Profile photo  of Hossein Ghorbani Menghari

Hossein Ghorbani

Lecturer

Office: Civil-Mechanical  132  (Campus Map)

Education

Mashhad Institute of Technology, Iran - Manufacturing Engineering – Machine Tools B.S.
Isfahan University of Technology, Iran - Mechanical Engineering (Manufacturing and Production Engineering) M.S.
Pusan National University, South Korea - Mechanical Engineering Ph.D.

Brief Bio

Dr. Hossein Ghorbani-Menghari is a Lecturer in the Department of Mechanical Engineering at the South Dakota School of Mines & Technology (SDSMT). He earned his Ph.D. in Mechanical Engineering from Pusan National University, South Korea, where his research focused on computational mechanics, powder compaction, and advanced manufacturing processes. Prior to joining SDSMT, he served as a Postdoctoral Researcher at Pusan National University, conducting research on crystal plasticity finite element modeling, fatigue prediction, and lightweight materials.

Dr. Ghorbani-Menghari's research interests include finite element analysis, metal forming, powder metallurgy, additive manufacturing, tribology, and machine learning applications in manufacturing. He has authored numerous peer-reviewed journal articles and conference papers, published technical books, and actively contributes to the engineering community through student mentorship, journal reviewing, and collaborative research projects with academic and industry partners.

Research Expertise

Dr. Ghorbani-Menghari's research focuses on computational mechanics and advanced manufacturing, integrating physics-based modeling, finite element analysis, and data-driven optimization to understand and improve manufacturing processes. His work spans powder metallurgy, metal forming, additive manufacturing, tribology, and fatigue analysis, with an emphasis on predicting material behavior across multiple length scales. He employs advanced simulation techniques, including crystal plasticity finite element modeling (CP-FEM), discrete element methods (DEM), multiscale modeling, and machine learning approaches to enhance process design, material performance, and manufacturing efficiency. Overall, his research aims to develop intelligent and predictive engineering frameworks that support the design of advanced materials and next-generation manufacturing systems.