Process-structure-property predictions for metal additive manufacturing
We developed a multi-scale high performance computing framework for additive manufacturing process-structure-property (PSP) predictions, based on reduced order modeling techniques. Examples of work include thermal fluid analysis for temperature evolution, elastoplastic analysis for residual stress, phase field modeling of microstructure evolution, and stochastic modeling of surface roughness.
Part-scale process modeling
Goal: efficient and accurate simulations of temperature evolution, residual stress, part distortion, etc.
Adaptive hyper reduced order thermal fluid analysis for temperature and melt pool predictions [Lu et al. CMAME, 2020]
Extended tensor decomposition based elastoplastic analysis for residual stress and strain predictions [Lu et al. CMAME, 2024]
Microstructure-scale modeling
Goal: predictions of microstructure evolution, mechanical properties, defects, etc.
Phase field modeling of grain structure evolution [Yuan et al, JCP, 2026]
Stochastic modeling
Goal: quantification of melt pool variability, surface roughness, porosity, etc.
Stochastic thermal fluid analysis for surface roughness and porosity predictions [Li et al, AM, 2024]
Publications:
Yuan et al. , An efficient and energy stable framework for phase field simulations of grain growth in additive manufacturing, Journal of Computational Physics, 2026.
Lu et al., Extended tensor decomposition model reduction methods: Training, prediction, and design under uncertainty, Computer Methods in Applied Mechanics and Engineering, 2024.
Li et al., Statistical Parameterized Physics-Based Machine Learning Digital Shadow Models for Laser Powder Bed Fusion Process, Additive Manufacturing, 2024.
Lu et al., Reduced order machine learning finite element methods: concept, implementation, and future applications, Computer Modeling in Engineering & Science, 2021.
Lu et al., Adaptive hyper reduction for additive manufacturing thermal fluid analysis, Computer Methods in Applied Mechanics and Engineering, 2020.
Phase field modeling of fracture
We developed a phase field modeling framework for quasi-static fracture of brittle materials. This development includes a robust solution solver for overcoming the unstable cracking and an efficient strategy for imposing the irreversible damage field. The developed phase field method has been applied to various fracture problems and the simulated fracture patterns showed a good agreement with experimental observations.
Publications:
Lu et al., An efficient and robust staggered algorithm applied to the quasi-static description of brittle fracture by a phase-field approach, Computer Methods in Applied Mechanics and Engineering, 2020.
Reduced order modeling for welding
We developed a data-driven reduced order surrogate modeling technique, namely High Order Proper Generalized Decomposition (HOPGD), for real time predictions of the welding process. The reduced order surrogate models serve as digital twins to the physical welding process and can provide real time predictions for residual stresses and distortions. The digital twin model can avoid solving repetitively the computationally expensive nonlinear space-time multiphysics model and can be used for performing the challenging high dimensional parametric studies, including process condition optimization, material calibration, and uncertainty quantification, etc.
Digital twins for accelerated process optimization, material calibration, uncertainty quantification, etc.
Publications:
Lu et al., Datadriven HOPGD based computational vademecum for welding parameters identification, Computational Mechanics, 2019.
Lu et al., Adaptive sparse grid based HOPGD: Towards a nonintrusive strategy for constructing space-time welding computational vademecum, International Journal for Numerical Methods in Engineering, 2018.
Lu et al., Multi-parametric space-time computational vademecum for parametric studies: Application to real time welding simulations, Finite Elements in Analysis and Design, 2018.
Lu et al., Space–time POD based computational vademecums for parametric studies: application to thermo-mechanical problems, Advanced Modeling and Simulation in Engineering Sciences, 2018.