Mechanistic Modeling for Additive Manufacturing
Our group develops physics-based models that capture the thermal, mechanical, and materials-level phenomena governing advanced additive manufacturing processes. By elucidating fundamental process–structure–property relationships, we enable predictive simulation, data-informed process optimization, and defect-aware design strategies. These modeling capabilities serve as a foundation for accelerating innovation across our advanced AM research portfolio and for enhancing the reliability, performance, and scalability of next-generation AM technologies.
Our current research directions:
- Data-driven surrogate modeling for AM process prediction
- Physics-informed machine learning for process–structure–property mapping
- Multiphysics simulation of thermo-mechanical interactions
- Simulation-driven process optimization
- T. Gabor, Y. Wang, S. Akin, F. Zhou, J. Chen, MBG. Jun, "Design, modeling, and characterization of a pulsed cold spray system", Surface & Coatings Technology, 2025. [Link]
- T. Gabor, S. Akin, MBG. Jun, "Numerical studies on cold spray gas dynamics and powder flow in circular and rectangular nozzles", Journal of Manufacturing Processes, 2024. [Link]
- S. Akin, P. Wu, JT. Tsai, J. Chen, MBG. Jun, "A study on converging-diverging nozzle design for supersonic spraying of liquid droplets toward nanocoating applications", ASME Journal of Manufacturing Science & Engineering, 2023. [Link]
- T. Gabor, H. Yun, S. Akin, MBG. Jun, "Continuous coaxial nozzle designs for improved powder focusing in direct laser metal deposition", Journal of Manufacturing Processes, 2022. [Link]
- S. Akin, P. Wu, JT. Tsai, J. Chen, MBG. Jun, "A study on droplets dispersion and deposition characteristics under supersonic spray flow for nanomaterial coating applications", Surface & Coatings Technology, 2022. [Link]