Faculty DirectoryZhengtao Gan
Research Assistant Professor
Contact
2145 Sheridan RoadTech A315
Evanston, IL 60208-3109
773-865-0314Email Zhengtao Gan
Website
Departments
Education
Ph.D. Sep. 2012 - Jul. 2017
Chinese Academy of Sciences, Beijing, China
Institute of Mechanics
B.E. Sep. 2008 - Jul. 2012
Chongqing University, China
Department of Mechanical Engineering
Research Interests
Multiscale and multiphysics modeling for advanced manufacturing. I am interested in the development of three-dimensional thermal-fluid-mechanical models incorporating physics at multiple scales for advanced manufacturing, such as additive manufacturing of metals and polymer. The model predictions were validated by highly controlled benchmark experiments conducted by the Air Force Research Laboratory (AFRL) and the National Institute of Standards and Technology (NIST). Northwestern team led by me was identified as Top Performer in AFRL Additive Manufacturing Modeling Challenge Series: Micro-scale Process-to-Structure Predictions (2020). My modeling work was also awarded 1st places by NIST for the best modeling results predicting the cooling rate, grain structure and dendritic microstructure in AM-Bench 2018.
Mechanistic data science for engineering. I am interested in data-driven discovery for engineering problems, which is a multidisciplinary field including mechanics, material, physics and computer science. I am developing data-driven methods and theories to understand the relationships between input parameters and final performance of a system and discover reduced-embedded structures and hidden physical laws in the data. I and my collaborators in Argonne National Laboratory (ANL) discovered strikingly simple but universal scaling laws using high-fidelity high-speed synchrotron x-ray imaging and multiphysics modeling. Dimensional analysis and machine learning were leveraged to identify a new dimensionless number, “Keyhole number”, to predict vapor depression aspect ratio and porosity formation in additive manufacturing.
Selected Publications
Xie, X., Bennett, J., Saha, S., Lu, Y., Cao, J., Liu, W. K.#, & Gan, Z.# (2021). Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing. npj: Computational Materials - Nature, 7(1), 1–12. (#Corresponding author).
Gan, Z., Kafka, O.L., Parab, N., Zhao, C., Fang, L., Heinonen, O., Sun, T., Liu, W.K., Universal scaling laws of keyhole stability and porosity in 3D printing of metals. Nature Communications, 12, 2379 (2021).
Saha, S.*, Gan, Z.*, Cheng, L., Gao, J., Kafka, O.L., Xie, X., Li, H., Tajdari, M., Kim, H.A. and Liu, W.K., 2021. Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering. Computer Methods in Applied Mechanics and Engineering, 373, p.113452 (*Saha, S., Gan, Z., Cheng, L. contributed equally.).
Gan, Z., Lian, Y., Lin, S.E., Jones, K.K., Liu, W.K. and Wagner, G.J. (2019). Benchmark study of thermal behavior, surface topography, and dendritic microstructure in selective laser melting of Inconel 625. Integrating Materials and Manufacturing Innovation, Special issue: Additive Manufacturing Benchmarks 2018, 1-16.
Gan, Z., Yu, G., He, X. and Li, S. (2017). Numerical simulation of thermal behavior and multicomponent mass transfer in direct laser deposition of Co-base alloy on steel, International Journal of Heat and Mass Transfer, 104: 28-38. (Top 1% highly cited papers in the field of Engineering)
Gan, Z., Liu, H., Li, S., He, X. and Yu, G. (2017). Modeling of thermal behavior and mass transport in multi-layer laser additive manufacturing, International Journal of Heat and Mass Transfer, 111: 709-722. (Top 1% highly cited papers in the field of Engineering)