MATBOX: An Open-source Microstructure Analysis Toolbox for microstructure generation, segmentation, characterization, visualization, correlation, and meshing
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97 MATHEMATICS AND COMPUTING↗
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MATBOX, an Open-Source Microstructure Analysis Toolbox for Meshing, Generation, Segmentation, and Characterization of 3D Heterogenous Volumes
Battery performance is strongly correlated with electrode microstructural properties. To account for its impact, lithium-ion battery (LIB) models either abstract the microstructural heterogeneity of composite electrodes using effective macroscopic properties (macro- or meso- scale models) or directly solve the system of equations on the microstructure geometry or mesh (microstructure-scale models). Therefore, to be adequate, both families of models require information from the microstructure geometry, which can be provided by the numerical tool presented in this work. MATBOX is a MATLAB open-source application [1] developed by NREL for performing various microstructure-related tasks including microstructure numerical generation, image filtering and microstructure segmentation, microstructure characterization and correlation, visualization, and microstructure meshing. MATBOX was originally developed for the analysis of LIB electrode microstructures; however, the algorithms provided by the toolbox are widely applicable to other heterogeneous materials. The toolbox provides a user-friendly experience thanks to a Graphical-User Interface, requires no coding by the user, and is well documented. This presentation will illustrate various MATBOX features for the characterization of a LIB electrode, including a fully automated Representative Volume Element (RVE) analysis, the numerical generation of complex 'virtual' microstructure, including dual-layer electrodes and carbon-binder additive phase, and the meshing of a complex NMC/graphite full cell microstructure suitable for 3D finite-element modeling. Other modules (segmentation, visualization, and correlation) will be briefly presented. Thanks to its modular, open-source approach, MATBOX can easily incorporate third-party algorithms to eventually build a standard in the field that will benefit the whole scientific community. Effective diffusion coefficient [2], additive phase numerical generation [3], and meshing [4] third-party algorithms have been already integrated in the toolbox with more to come.
DIRECT ENERGY CONVERSION,MATHEMATICS AND COMPUTING↗
A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures
Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.
Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
MATBOX (Microstructure Analysis Toolbox) [SWR-20-76]
MATBOX is a MATLAB application for performing various microstructure-related tasks including microstructure numerical generation, image filtering and microstructure segmentation, microstructure characterization, result three-dimensional visualization and result correlation, and microstructure meshing. MATBOX was originally developed to analyze electrode microstructures for lithium ion batteries; however, the algorithms provided by the toolbox are widely applicable to other heterogeneous materials. The toolbox provides a user-friendly experience thanks to a Graphic-User Interface.
Usseglio Viretta, Francois↗
Microstructure analysis and machinability of additively manufactured A205 aluminum with heat treatments
A205 aluminum is one of the few high-strength aluminum alloys discovered for additive manufacturing (AM). The microstructure and heat treatment effects of AM A205 vary from those of its cast form, indicating possible differences in machinability during post-processing. Here, the objective of this research is to determine these variations of AM A205 and their effects on machinability using the cast condition as the baseline. As built and two heat-treated conditions (solution treatment and age hardening) were applied and compared in terms of their microstructures, micro-hardness, and machinability including vibration, specific cutting energy, chip morphology, and surface finish. The effects of print orientations on machinability were tested as well and found to be insignificant. AM as built had a higher hardness and slightly better machinability than its cast as built cast counterpart due to a smaller grain size (1 µm vs. 15 µm), eutectic θ-Al 2 Cu lattice and a fine, homogeneous TiB 2 distribution. The solution treatment dissolved this θ-Al 2 Cu lattice and increased the grain size (3 µm) which decreased the hardness, increased the ductility seen in the chips, and reduced the overall machinability. The presence of θ’-Al 2 Cu precipitates improved the machinability of aged AM A205. In addition, it was found that A205 exhibited both ductile and brittle behavior depending on the cutting speed, thus producing different machinability. The speed dependency is related to the material condition and heat treatment.
36 MATERIALS SCIENCE↗
Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy
Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.
36 MATERIALS SCIENCE↗
Microstructure analysis of laser beam weldments performed on neutron-irradiated 304L steel containing 3 and 8 appm helium
We report that AISI 304 L austenitic stainless steel is one of the major structural materials used in light water reactors (LWRs). In the future, 304 L components may require repair, and several welding techniques have been proposed as candidates. In this study, laser beam welding using the low-energy contribution approach was performed in ~2016 on neutron-irradiated AISI 304 L steel with nominal values of 3 and 8 appm helium (He). The goal was to investigate the impact of helium on the irradiated material weldability and evaluate helium-associated damage. The weld and heat-affected zone (HAZ) microstructure was studied using scanning electron microscopy, electron backscatter diffraction, and transmission electron microscopy (TEM). Analysis of the weldment cross-sections did not reveal severe cracking. Still, evidence of incipient helium-associated damage was observed in the HAZ, manifesting as degraded grain boundaries (GBs) “decorated” by pore chains and mostly small (i.e., ≤ 25-30 µm) scattered cracks. The largest observed crack was ~50 µm in length. Helium-associated damage was localized within ~200-300 µm of the weld pool boundary, and the fraction of the compromised GBs, as a rule, was below ~9-11% of the total GB network. TEM analysis showed significant annealing of the radiation-induced defects at distances up to ~300 µm from the weld pool boundary. Inside the HAZ, the cavities (likely, helium bubbles) tended to form at GBs and inclusions, such as manganese sulfide precipitates in the grain interior. Crystallography analysis of the HAZ damage showed that random high-angle boundaries were most susceptible to helium-associated damage. In contrast, low-angle random boundaries and twin boundaries appeared to be strongly resistant to degradation from the combined effects of helium and welding.
36 MATERIALS SCIENCE↗
A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network
Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.
36 MATERIALS SCIENCE↗
Irradiation creep measurement and microstructural analysis of chromium nitride–coated zirconium alloy using pressurized tubes
Environmental barrier coatings for Zr-based materials are currently under development to reduce oxidation and embrittlement in light-water reactors. Chromium nitride is one such candidate for this application, particularly as accident-tolerant fuel cladding. However, quantifying the impact of coatings on the irradiation-induced creep of zircaloy (Zry) is critical as this mechanism often exceeds thermal creep rates under light-water reactor operating conditions and can be a limiting design characteristic. Additionally, examining irradiation effects in the microstructure at the coating interface is key to understanding the compatibility of the material system. Here, to accelerate the experimental measurement of irradiation creep and microstructure evolution in CrN-Zry, compact, pressurized creep tubes were fabricated and irradiated in the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. Miniature, thin-walled rodlets fabricated from annealed Zr-Sn barstock were coated with CrN using physical vapor deposition (PVD) to nominal thicknesses of 4 and 8 μm. Coated and uncoated rodlet specimens were internally pressurized and welded, generating nominal circumferential hoop stresses of 0, 90, or 180 MPa under 300°C irradiation conditions. Twelve specimens were measured diametrically prior to irradiation using a low-cost, automated, contactless laser profilometer developed for this work. Specimens were irradiated in sealed capsules for one 25-day HFIR cycle, accumulating approximately 1.8 $\times$ 10 21 n/cm 2 fast fluence (E n > 10 MeV). The irradiated samples were retrieved and remeasured using the same profilometry system in a shielded hot cell facility. Irradiation creep between specimens was compared using standard statistical tests and showed that both thicknesses of CrN coating had a negligible effect on the irradiation creep strain of the Zry material. Microstructure characterization of pre- and post-irradiated CrN-Zry specimens showed minimal changes due to irradiation but did show a substantial O-rich region at the Zry-CrN interface.
36 MATERIALS SCIENCE↗
Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718
Additive manufacturing (AM) is revolutionizing almost all industries through the production of intricate geometries previously prohibited by cost or machinability. Ni-based superalloys form a primary alloy class for high temperature applications in the petrochemical, aerospace, and nuclear industries because of their intrinsic resistance to creep and the ability to heat treat the superalloy for high strength. Despite these attractive properties, the extreme work hardening of Ni-based superalloys makes traditional manufacturing of complex shapes difficult and thus, these alloys are an attractive target for AM. Superalloy 718 was chosen as an example superalloy because of the wide variety of precipitates that can form within its composition space from the repetitive heating and cooling cycles of the AM process. The precipitates and other microstructure features, such as grain boundaries and dislocations, will dictate the mechanical properties and thus, there is an extensive challenge to characterize the size, number density, composition, and volume fraction of each microstructural feature from AM fabrication using analytical electron microscopy. This work focused on the application of a pixel-wise classification machine learning (ML) model called a dynamic segmentation convolutional neural network (DSCNN) to identify the microstructural features of an as-fabricated additively manufactured superalloy 718.
Taller, Stephen↗
Hybrid manufacturing by additive friction stir deposition, metrology, CNC machining, and microstructure analysis
Aerospace flight panels must provide high strength with low mass. For aluminum panels, it is common practice to begin with a wrought plate and remove the majority of the material to attain the desired structure, comprising a thinner plate with the desired pattern of reinforcement ribs. As an alternative, this study implements hybrid manufacturing, where aluminum is first deposited on a baseplate only at the rib locations using additive friction stir deposition (AFSD). Structured light scanning is then used to measure the printed geometry. This geometry is finally used as the stock model for computer numerical control (CNC) machining. This paper details the hybrid manufacturing process that consists of: AFSD to print the preform, structured light scanning to generate the stock model and tool path, three-axis CNC machining, and post-process measurements for part geometry and microstructure.
42 ENGINEERING↗
Reactivity in cement pastes bearing fine fraction concrete and glass from construction and demolition waste: Microstructural analysis of viability
Converting construction and demolition waste (CDW) into secondary raw materials is one of the priorities of environmental policy and circular economy strategy. This study analysed the variation in eco-efficient paste reactivity with OPC replacement ratio (5% to 10%) and hydration time (2 d, 28 d and 90 d). Three types of CDW were explored: two (calcareous and siliceous) consisting of fine (4 AH{sub 13} and C{sub 4}AcH{sub 11}, ettringite and portlandite, were the same as observed in OPC hydration. These findings attest to the scientific viability recycling such CDWs as mineral additions in eco-cement manufacture and consequently eliminating the need for stockpiling these materials at recycling/storage plants.
36 MATERIALS SCIENCE↗
Stochastic microstructural analysis of ceramic matrix composites using a high-fidelity multiscale framework
This paper presents a high-fidelity multiscale framework to simulate the mechanical behavior of ceramic matrix composites (CMCs), accounting for the complex micromorphology captured using detailed material characterization. First, high-resolution micrographs are obtained for the specific carbon fiber silicon-carbide-nitride matrix (C/SiNC) CMC to characterize the variability of the architectural features and manufacturing-induced defects at the microscale. An image processing algorithm is then used to precisely estimate the size and distribution of all subscale features and defects from the micrographs. The information is then used to generate a three-dimensional stochastic representative volume element (SRVE) to reconstruct microscale constituents accounting for the variability. As a result, the generated SRVEs are simulated using the high-fidelity generalized method of cells (HFGMC) micromechanics theory to investigate the effects of defects on the elastic properties of C/SiNC CMCs.
Ceramic Matrix Composites↗
Effect of moderate temperatures on compressive strength of ultra-high-performance concrete: A microstructural analysis
Highlights: • All coarse aggregates and steel fibres were surrounded by the binder. • X-ray diffraction is useful for explaining the increase in compressive strength with increasing temperature. • Rosenhahnite and/or quartz Dauphiné-twinned phases improved the compressive strength. • Polypropylene fibres prevented spalling and preserved the compressive strength. • Concrete with steel and polypropylene fibres did not exhibit spalling at 300 °C. Concrete with two types of steel fibres and a polypropylene fibre prevented spalling and preserved the compressive strength at 300 °C, which makes these concretes suitable for long-term applications up to 300 °C, such as for steam collectors or thermal energy storage systems. The compressive strength behaviour of three types of ultra-high-performance fibre-reinforced concrete manufactured with the same matrix was investigated. For this purpose, a complete characterisation of all the raw materials and the three types of fibres used was performed. The morphology of all concrete mixtures at room temperature was analysed using scanning electron microscopy–energy-dispersive X-ray spectroscopy. From the results, it was ascertained that the steel fibres and coarse siliceous aggregates were not in contact (being separated by ≥3.41 μm) and were surrounded by the binder (of ≥1 μm in thickness) for all the mixtures studied. Rosenhahnite and/or quartz Dauphiné-twinned phases improved the compressive strength (as determined by X-ray diffraction).
36 MATERIALS SCIENCE↗
Microstructural Analysis of Cadmium Whiskers on Long-Term-Used Hardware
A survey of cadmium plated field return hardware showed ubiquitous cadmium whisker growth. The most worn and debris-covered hardware showed the densest whisker growth. Whiskers were often found growing in agglomerates of nodules and whiskers. The hardware was rinsed with alcohol to transfer whiskers and debris from the hardware to a flat stub. Fifty whiskers were studied individually by scanning electron microscopy (SEM), including energy dispersive spectroscopy (EDS) and electron backscatter diffraction (EBSD). Most of the whiskers were single crystal, though three were found to contain grain boundaries at kinks. The whiskers ranged from 5 to 600 μm in length and 80 pct showed a $<\bar{1} 2 \bar{1} 0>$ type growth direction. This growth direction facilitates the development of low energy side faces of the whisker, (0001) and {1010}.
36 MATERIALS SCIENCE↗
Microstructural analysis of fine-grained magnesium after cyclic tension–tension fatigue testing at 0 °C
Not Available
36 MATERIALS SCIENCE↗
Microstructural analysis of tristructural isotropic particles in high-temperature steam mixed gas atmospheres
High-temperature gas-cooled reactors (HTGRs) use tristructural isotropic (TRISO) particles embedded in a graphitic matrix material to form the integral fuel element. Potential off-normal reactor conditions for HTGRs include steam ingress with temperatures above 1,000 °C. Fuel element exposure to steam can cause the graphitic matrix material to evolve, forming an atmosphere composed of oxidants and oxidation products and potentially exposing the TRISO particles to these conditions. Investigating the oxidation response of TRISO particles exposed to a mixed gas atmosphere will provide insight into the stability under off-normal conditions. In this study, surrogate TRISO particles were exposed to high temperatures (T = 1,200 °C) in flowing steam (3% < pH 2 O < 21%) and CO (pCO < 1%) to determine the oxidation behavior of the SiC layer when exposed to various mixed gas atmospheres. Scanning electron microscopy, x-ray diffraction, and focused ion beam milling was used to determine the impact of CO and steam on the oxidation behavior of the SiC layer. Therefore, the data presented demonstrates how the SiC layer showed strong oxidation resistance due to limited SiO 2 growth and maintained its structural integrity under these off-normal conditions.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Microstructural analysis of novel Gd2Ti2O7 thin films processed via sputter deposition
Not Available
36 MATERIALS SCIENCE↗