Yunkai Ma
Papers
8
Total Citations
192
H-Index
6
About
Yunkai Ma is a leading researcher in intelligent robotic welding systems, whose work bridges computer vision, deep learning, and industrial automation. His primary research areas include weld seam detection and tracking, robotic path generation, and sensor-guided manipulation. Ma’s most impactful contribution is the development of Shuffle-YOLO, a deep neural network for complex weld seam feature point extraction that enables real-time seam tracking and posture adjustment—a paper that has garnered 63 citations. He also proposed WeldNet for weld type identification and initial point guidance (44 citations), and DeepKP for robust keypoint extraction under arc light interference (11 citations). His work on efficient start point guiding for curved welds (48 citations) addresses a critical bottleneck in automated welding. Beyond welding, Ma has contributed to robotic end-effector design for bolting operations and developed frameworks for automatic welding path generation from CAD models. His research has direct applications in manufacturing, shipbuilding, and aerospace, where his methods improve precision, reduce programming time, and enable flexible production. With over 190 cumulative citations, Ma’s innovations are shaping the next generation of intelligent, adaptive welding robots.
Research Focus
Key Achievements
Top Papers
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- 4A Robotic End-Effector for Screwing and Unscrewing Bolts From the Side12 citations · 2022
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- 7Maximum Allowable TCF Calibration Error for Robotic Pose Servoing2 citations · 2024
- 8Pose Measurement and TCF Calibration for Automatic Cabin Docking1 citations · 2024