Digitalizing metallic materials from image segmentation to multiscale solutions via physics informed operator learning

npj Computational Materials, vol. 11

Abstract

Abstract Fast prediction of microstructural responses based on realistic material topology is vital for linking process, structure, and properties. This work presents a digital framework for metallic materials using microscale features. We explore deep learning for two primary goals: (1) segmenting experimental images to extract microstructural topology, translated into spatial property distributions; and (2) learning mappings from digital microstructures to mechanical fields using physics-informed operator learning. Loss functions are formulated using discretized weak or strong forms, and boundary conditions-Dirichlet and periodic-are embedded in the network. Input space is reduced to focus on key features of 2D and 3D materials, and generalization to varying loads and input topologies are demonstrated. Compared to FEM and FFT solvers, our models yield errors under 1–5% for averaged quantities and are over 1000× faster during 3D inference.

Authors 13

1. Shahed Rezaei corresponding Aachen Access e.V

   Access

   Affiliation as printed

   ACCESS e.V., Aachen, Germany

2. Kianoosh Taghikhani Aachen Access e.V

   Access

   Affiliation as printed

   ACCESS e.V., Aachen, Germany

3. Alexandre Viardin Aachen Access e.V

   Access

   Affiliation as printed

   ACCESS e.V., Aachen, Germany

4. Reza Najian Asl

   Technical University of Munich

   Affiliation as printed

   Technical University of Munich, München, Germany

5. Nikhil Vijay Jagtap Aachen Institute for Metal Forming

   RWTH Aachen University

   Affiliation as printed

   Institute for Metal Forming, Aachen, Germany

6. David Benjamin Bailly Aachen Institute for Metal Forming

   RWTH Aachen University

   Affiliation as printed

   Institute for Metal Forming, Aachen, Germany

7. Hannah Naber Aachen Steel Institute

   RWTH Aachen University

   Affiliation as printed

   Steel Institute, Aachen, Germany

8. Alexander Gramlich Aachen Steel Institute

   RWTH Aachen University

   Affiliation as printed

   Steel Institute, Aachen, Germany

9. Tim Brepols Aachen Institute of Applied Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Mechanics, RWTH Aachen University, Aachen, Germany

10. Mustapha Abouridouane Aachen Manufacturing Technology Institute (MTI)

    RWTH Aachen University

    Affiliation as printed

    Manufacturing Technology Institute, Aachen, Germany

11. Ulrich Krupp Aachen Steel Institute

    RWTH Aachen University

    Affiliation as printed

    Steel Institute, Aachen, Germany

12. Thomas Bergs Aachen Manufacturing Technology Institute (MTI)

    RWTH Aachen University

    Affiliation as printed

    Manufacturing Technology Institute, Aachen, Germany

13. Markus Apel Aachen Access e.V

    Access

    Affiliation as printed

    ACCESS e.V., Aachen, Germany