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• W4319302520 abstract "The use of crystal plasticity models in macroscopic numerical analysis still poses challenges. Component and crystal structures have different scales by several orders of magnitude. For this reason, a discretization of the crystal structure within the framework of the component is not reasonably possible. Therefore, for each integration point its microstructure is represented by a representative volume element. Due to the computational effortComputational efficiency associated with this coupling, the use of crystal plasticityCrystal plasticity models has been limited. They are mainly used in academia and with little application in component and process design. The approach taken here is to decouple computational effortComputational efficiency through machine learningMachine learning by training a neural networkNeural networks that eventually serves as a material model in macro-scale analysis. Based on the deformation gradient and the microstructure, the trained neural networkNeural networks reproduces the resulting stress response, where the conducted investigations also cover consecutive deformation patterns and general stress states." @default.
• W4319302520 created "2023-02-07" @default.
• W4319302520 creator A5006346206 @default.
• W4319302520 date "2023-01-01" @default.
• W4319302520 modified "2023-10-16" @default.
• W4319302520 title "Towards Machine Learning of Crystal Plasticity by Neural Networks" @default.
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• W4319302520 doi "https://doi.org/10.1007/978-3-031-22524-6_51" @default.
• W4319302520 hasPublicationYear "2023" @default.
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