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.
Crack initiation governs high cycle fatigue life and is susceptible to microstructural details. While corresponding microstructure-sensitive models are available, their validation is difficult. We propose a validation framework where a fatigue test is mimicked in a sub-modeling simulation by embedding the measured microstructure into the specimen geometry and adopting the experimental boundary conditions. Exemplary, a phenomenological crystal plasticity model was applied to predict deformation in ferritic steel (EN1.4003). Hotspots in commonly used fatigue indicator parameter maps are compared…
In this paper we report the numerical design and experimental validation of a pentamode cloak for scattering reduction of elliptical targets in underwater acoustics. Despite being a well-known theoretical concept, experimental validations of transformation-based acoustic cloaking for non-axisymmetric targets have been hindered due to the difficulties related to the complexity of the required material distribution. We overcome these by adopting a linear quasi-symmetric map defined in elliptic coordinates that allows for the design of a pentamode cloak with constant anisotropic elasticity and a …
Microstructure reconstruction, a major component of inverse computational materials engineering, is currently advancing at an unprecedented rate. While various training-based and training-free approaches are developed, the majority of contributions are based on generative adversarial networks. In contrast, diffusion models constitute a more stable alternative, which have recently become the new state of the art and currently attract much attention. The present work investigates the applicability of diffusion models to the reconstruction of real-world microstructure data. For this purpose, a hi…
Faces-classes of grains, often referred to as topological features, largely dictate the evolution of polycrystalline microstructures during grain growth. Realising these topological features is generally an arduous task, often demanding sophisticated techniques. In the present work, a distinct machine-learning algorithm is extended for the first time to comprehend the topological distribution of the grains constituting a polycrystalline continuum. This regression-based object-detection approach, besides significantly reducing human-efforts and ensuring computational efficiency, predicts the fa…
A link between microstructure evolution and macroscopic response in elasto-plasticity: formulation and numerical approximation of the higher-dimensional Continuum Dislocation Dynamics theory
Uncertainty quantification (UQ) plays a major role in verification and validation of computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive exp…
U.S. DOE National Energy Technology Laboratory（美国能源部国家能源技术实验室）；Carnegie Mellon University（卡内基梅隆大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Leidos Research Support Team（莱多斯研究支持团队）
Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-bas…
Predicting cement microstructure and mechanical properties in hydrating cement paste with a Phase-Field model
Duke University（杜克大学）；UCLouvain（天主教鲁汶大学）；ENPC, Institut Polytechnique de Paris, Univ Gustave Eiffel（法国国立路桥学校，巴黎理工学院，古斯塔夫·埃菲尔大学）；University of Montpellier（蒙彼利埃大学）
Increased demands for high-performance materials have led to advanced composite materials with complex hierarchical designs. However, designing a tailored material microstructure with targeted properties and performance is extremely challenging due to the innumerable design combinations and prohibitive computational costs for physics-based solvers. In this study, we employ a neural operator-based framework, namely Fourier neural operator (FNO) to learn the mechanical response of 2D composites. We show that the FNO exhibits high-fidelity predictions of the complete stress and strain tensor fiel…
This study details the development and validation of a new algorithm that determines the dominant habit plane of a transformed child phase from orientation maps of a single planar cross-section. The method describes the habit plane in terms of its five-parameter grain boundary character and couples it to the specific orientation relationship of the identified orientation variant. The symmetry operations associated with the specific orientation relationship of the variants are applied to transform habit plane traces as determined in the specimen-fixed reference frame into the parent or child re…
We employ physics-informed neural networks (PINNs) to quantify the microstructure of a polycrystalline Nickel by computing the spatial variation of compliance coefficients (compressibility, stiffness and rigidity) of the material. The PINN is supervised with realistic ultrasonic surface acoustic wavefield data acquired at an ultrasonic frequency of 5 MHz for the polycrystalline material. The ultrasonic wavefield data is represented as a deformation on the top surface of the material with the deformation measured using the method of laser vibrometry. The ultrasonic data is further complemented …
Quantum Extreme Reservoir Computing for Phase Classification of Polymer Alloy Microstructures
Graduate School of Science and Technology, Keio University（庆应义塾大学理工学研究科）；Quantum Information Science and Technology Unit, Okinawa Institute of Science and Technology Graduate University（冲绳科学技术大学院大学量子信息科学与技术单元）；Department of Mechanical Engineering, Keio University（庆应义塾大学机械工程系）
The ability to image materials at the microscale from long-wavelength wave data is a major challenge to the geophysical, engineering and medical fields. Here, we present a framework to constrain microstructure geometry and properties from long-scale waves. To realistically quantify microstructures we use two-point statistics, from which we derive scale-dependent effective wave properties - wavespeed and attenuation - using strong-contrast expansions (SCE) for (visco)elastic wavefields. By evaluating various two-point correlation functions we observe that both effective wavespeeds and attenuati…
In the present work, 3D convolutional neural networks (CNNs) are trained to link random heterogeneous, two-phase materials of arbitrary phase fractions to their elastic macroscale stiffness thus replacing explicit homogenization simulations. In order to reduce the uncertainty of the true stiffness of the synthetic composites due to unknown boundary conditions (BCs), the CNNs predict beyond the stiffness for periodic BC the upper bound through kinematically uniform BC, and the lower bound through stress uniform BC. This work describes the workflow of the homogenization-CNN, from microstructure …
Discovering relationships between materials' microstructures and mechanical properties is a key goal of materials science. Here, we outline a strategy exploiting Bayesian optimization to efficiently search the multidimensional space of microstructures, defined here by the size distribution of precipitates (fixed impurities or inclusions acting as obstacles for dislocation motion) within a simple two-dimensional discrete dislocation dynamics model. The aim is to design a microstructure optimizing a given mechanical property, e.g., maximizing the expected value of shear stress for a given strain…
Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA（电气与计算机工程系，佛罗里达大学，盖恩斯维尔，佛罗里达州，美国）；Northwestern University（西北大学）；Lawrence Livermore National Laboratory, Livermore, CA, United States（劳伦斯利弗莫尔国家实验室，利弗莫尔，加利福尼亚州，美国）
