Junying Min

Tongji University

Papers

9

Total Citations

92

H-Index

6

About

Junying Min is a researcher specializing in advanced manufacturing processes, robotics-integrated forming technologies, and ultrahigh-strength steel processing. His work sits at the intersection of laser-assisted forming, robotic systems, and materials science, with a particular focus on developing innovative solutions for manufacturing components from challenging high-strength materials. Min's most significant contributions center on laser-assisted robotic roller forming (LRRF), a process he has helped pioneer to overcome the formability limitations of low-ductility ultrahigh-strength steels such as martensitic and quenching-and-partitioning grades exceeding 1180 MPa. His research has produced both practical process innovations and sophisticated thermo-metallurgical-mechanical models that capture microstructure evolution during forming, demonstrating a rare combination of experimental and theoretical rigor. His work on iterative path compensation methods for thin-walled profiles further highlights his commitment to precision manufacturing outcomes. Beyond forming processes, Min has made notable contributions to industrial robot characterization, developing joint stiffness identification methods using 3D digital image correlation techniques — directly addressing the well-known rigidity limitations of serial robots in manufacturing settings. With approximately 92 cumulative citations across his most impactful papers, Min's research is shaping the future of flexible, high-precision robotic manufacturing for the automotive and structural engineering sectors.

Research Focus

Key Achievements

6
H-Index
9
Papers
92
Total Citations
10
Avg Citations/Paper
🏆 Most Cited Paper
Laser-assisted robotic roller forming of an ultrahigh strength martensitic steel
19 citations · 2022
📈 Most Prolific Year: 2023 (4 Papers)
🤝 Key Collaborators: 21
🏛 Institutions: Tongji University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago