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Direct extraction of spatial correlation functions from limited x-ray tomography data for microstructural quantification
Abstract not provided
Improving Microstructural Quantification in 3D FIB-SEM Tomography
A detailed understanding of 3D microstructural features is crucial during postprocessing of the acquired data, while determining microstructural properties. Focused ion beam and scanning electron microscopy (fib-sem) is an ideal technique for the acquisition and quantification of 3D microstructures in subsurface features. In this work, we evaluated and implemented approaches to quantify 3D morphological features of plutonium oxalate and oxide particles. Image analysis, as well as statistical analysis were utilized to study the effect of process variables on the pore morphology of plutonium oxalate and oxide particles. The primary results were the creation of morphological maps of particles and identify indicators that correlated parameters allowing for discrimination between direct and reverse strike processes. This enabled us to properly quantify and map particles with their images obtained via sem. For this project, the highest correlation was found to be between Volume and Sphericity (R 2 = 0.7432) for the Revere Strike process, and Volume % and Sphericity (R 2 = 0.7450) for the Direct Strike process.
Utilizing local orientation image analysis for microstructure quantification in additive manufacturing
Not provided.
Microstructure Quantification of Wire Additive Manufacturing of Titanium Alloys using Polarized Light Microscopy
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Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification
Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.
Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures
Highlights: • Microstructure quantification based on directional chord length distribution and principal component analysis is presented • A boundary-based algorithm of calculations of chord length distributions is introduced • Utility of the quantification framework is shown in three case studies on synthetic, experimental, and simulation datasets • Angularly resolved chord length distribution captures essential details of grain size and shape in diverse microstructures • Low-dimensional representations of directional chord length distributions are obtained using principal component analysis - Abstract: Quantification of mesoscale microstructures of polycrystalline materials is important for a range of practical tasks of materials design and development. The current protocols of quantifying grain size and morphology often rely on microstructure metrics (e.g., mean grain diameter) that overlook important details of the mesostructure. In this work, we present a quantification framework based on directionally resolved chord length distribution and principal component analysis as a means of extracting additional information from 2-D microstructural maps. Towards this end, we first present in detail a method for calculating chord length distribution based on boundary segments available in modern digital datasets (e.g., from microscopy post-processing) and their low-rank representations by principal component analysis. The utility of the proposed framework for capturing grain size, morphology, and their anisotropy for efficient visualization, representation, and specification of polycrystalline microstructures is then demonstrated in case studies on datasets from synthetic generation, experiments (on Ni-base superalloys), and simulations (on steel during recrystallization).
Quantifying Microstructure Variability in Laser Powder Bed Fusion 316 L Stainless Steel Microstructures with Spatial Statistics
Here, we have explored data-driven methods for material microstructure quantification that improve sensitivity to microstructural changes compared to traditional approaches. The methods integrate multiple microstructural properties, including grain morphology, crystallographic orientation, and material phase information. The simpler method employs maps of the Euclidean distance transformation metric to evaluate the morphology of grain boundary networks. The more intensive approach employs generalized spherical harmonic mapping for crystallographic orientations, per-pixel phase information, and a variational auto-encoder for dimensionality reduction and results in a multidimensional clustering of by microstructure similarity. Applied to an experimental dataset of additively manufactured steel, both methods detected slight variations in samples produced under nominally identical processing conditions. Both methods were able to distinguish between samples from multiple (nominally identical) builds, while the generalized spherical harmonics-based method could additionally cluster data samples rotated at two orientations on the build plate. The improved sensitivity of the methods, demonstrated through comparison with traditional microstructure characterization techniques, offers advantages for microstructure quantification and comparisons in advanced manufacturing applications.
Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification
Here, we present a data-driven nonlinear uncertainty quantification and propagation framework to study the microstructure-induced stochastic performance of unidirectional (UD) carbon fiber reinforced polymer (CFRP) composites. The proposed approach integrates (1) microscopic image characterization, (2) stochastic microstructure reconstruction, and (3) efficient multiscale finite element simulations enabled by self-consistent clustering (SCA) analysis. To model the complex microstructural variability, the proposed UQ methods take the non-Gaussian uncertainty sources into account through a distribution-free sampling approach leveraging nonparametric and asymptotic statistical tools. A hierarchical conditional sampling strategy enables the simultaneous sampling of multiple sources of uncertainties. Our approach provides insights into the impact of microstructural variabilities, which are shown to have an increasing impact on the nonlinear responses of UD CFRP parts under progressive compression loading and ultimately on the failure rate over time. We discover that before CFRP parts start to fail, a characteristic time period emerges with distinctive uncertainty distributions specific to the microstructure variability and the probability of failure. Identifying the failure time period is crucial to the reliability prediction, which is an essential component of CFRP design.
Meso-scale microstructural agglomerate quantification in boron carbide using X-ray microcomputed tomography
Highlights: • First known quantification of agglomerate defect distributions comprising of carbonaceous and aluminum nitride phases B{sub 4}C. • In-depth statistical analyses of the size, orientation, and morphology of the defects. • The determination of directional dependence of the hot-pressing direction on the agglomerate distributions. - Abstract: Failure in brittle materials is governed, in part, by the specific microstructure of that material, with heterogeneous microstructures generally leading to reduced mechanical properties and performance. In ceramics used for armor, an increase in compressive strength can be an indicator of improved ballistic performance. In hot-pressed boron carbide (B{sub 4}C) ceramic plates, failure has been attributed to carbonaceous flakes observed on the fracture surfaces of failed specimens. However, this current work shows agglomerates, in addition to the carbonaceous flakes, are present in this boron carbide. The density and distribution of these agglomerates are believed to influence the ballistic performance. Therefore, it is of interest to improve the quantification of meso-scale microstructural agglomerates so that strategies can be developed to mitigate them. This work is focused on the microstructural characterization of dumbbell-shaped compression specimens that were machined from a single tile of pressure-aided densification (PAD) B{sub 4}C, using high-resolution micro-computed tomography (microCT). Large clusters of non-B{sub 4}C agglomerates, up to 125 μm in diameter, were observed in addition to the typical smaller carbonaceous flakes and aluminum-based phases. Agglomerate size distribution, orientation, morphology, and spacing were quantified both parallel and perpendicular to the pressing direction. This defect quantification provides valuable information for a better understanding of the material microstructure which is a critical step in improving armor materials for the future.
Uncertainty Quantification of Microstructural Material Variability Effects
This project has developed models of variability of performance to enable robust design and certification. Material variability originating from microstructure has significant effects on component behavior and creates uncertainty in material response. The outcomes of this project are uncertainty quantification (UQ) enabled analysis of material variability effects on performance and methods to evaluate the consequences of microstructural variability on material response in general. Material variability originating from heterogeneous microstructural features, such as grain and pore morphologies, has significant effects on component behavior and creates uncertainty around performance. Current engineering material models typically do not incorporate microstructural variability explicitly, rather functional forms are chosen based on intuition and parameters are selected to reflect mean behavior. Conversely, mesoscale models that capture the microstructural physics, and inherent variability, are impractical to utilize at the engineering scale. Therefore, current efforts ignore physical characteristics of systems that may be the predominant factors for quantifying system reliability. To address this gap we have developed explicit connections between models of microstructural variability and component/system performance. Our focus on variability of mechanical response due to grain and pore distributions enabled us to fully probe these influences on performance and develop a methodology to propagate input variability to output performance. This project is at the forefront of data-science and material modeling. We adapted and innovated from progressive techniques in machine learning and uncertainty quantification to develop a new, physically-based methodology to address the core issues of the Engineering Materials Reliability (EMR) research challenge in modeling constitutive response of materials with significant inherent variability and length-scales.
Spatial Quantification of Microstructural Degradation during Fast Charge in 18650 Lithium-Ion Batter
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Microstructure Variability Quantifications and Predictions in Metal Additive Manufacturing
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TEM Approaches for Microstructure-Informed Prediction of Mechanical Properties in Structural Alloys
Predicting the mechanical performance of structural alloys from their evolving microstructure remains a major challenge in materials science, particularly for nuclear structural materials, where irradiation-induced defects span multiple types and length scales and interact through complex mechanisms. The dispersed barrier hardening (DBH) [1] and Friedel–Kroupa–Hirsch (FKH) [2,3] models have been widely used to evaluate the hardening contributions of individual obstacles and to estimate tensile strength from quantified microstructures; however, when multiple size-dependent obstacles coexist and evolve, predicting temperature-dependent tensile strength becomes significantly more complex, and a fully consistent hardening model is still lacking. Transmission electron microscopy (TEM) plays a central role in refining hardening models and enabling predictive assessments of tensile strength evolution by providing quantitative characterization of dislocations, irradiation-induced defects (e.g., dislocation loops and cavities), precipitates, and grain structure (Fig. 1.). These experimentally measured defect densities are incorporated into physically based hardening models with size- and shape- dependent obstacle strengths [4], using root-sum-square superposition for obstacles of comparable strength and linear superposition for dissimilar ones [5]. In addition, recent advances in TEM [6-8], including high-resolution imaging, 4D-STEM strain mapping, EDS/EELS elemental analysis, and flash-polishing-based TEM specimen preparation and extraction-replica methods (Fig. 2), further improve the accuracy of microstructural quantification. By comparison with prior studies as well as our own results, we show that when TEM-derived microstructural information is carefully integrated with physically grounded hardening models, yield strength (or irradiation-induced hardening) measured at room temperature can be predicted with good quantitative agreement across multiple alloy classes. In-situ TEM combined with high-temperature mechanical testing represents an important next step for refining hardening models by directly probing dislocation–obstacle interactions across varying irradiation doses and temperatures [9]. Because the barrier strength factor (α) depends on both temperature and obstacle size, it should not be treated as a constant fitting parameter; rather, it must be explicitly evaluated to achieve physically meaningful predictions of mechanical behaviour at operating temperatures. This presentation therefore discusses why all strengthening contributions (e.g., Peierls stress, solid-solution strengthening, voids, bubbles, dislocation loops, dislocation lines, and grain boundaries) must be considered collectively, why appropriate superposition methods are essential when obstacles possess different barrier strength factors, how hardness measurements can be meaningfully related to tensile properties, and how TEM-derived microstructural information can be systematically incorporated into hardening models. More broadly, it outlines a pathway toward microstructure-informed prediction of mechanical properties and supports the goal of establishing science-based tools for evaluating structural materials in extreme environments [10].
An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.
Implementation of disruptive designs for gas turbine components using direct energy deposition additive manufacturing
This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.
Controlling Cu–Sn mixing so as to enable higher critical current densities in RRP ® Nb 3 Sn wires
Dipole magnets for the proposed Future Circular Collider (FCC) demand specifications significantly beyond the limits of all existing Nb 3 Sn wires, in particular a critical current density (J c ) of more than 1500 A mm -2 at 16 T and 4.2 K with an effective filament diameter (D eff ) of less than 20 μm. The restacked-rod-process (RRP ® ) is the technology closest to meeting these demands, with a J c (16 T) of up to 1400 A mm -2 , residual resistivity ratio > 100, for a sub-element size D s of 58 μm (which in RRP ® wires is essentially the same as D eff ). An important present limitation of RRP ® is that reducing the sub-element size degrades J c to as low as 900 A mm -2 at 16 T for D s = 35 μm. To gain an understanding of the sources of this J c degradation, we have made a detailed study of the phase evolution during the Cu–Sn 'mixing' stages of the wire heat treatment that occur prior to Nb 3 Sn formation. Using extensive microstructural quantification, we have identified in this paper the critical role that the Sn–Nb–Cu ternary phase (Nausite) can play. The Nausite forms as a well-defined ring between the Sn source and the Cu/Nb filament pack, and acts as an osmotic membrane in the 300 °C–400 °C range—greatly inhibiting Sn diffusion into the Cu/Nb filament pack while supporting a strong Cu counter-diffusion from the filament pack into the Sn core. This converts the Sn core into a mixture of the low melting point (408 °C) η phase (Cu 6 Sn 5 ) and the more desirable ε phase (Cu 3 Sn), which decomposes at 676 °C. After the mixing stages, when heated above 408 °C towards the Nb 3 Sn reaction, any residual η liquefies to form additional irregular Nausite on the inside of the membrane. All Nausite decomposes into NbSn 2 on further heating, and ultimately transforms into coarse-grain (and often disconnected) Nb 3 Sn which has little contribution to current transport. Understanding this critical Nausite reaction pathway has allowed us to simplify the mixing heat treatment to only one stage at 350 °C for 400 h which minimizes Nausite formation while encouraging the formation of the higher melting point ε phase through better Cu–Sn mixing. Finally, at a D s of 41 μm, the Nausite control heat treatment increases the J c at 16 T by 36%, reaching 1300 A mm -2 (i.e. 2980 A mm -2 at 12 T), and moving RRP ® closer to the FCC targets.
Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging
In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.