Professor Wing Kam Liu
Hierarchical Deep Learning Neural Network (HiDeNN)-AI for process design and performance prediction of material systems
Professor Wing Kam Liu is the Walter P. Murphy Professor of Northwestern University, Director of Global Center on Advanced Material Systems and Simulation, Past President (2018-present) and President (2014-2018) of the International Association for Computational Mechanics (IACM), Past Chair (2017-2018) and Chair (2015-2016) of the US National Committee on TAM and Member of Board of International Scientific Organizations, both within the US National Academies.
Selected synergistic activities includes the development of ICME multiscale theories, methods, and software with experimental validations for the design and analysis of engineering material systems, materials design, advanced and additive manufacturing; and technology transfer.
He is Cited by Institute for Scientific Information (ISI) as one of the most highly cited, influential researchers in Engineering (2001) and Computer Science (2014). He has over 39 years of engineering and manufacturing consulting, including a broad array of companies and industries, small businesses, and international corporations. Liu’s selected honors include the Distinguished Achievement Team Award for industry-university-government partnerships from the DOE Vehicle Technologies Office; Japan Society of Computational Engineering Sciences Grand Prize; Computational Mechanics Award from Japanese Society of Mechanical Engineers; Honorary Professorship from Dalian University of Technology, IACM Gauss-Newton Medal (highest honor) and Computational Mechanics Award; ASME Dedicated Service Award, ASME Robert Henry Thurston Lecture Award, ASME Gustus L. Larson Memorial Award, ASME Pi Tau Sigma Gold Medal and ASME Melville Medal; John von Neumann Medal (highest honor) and Computational Structural Mechanics Award from the US Association of Computational Mechanics (USACM).
He was the founding Director of the NSF Summer Institute on Nano Mechanics and Materials and Founding Chair of the ASME NanoEngineering Council. He is the editor of two International Journals and honorary editor of two journals and has been a consultant for more than 20 organizations. Liu has written four books; and he is a Fellow of ASME, ASCE, USACM, AAM, and IACM.
We propose a mechanistic Artificial Intelligence (AI) framework, called Hierarchical Deep Learning Neural Networks or HiDeNN-AI [1,2] for discovering the multiscale linkage of process-structure-property of additive manufacturing systems.
The HiDeNN-AI discovery has three sequentially executed steps: (1) using available data to characterize an unknown physical process in manufacturing, (2) enriching the database and training with mechanistic knowledge coming from the system identification in step (1) with uncertain parameters to create a reduced order model with uncertainty, and (3) using the reduced order model to generate sufficient data to discover new robust mathematical principles that are able to (a) perform predictive solutions for design and optimization, and (b) provide simple relationship for online monitoring and control. We have applied this HiDeNN-AI framework to address the Air Force Research Lab (AFRL) AM modeling challenges [3, 4, 5]; and for the prediction of the as-built mechanical properties . To further enhance HiDeNN-AI, a reduced-order modeling method accounting input uncertainty, called the Tensor Decomposition (TD) , is being developed. The so-called HiDeNN-AI-TD is expected to solve the general engineering design and manufacturing problems in high dimensional space-time-parametric domains at deep discount in computational cost.
Once the offline database is set up, the mechanistic machine learning module of HiDeNN-AI can be activated for process design, real time system monitoring and control or the identification of key processing parameters for the desired performance of the manufactured material systems with uncertainty quantification. Various results comparing the HiDeNN-AI-TD approach with the conventional machine learning models will be shown using real-time IR in-situ measurement, and high-frequency thermal signatures for the predictions of mechanical properties and the detection of lack of fusion and keyhole porosities. Similar applications to polymer matrix composites will be presented.
 Zhang, L., Cheng, L., Li, H., Gao, J., Yu, C., Domel, R., Yang, Y., Tang, S. and Liu, W.K., 2021. Hierarchical deep-learning neural networks: finite elements and beyond. Computational Mechanics, 67(1), pp.207-230.
 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. https://doi.org/10.1016/j.cma.2020.113452.
 Gan, Z., Jones, K.K., Lu, Y., and Liu, W.K., 2021, Benchmark study of melted track geometries in laser powder bed fusion of Inconel 625, Integrating Materials and Manufacturing Innovation, 10, pages 177–195.
 Saha, S., Kafka, O.L., Lu, Y., Liu, W. K., 2021, Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Material Model Parameter Identification, Integrating Materials and Manufacturing Innovation, 10, pages142–156 (2021). (https://doi.org/10.1007/s40192-021-00208-5)
 Saha, S., Kafka, O.L., Lu, Y., Liu, W. K., 2021, Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure through Advanced Homogenization, Integrating Materials and Manufacturing Innovation, 10, pages 360–372 (2021). (https://doi.org/10.1007/s40192-021-00221-8)
 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, 7(1), 1–12. https://doi.org/10.1038/s41524-021-00555-z
 Lu, Y., Mojumder, S., Saha, S., Liu, W. K., An extended tensor decomposition model reduction method: training, prediction, and design under uncertainty, In prep.
 Lin, H., Mao, Y., Carter, F., Gao, Z., Agrawal, A., Liu, W.K., Cao, J. Mechanistic artificial intelligence for defect detection using in-situ IR measurements and physics-informed models, In prep.