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To build materials knowledge, rigorous description of the material structure and associated tools to explore and exploit information encoded in the structure are needed. These enable recognition, categorization and identification of different classes of microstructure and ultimately enable to link structure with properties of materials. Particular interest lies in the protocols capable of mining the essential information in large microstructure datasets and building robust knowledge systems that can be easily accessed, searched, and shared by the broader materials community. In this paper, we develop a protocol based on automated tools to classify microstructure taxonomies in the context of coarsening behavior which is important for long term stability of materials. Our new concepts for enhanced description of the local microstructure state provide flexibility of description. The mathematical description of microstructure that capture crucial attributes of the material, although central to building materials knowledge, is still elusive. The new description captures important higher order spatial information, but at the same time, allows down sampling if less information is needed. We showcase the classification protocol by studying coarsening of binary polymer blends and classifying steady state structures. We study several microstructure descriptions by changing the microstructure local state order and discretization and critically evaluate their efficacy. Our analysis revealed the superior properties of microstructure representation is based on the first order-gradient of the atomic fraction.

Data acquired from synchrotron-based X-ray computed tomography provide complete descriptions of the stochastic positions of each fiber in large bundles within composite samples. The data can be accumulated for distances along the nominal fiber direction that are long enough to reveal meandering or misalignment. Data are analyzed for a single fiber bundle consolidated as a mini-composite specimen and a block of fibers embedded within a single ply in a tape laminate specimen. The fibers in these materials differ markedly in their departure from alignment and the patterns formed by fiber deviations. The tape laminate specimen exhibits evidence of fibers that have slipped laterally through the bundle in narrow shear bands, which may be a mechanism of bundle deformation under transverse compression and shear. This pattern is absent in the single-tow specimen, which was not subject to transverse loads in processing. We propose a combination of topological and Euclidean metrics to quantify these and other stochastic bundle characteristics. Topological metrics are based on the neighbor map of fibers, which is constructed on cross-sections of the bundle by Delaunay triangulation (or Voronoi tessellation). Variations of the neighbor map along the fiber direction describe fiber meandering, twist, etc. Euclidean metrics include factors such as local fiber density and fiber orientation. The metrics distinguish bundle types, enable quantification of the effects of the manufacturing history of bundles, and provide target statistics to be matched by virtual specimens that might be generated for use in fiber-scale virtual tests.

High-throughput discovery of process-structure-property relationships in materials through an informatics-enabled empirical approach is an increasingly utilized technique in materials research due to the rapidly expanding availability of data. Here, process-structure-property relationships are extracted for the nucleation, growth, and deposition of semiconducting poly(3-hexylthiophene) (P3HT) nanofibers used in organic field effect transistors, via high-throughput image analysis. This study is performed using an automated image analysis pipeline combining existing open-source software and new algorithms, enabling the rapid evaluation of structural metrics for images of fibrillar materials, including local orientational order, fiber length density, and fiber length distributions. We observe that microfluidic processing leads to fibers that pack with unusually high density, while sonication yields fibers that pack sparsely with low alignment. This is attributed to differences in their crystallization mechanisms. P3HT nanofiber packing during thin film deposition exhibits behavior suggesting that fibers are confined to packing in two-dimensional layers. We find that fiber alignment, a feature correlated with charge carrier mobility, is driven by increasing fiber length, and that shorter fibers tend to segregate to the buried dielectric interface during deposition, creating potentially performance-limiting defects in alignment. Another barrier to perfect alignment is the curvature of P3HT fibers; we propose a mechanistic simulation of fiber growth that reconciles both this curvature and the log-normal distribution of fiber lengths inherent to the fiber populations under consideration.

We present a methodology for characterizing and reconstructing in-plane weave variability in textile composites. Surface topography of a partially processed C-fiber/SiC matrix composite panel was measured using digital image correlation. The centroids of tow segments that appear periodically on the fabric surface were located by image analysis and used as fiducial markers. Stochastic deviations of the fiducial markers from the ideal periodic weave structure indicate geometrical variance. Fourier analysis shows that spatial wavelengths of the deviations range from the size of one unit cell to the dimensions of the entire panel. Long-range deviations are attributed principally to fabric deformation after manufacture, during handling. Short-range fluctuations, extracted by computing spatial derivatives of the positions of the fiducial markers, are attributed to variations in tow packing density that arises during weaving. A simple set of statistics for these deviations is presented and its use in generating stochastic virtual specimens is demonstrated.

The diffusion media (DM) has been shown to be a vital component for performance of polymer electrolyte fuel cells (PEFCs). The DM has a dual-layer structure composed of a macro-substrate referred to as the gas diffusion layer (GDL) coated with a micro-porous layer (MPL). Efficient prediction of the effective transport properties of the DM from its internal structure is essential to optimizing the multifunctional characteristics of this critical component. In this work, a unique data-driven approach to establishing structure–property correlations is introduced and applied to the case of gas diffusion in the GDL and MPL. This new approach provides an automated process to produce unbiased estimators to microstructural variance, in contrast to many process-related (hence biased) parameters employed by prominent correlations in the field. The present approach starts with a rigorous quantification of microstructure in the form of n-point statistics. It is followed by the identification of the key aspects of the internal structure through the use of principle component analysis. A data-driven correlation is established when the principal components are related to effective diffusivity by multivariate linear regression. This data-driven approach is compared to the conventional correlations and shown to achieve a very high accuracy for capturing the diffusive transport in the tested PEFC components.

Localization (as opposed to homogenization) describes the spatial distribution of the response field of interest at the microscale for an imposed loading condition at the macroscale. A novel approach called Materials Knowledge Systems (MKS) has recently been formulated to construct accurate, bidirectional, microstructure–property–processing linkages in hierarchical material systems to facilitate computationally efficient multiscale modeling and simulation. This approach is built on the statistical continuum theories developed by Kroner that express the localization of the response field at the microscale using a series of highly complex convolution integrals, which have historically been evaluated analytically. The MKS approach dramatically improves the accuracy of these expressions by calibrating the convolution kernels in these expressions to results from previously validated physics-based models. All of the prior work in the MKS framework has thus far focused on calibration and validation of the first-order terms in the localization relationships. In this paper, we explore, for the first time, the calibration and validation of the higher-order terms in the localization relationships. In particular, it is demonstrated that the higher-order terms in the localization relationships play an increasingly important role in the spatial distribution of elastic stress or strain fields at the microscale in composite systems with relatively high contrast.

A novel mathematical framework called materials knowledge systems (MKS) was recently formulated to extract, store and recall computationally efficient hierarchical linkages that are at the core of multi-scale modeling of materials phenomena. A salient feature of this new framework is that it facilitates flow of high-fidelity information in both directions between the constituent length scales, and thereby offers a new strategy for concurrent multi-scale modeling. The viability of this new framework has thus far been largely explored for capturing the mechanical response of composite material systems. This paper extends the MKS framework to applications involving microstructure evolution, where the local states are typically defined in a continuous local state space. In particular, it will be shown that it is possible to obtain an efficient discretization of the local state space to produce a sufficiently accurate description of the linearized structure–structure evolution linkages for modeling spinodal decomposition. Furthermore, it will be shown that these linkages can be used successfully to accurately predict the continuous evolution of microstructure over the long time periods involved in such problems.

Synchrotron based energy dispersive X-ray diffraction has been used to profile the strains around fatigue cracks in 4140 steel test specimens. In particular strain field comparisons were made on specimens prepared: with initial constant stress intensity fatigue; with this initial fatigue followed by a single overload cycle; and with this fatigue-overload sequence followed by an additional constant stress intensity fatigue. The strain profiles behind, at and in-front-of the crack tip are discussed in detail. Selected strain profiles measurements under in situ applied tensile stress are also presented. The technique of optical surface height profiling reveals surface depression effects which can be correlated with the interior strain profiles.

This paper presents a generalized framework along with the associated computational strategies for a rigorous quantification of the material structure in a range of different applications using the framework of 2-point spatial correlations. In particular, we focus on applications requiring different assumptions about the periodicity and/or involving irregular domain shapes and potentially extremely large datasets. Important details of the computational algorithms needed to address these challenges are developed and illustrated with example case studies. Algorithms developed and presented in this work are available at http://dx.doi.org/10.5281/zenodo.31329.

cited By 27; International audience; We review the development of virtual tests for high-temperature ceramic matrix composites with textile reinforcement. Success hinges on understanding the relationship between the microstructure of continuous-fiber composites, including its stochastic variability, and the evolution of damage events leading to failure. The virtual tests combine advanced experiments and theories to address physical, mathematical, and engineering aspects of material definition and failure prediction. Key new experiments include surface image correlation methods and synchrotron-based, micrometer-resolution 3D imaging, both executed at temperatures exceeding 1,500°C. Computational methods include new probabilistic algorithms for generating stochastic virtual specimens, as well as a new augmented finite element method that deals efficiently with arbitrary systems of crack initiation, bifurcation, and coalescence in heterogeneous materials. Conceptual advances include the use of topology to characterize stochastic microstructures. We discuss the challenge of predicting the probability of an extreme failure event in a computationally tractable manner while retaining the necessary physical detail. © 2014 by Annual Reviews. All rights reserved.