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An FE–DMN method for the multiscale analysis of short fiber reinforced plastic components

Gajek, Sebastian 1; Schneider, Matti 1; Böhlke, Thomas ORCID iD icon 1
1 Institut für Technische Mechanik (ITM), Karlsruher Institut für Technologie (KIT)


In this work, we propose a fully coupled multiscale strategy for components made from short fiber reinforced composites, where each Gauss point of the macroscopic finite element model is equipped with a deep material network (DMN) which covers the different fiber orientation states varying within the component. These DMNs need to be identified by linear elastic precomputations on representative volume elements, and serve as high-fidelity surrogates for full-field simulations on microstructures with inelastic constituents.

We discuss how to extend direct DMNs to account for varying fiber orientation, and propose a simplified sampling strategy which significantly speeds up the training process. To enable concurrent multiscale simulations, evaluating the DMNs efficiently is crucial. We discuss dedicated techniques for exploiting sparsity and high-performance linear algebra modules, and demonstrate the power of the proposed approach on an injection molded quadcopter frame as a benchmark component. Indeed, the DMN is capable of accelerating two-scale simulations significantly, providing possible speed-ups of several magnitudes.

Verlagsausgabe §
DOI: 10.5445/IR/1000134252
Veröffentlicht am 23.06.2021
DOI: 10.1016/j.cma.2021.113952
Zitationen: 36
Zitationen: 43
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technische Mechanik (ITM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0045-7825, 1879-2138
KITopen-ID: 1000134252
Erschienen in Computer Methods in Applied Mechanics and Engineering
Verlag Elsevier
Band 384
Seiten Art.-Nr.: 113952
Schlagwörter Micromechanics; Computational homogenization; Multiscale simulation; Deep material networks; Laminates; Short fiber reinforced composites
Nachgewiesen in Dimensions
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