligent robotic welding, with a focus on vision sensing, human-machine interaction, and path planning for complex weld geometries. His work addresses critical challenges in automated welding, particularly for multi-pass and multi-layer processes. Hu’s most-cited paper, “Robotic seam tracking system based on vision sensing and human-machine interaction for multi-pass MAG welding” (2020, 60 citations), introduced a novel approach combining real-time visual feedback with operator guidance to improve weld quality and adaptability. He further advanced this field with a 2022 study (43 citations) integrating conditional generative adversarial networks (CGAN) with laser vision for enhanced seam tracking in multi-layer applications. Earlier, Hu developed a path planning method for tube–sphere intersection welds with J-groove joints (2012, 39 citations), showcasing his expertise in complex joint configurations. His contributions have significantly improved the precision and efficiency of robotic welding systems, with over 140 total citations reflecting their impact on manufacturing automation. Hu’s work bridges theoretical algorithms and practical industrial solutions, making him a key figure in the evolution of smart welding technologies.