Jiecai Feng

Shanghai University, Harbin Institute of Technology

Papers

Total Citations

H-Index

About

Jiecai Feng is a leading researcher in intelligent welding and robotic manufacturing, with a focus on vision-based sensing and adaptive process control. His work addresses critical challenges in automated welding, particularly the automatic identification of weld seam types and the optimization of welding parameters for high-strength materials. Feng’s most cited paper (2020, 41 citations) introduces a novel silhouette-mapping method using vision sensors to classify multi-type weld seams, enabling robots to autonomously adjust trajectory and parameters in variable environments—a key step toward fully intelligent welding systems. He also advanced laser-MAG hybrid welding techniques (2016, 12 citations) by developing synchronized weaving and scanning methods that reduce porosity and improve weld morphology in high-strength steel. More recently (2023), Feng proposed a systematic framework using prototype features to correct anomalous pre-welding workpiece postures, enhancing robustness in industrial applications. With over 59 cumulative citations, his contributions bridge computer vision and welding metallurgy, offering practical solutions for adaptive, defect-free manufacturing. Feng’s work is foundational for students and engineers seeking to integrate sensing and control in next-generation robotic welding.