Two-stage 2D-to-3D reconstruction of realistic microstructures: Implementation and numerical validation by effective properties
arXiv:2301.09313 · doi:10.1016/j.cma.2023.116098
Abstract
Realistic microscale domains are an essential step towards making modern multiscale simulations more applicable to computational materials engineering. For this purpose, 3D computed tomography scans can be very expensive or technically impossible for certain materials, whereas 2D information can be easier obtained. 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. Building upon DMCR, this work introduces a highly accurate two-stage reconstruction algorithm that refines the DMCR results under consideration of microstructure descriptors. 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. 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. 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.
References in corpus (13)
- Adam: A Method for Stochastic Optimization
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Modeling Heterogeneous Materials via Two-Point Correlation Functions: I. Basic Principles
- A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
- Generating 3D structures from a 2D slice with GAN-based dimensionality expansion
- Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
- Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy
- Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)
- The mechanical response of cellular materials with spinodal topologies
- Inferring low-dimensional microstructure representations using convolutional neural networks
- Model-data-driven constitutive responses: application to a multiscale computational framework
- Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
- A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures