Bingqi Jia
Papers
7
Total Citations
35
H-Index
4
About
Bingqi Jia is a rising researcher in intelligent robotic welding systems, focusing on precision, energy efficiency, and automation. His work spans vibration suppression, positioning accuracy enhancement, and energy consumption modeling for industrial robots. Jia’s most cited paper, “Vibration suppression of welding robot based on chaos-regression tree dynamic model” (2024, 11 citations), introduces a novel approach to mitigate harmful vibrations during welding operations. He has made significant contributions to improving absolute positioning accuracy through joint error compensation and kinematic calibration methods, including a Newton–Raphson-based 10-parameter compensation technique. Jia also developed YOLO-CE, a deep learning model integrated with 3D point cloud analysis for automated welding seam feature extraction on non-standard steel structures like electric power tower bases. His data-driven energy consumption modeling and optimization framework for welding robot systems addresses sustainability in manufacturing. Additionally, Jia proposed the LF-IWOA algorithm for optimizing robotic tack welding paths, overcoming local optimum issues. With over 35 citations across his publications, Jia’s work is directly applicable to real-world manufacturing challenges, bridging robotics, machine learning, and industrial automation.
Research Focus
Key Achievements
Top Papers
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- 6Robot 10 parameter compensation method based on Newton–Raphson method3 citations · 2023
- 7Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA1 citations · 2025