Turbulence, Coherent Structures, Dynamical Systems and SymmetryP. Holmes, J. L. Lumley, G. Berkooz, and C. W. Rowley, 2nd ed., Cambridge University Press, Cambridge, England, U.K., 2012, 386 pp., $90

Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm

Limiting oneself to the use of solely the indentation curve prevents one from obtaining valuable
information about anisotropic materials through the indentation test. Mapping the residual deformation
(imprint shape) can help us identify a larger number of material properties. In the present work,
we propose an approach that automatically determines the minimum number of parameters needed in
order to characterize the imprint shape and link them with the material properties in the constitutive law.

Identification of material properties with indentation test and shape manifold learning approach

Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences, i.e., thermal histories. The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process, such as critical temperature ranges and fundamental thermal frequencies. We systematically compare the developed approach with other machine learning methods. The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data. It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.

/pdf/mechanistic-data-driven-prediction-of-as-built-mechanical-1jih8tieva.pdf

Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

Adaptive sparse grid based HOPGD: Toward a nonintrusive strategy for constructing space-time welding computational vademecum

Multi-parametric space-time computational vademecum for parametric studies: Application to real time welding simulations

Standard numerical simulations for optimization or inverse identification of welding processes remain costly and difficult due to their multi-parametric aspect and inherent complexity. The aim of this paper is to propose a non-intrusive strategy for building computational vademecums dedicated to real-time simulations of nonlinear thermo-mechanical problems. There is in essence, a set of precomputed space–time parametric solutions (snapshots), selected by an appropriate approach in the parameter space and stored in memory as quasi-optimal reduced bases (RBs) provided by the proper orthogonal decomposition method. Once the RBs are obtained, the computational vademecums can be used online and provide real-time space–time transient nonlinear thermo-mechanical solutions for any desired parameter value. The contributions of the paper consist in a space–time RBs interpolation approach with the Grassmann manifolds method, and a localized multigrid selection method that allows an automatic selection of snapshots in the parameter areas of interest for a given level of accuracy. As application, the welding simulation is considered with a transient non-linear thermo-mechanical model using the finite element method. It is shown that the moving frame allows an optimal design of the RBs. A good efficiency of the proposed approach is demonstrated. Computational vademecums can be used for optimization or inverse identification problems of welding.

/pdf/space-time-pod-based-computational-vademecums-for-parametric-50y6k9s5py.pdf

Ye Lu

Papers

Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

Adaptive sparse grid based HOPGD: Toward a nonintrusive strategy for constructing space-time welding computational vademecum

Multi-parametric space-time computational vademecum for parametric studies: Application to real time welding simulations

Space–time POD based computational vademecums for parametric studies: application to thermo-mechanical problems

An efficient and robust staggered algorithm applied to the quasi-static description of brittle fracture by a phase-field approach