Martensitic materials show a complex, hierarchical microstructure containing structural domains separated by various types of twin boundaries. Several concepts exist to describe this microstructure on each length scale, however, there is no comprehensive approach bridging the whole range from the nano- up to the macroscopic scale. Here, we describe for a Ni-Mn-based Heusler alloy how this hierarchical microstructure is built from scratch with just one key parameter: the tetragonal distortion of the basic building block at the atomic level. Based on this initial block, we introduce five successive levels of nested building blocks. At each level, a larger building block is formed by twinning the preceding one to minimise the relevant energy contributions locally. This naturally explains the occurrence of different types of twin boundaries. We compare this scale-bridging approach of nested building blocks with experiments in real and reciprocal space. Our approach of nested building blocks is versatile as it can be applied to the broad class of functional materials exhibiting diffusionless transformations.
Martensitic transformation induced by cooling NiTi wire under various tensile stresses: martensite variant microstructure, textures, recoverable strains and plastic strains
Stress induced martensitic transformation in NiTi at elevated temperatures: martensite variant microstructures, recoverable strains and plastic strains
In particular types of layer- or lamellar-like microstructures such as pearlite and lath martensite, plastic slip occurs favorably in directions parallel to inter-lamellar boundaries. This may be due to the interplay between morphology and crystallographic orientation or, more generally, due to constraints imposed on the plastic slip due to the lamellar microstructural geometry. This paper proposes a micromechanics based, computationally efficient, scale independent model for particular type of lamellar microstructures containing softer lamellae, which are sufficiently thin to be considered as…
The onset of plasticity in quenched martensitic microstructures is characterized by a low initial yield stress followed by an extremely strong initial hardening response, and then a sudden hardening saturation. Literature attributes this behavior to residual stresses and dislocations inherited from the martensitic transformation, or to microstructural heterogeneities causing strength differences among the grains. Here, we argue that orientation-dependent yielding of lath martensite due to inter-lath sliding, which induces a substructure boundary sliding mechanism, may also contribute significa…
Titanium and its alloys undergo temperature-driven martensitic phase transformation leading to the development of complex microstructures at mesoscale. Optimizing the mechanical properties of these materials requires an understanding of the correlations between the processing parameters and the mechanisms involved in the microstructure formation and evolution. In this work, we study the temperature-induced phase transition from BCC to HCP in pure titanium by atomistic modeling and investigate the influence of local stress conditions on the final martensite morphology. We simulate the transitio…
记忆合金的宏观性能是由马氏体相变形成的介观组织继承而来的。在立方相到正交相的转变中，与具有过渡层的常规奥氏体/孪晶马氏体界面相比，马氏体的单个变体对于一些特殊的晶格参数可以具有与奥氏体相容的（精确的）界面。在实验上，相compat，在热滞后的结果急剧下降，并在循环中产生非常稳定的功能特性。在这里，我们调查观察到的微观结构在Ti 50 Ni 50-xPdx合金，进行立方正交马氏体相变，使用三维相场方法。我们将表明，模拟结果与透射电子显微镜观察非常吻合。然而，热滞后下降的理解需要相变与塑性活动的耦合。我们将在热弹性的框架内讨论这一点，这是马氏体相变的一般特征。
We study the branching of twins appearing in shape memory alloys at the interface between austenite and martensite. In the framework of three-dimensional non-linear elasticity theory, we propose an explicit, low-energy construction of the branched microstructure, generally applicable to any shape memory material without restrictions on the symmetry class of martensite or on the geometric parameters of the interface. We show that the suggested construction follows the expected energy scaling law, i.e., that (for the surface energy of the twins being sufficiently small) the branching leads to en…
For the first time, cyclic re-austenitization is carried out for additively manufactured high-strength low-alloy (HSLA) steels in order to refine the microstructure by reducing the prior austenite grain (PAG) size. In this work, HSLA-100 steels processed using laser powder bed fusion (LPBF) technique are subjected to several cycles of re-austenitization using quenching dilatometry. Microstructure characterization for every cycle revealed the presence of bainite, martensite and martensite/austenite (M/A) islands. From the analysis of the dilatometry curves and extensive microstructure character…
Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation, Damage Analysis and High-Throughput Modelling Input
针对阈值法难以分割复杂显微组织的问题，提出基于 nnU-Net 与 GAN 的两种 AI 分割方法，并在双相钢 SEM 图像中验证其泛化性及面向表征、损伤分析和有限元建模的价值。
Fossil-free ironmaking is indispensable for reducing massive anthropogenic CO2 emissions in the steel industry. Hydrogen-based direct reduction (HyDR) is among the most attractive solutions for green ironmaking, with high technology readiness. The underlying mechanisms governing this process are characterized by a complex interaction of several chemical (phase transformations), physical (transport), and mechanical (stresses) phenomena. Their interplay leads to rich microstructures, characterized by a hierarchy of defects ranging across several orders of magnitude in length, including vacancies…
Effective and accurate characterization and quantification of complex microstructure of a heterogeneous material and its evolution under external stimuli are very challenging, yet crucial to achieving reliable material performance prediction, processing optimization and advanced material design. Here, we address this challenge by developing a set of novel hierarchical statistical microstructural descriptors, which we call the "n-point polytope functions" Pn, for quantitative characterization, representation and modeling of complex material microstructure and its evolution. These novel polytope…
Hierarchical microstructures are often invoked to explain the high resilience and fracture toughness of biological materials such as bone and nacre. Biomimetic material models inspired by those structural arrangements face the obvious challenge of capturing their inherent multi-scale complexity, both in experiments and in simulations. To study the influence of hierarchical microstructural patterns in fracture behavior, we propose a large scale three-dimensional hierarchical beam-element simulation framework, where we generalize the constitutive behavior of Timoshenko beam elasticity and Maximu…
We devise reduced-dimension metrics for effectively measuring the distance between two points (i.e., microstructures) in the microstructure space and quantifying the pathway associated with microstructural evolution, based on a recently introduced set of hierarchical $n$-point polytope functions $P_n$. The $P_n$ functions provide the probability of finding particular $n$-point configurations associated with regular $n$-polytopes in the material system, and a special sub-set of the standard $n$-point correlation functions $S_n$ that effectively decomposes the structural features in the systems …
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…
Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented …
Emerging hierarchical dislocation structures: Insights from scanning electron microscopy-electron backscatter diffraction in situ tensile testing and multifractal analysis
Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins
Center for Integrated Nanotechnologies（集成纳米技术中心）；Sandia National Laboratories（桑塔纳国家实验室）；Theoretical Division（理论 division）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Department of Mechanical Engineering（机械工程系）；Pohang University of Science and Technology（釜山科学技术大学）
We discuss the relevance of methods of graph theory for the study of damage in simple model materials described by the random fuse model. While such methods are not commonly used when dealing with regular random lattices, which mimic disordered but statistically homogeneous materials, they become relevant in materials with microstructures that exhibit complex multi-scale patterns. We specifically address the case of hierarchical materials, whose failure, due to an uncommon fracture mode, is not well described in terms of either damage percolation or crack nucleation-and-growth. We show that in…
