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Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, Germany.ORCID iD: 0000-0002-7171-1219
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.ORCID iD: 0000-0002-8704-8538
Institute of Photonics, Gottfried Wilhelm Leibniz Universität Hannover, Hannover, Germany.
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2024 (English)In: Composite structures, ISSN 0263-8223, E-ISSN 1879-1085, Vol. 327, article id 117601Article in journal (Refereed) Published
Abstract [en]

We introduce an interpretable stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene-enhanced composites (PGECs). This method encompasses the propagation of uncertain input parameters from the meso to macro scale, implemented through a foundational bottom-up multi-scale framework. In this context, Representative Volume Elements in Finite Element Modeling (RVE-FEM) are employed to derive the homogenized thermal conductivity. Besides, we employ two sets of techniques: Regression-tree-based methods (Random Forest and Gradient Boosting Machine) and Neural networks-based approaches (Artificial Neural Networks and Deep Neural Networks). To ascertain the relative influence of factors on output estimations, the SHapley Additive exPlanations (SHAP) algorithm is integrated. This interpretable machine learning methodology demonstrates strong alignment with published experimental data. It holds promise as an efficient and versatile tool for designing new composite materials tailored to applications involving thermal management.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 327, article id 117601
Keywords [en]
Polymeric graphene-enhanced composites (PGECs), Interpretable Integrated Learning, Stochastic multi-scale modeling, Thermal properties, Data-driven technique
National Category
Composite Science and Engineering Applied Mechanics
Identifiers
URN: urn:nbn:se:umu:diva-215912DOI: 10.1016/j.compstruct.2023.117601Scopus ID: 2-s2.0-85175088621OAI: oai:DiVA.org:umu-215912DiVA, id: diva2:1807939
Funder
The Kempe Foundations, JCK-2136EU, Horizon 2020, 101016854J. Gust. Richert stiftelse, 2023-00884Swedish Research Council, 2018-05973Swedish Research Council, 2022-06725Available from: 2023-10-29 Created: 2023-10-29 Last updated: 2023-11-10Bibliographically approved

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Liu, BokaiLu, WeizhuoOlofsson, Thomas

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