Papers
6
Total Citations
162
H-Index
4
About
Dr. Baohua Chang is a leading researcher in intelligent robotic welding and manufacturing, with a focus on computer vision, deep learning, and automation for complex industrial applications. His work addresses critical challenges in automatic joint detection, seam tracking, and quality monitoring for narrow butt joints and multi-layer/multi-pass welding, which are essential in energy, aerospace, and shipbuilding industries. Dr. Chang’s most cited paper, “A Vision Based Detection Method for Narrow Butt Joints and a Robotic Seam Tracking System” (2019, 62 citations), introduces a novel vision-based approach for precise joint detection and tracking. He further advanced the field with “A Weld Position Recognition Method Based on Directional and Structured Light Information Fusion” (2018, 45 citations), enhancing accuracy in multi-pass welding. His recent work, “AF-FTTSnet: An end-to-end two-stream convolutional neural network for online quality monitoring of robotic welding” (2024, 37 citations), leverages deep learning for real-time process control. Dr. Chang has also contributed to 3D reconstruction for robot machining and model-driven programming systems, demonstrating a commitment to bridging digital models and physical manufacturing. With over 160 citations across his key publications, his research is pivotal in advancing autonomous, high-precision welding technologies.
Research Focus
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Top Papers
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