DMN is a physics-embedded machine learning model that learns the microscale material morphologies hidden in representative volume elements of composites through offline training. In this work, we present a machine learning-based multiscale method by integrating injection molding-induced microstructures, material homogenization, and Deep Material Network (DMN) in the finite element simulation software LS-DYNA for structural analysis of SFRC. Typically, SFRC structures are manufactured by injection molding, which induces heterogeneous microstructures, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical analyses. Short-fiber-reinforced composites (SFRC) are high-performance engineering materials for lightweight structural applications in the automotive and electronics industries. Together with the free accessibility of the presented algorithms in MCRpy, the excellent results in this study motivate interdisciplinary cooperation in applying numerical multiscale simulations for computational materials engineering. After a detailed discussion of systematic errors in the descriptor space, the reconstructed microstructures are compared to the reference in terms of the numerically obtained effective elastic and plastic properties. Furthermore, the 2D-to-3D reconstruction is validated using a real computed tomography (CT) scan of a recently developed beta-Ti/TiFe alloy as well as anisotropic "bone-like" spinodoid structures. Building upon DMCR, this work introduces a highly accurate two-stage reconstruction algorithm that refines the DMCR results under consideration of microstructure descriptors. Based on a single or three orthogonal 2D slices, the recently proposed differentiable microstructure characterization and reconstruction (DMCR) algorithm is able to reconstruct multiple plausible 3D realizations of the microstructure based on statistical descriptors, i.e., without the need for a training data set. For this purpose, 3D computed tomography scans can be very expensive or technically impossible for certain materials, whereas 2D information can be easier obtained. Realistic microscale domains are an essential step towards making modern multiscale simulations more applicable to computational materials engineering. The results of mono-and polycrystal properties for both, experimental and analytical findings, are tabulated in clear and concise form, so that they are readily accessible to design engineers. For the application of the effective properties in practicable calculations, this implies that special emphasis must be placed on the origin of these data. The resulting deviations are discussed whereby the reasons for these discrepancies are identified. The results are also contrasted to the measured findings. In context of present material, the methods are applied and effective properties are predicted analytically while results are compared in terms of the different approaches applied and the material data sets accessed. We here give a synopsis on established analytical approaches used to predict effective values as well as a review on experimental outcomes at crystal and aggregate level. The silicon single crystals obey cubic symmetry while for the aggregate, at random orientation of its constituents, isotropy results. The problem of effective elastic parameters of polycrystals is also a question of material symmetry. For a safe and economic operation with this material, the most accurate prediction or measurement of the elastic properties possible is of interest in the first place even if the focus is on the analysis of the inelastic behavior and related reliability and service life predictions. Thereby, the use of polycrystalline silicon has a very wide range of application. For instance, it is an extremely important material for renewable energy systems like photovoltaics. Silicon has a large impact on today's world economy, also known as Silicon Age.
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