Chengnan Jin
Papers
1
Total Citations
12
H-Index
1
About
Chengnan Jin is a researcher in advanced manufacturing and welding process control, with a primary focus on intelligent monitoring and quality assurance in flux-cored arc welding—a technique critical to industries like shipbuilding. His most cited work, "Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning" (2021), addresses a persistent industrial challenge: maintaining consistent weld bead quality despite variable weld gaps caused by shell forming errors. By integrating deep learning algorithms with real-time monitoring, Jin developed a system capable of detecting and compensating for gap variations during the weaving process, enabling adaptive quality control without human intervention. This contribution has garnered 12 citations, reflecting its relevance to both academic research and practical manufacturing. Jin’s work stands out for bridging the gap between traditional welding metallurgy and modern artificial intelligence, offering a scalable solution for automated defect prevention. His research is particularly notable for its direct application to high-stakes environments where weld integrity is paramount, positioning him as a key figure in the evolution of smart welding technologies.
Research Focus
Key Achievements
Top Papers
- 1