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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Recent Developments to the Porous Microstructure Analysis (PuMA) Software
The Porous Microstructure Analysis (PuMA) software is a suite of tools for the analysis of porous materials and generation of material microstructures. From microstructural data, often obtained through X-ray microtomography, PuMA can determine a number of effective material properties and perform material response simulations. Version 2.2 includes capabilities for computing volume fractions, porosity, specific surface area, effective thermal and electrical conductivities, and continuum and rarefied diffusive tortuosity. PuMA can also simulate competitive diffusion/reaction processes at the micro-scale, such as surface oxidation. In this poster, recent advancements to the PuMA software are detailed, including the full refactoring of PuMA into v3.0, a new module to compute heat conduction in anisotropic materials, a particle method for simulating molecular beam experiments, a new finite-volume Laplace solver, complex fibrous material generation, woven material generation, and a coupling of PuMA with the DAKOTA software for advanced statistics.
PuMA↗
Porous Microstructure Analysis (PuMA) software
The Porous Microstructure Analysis (PuMA) software was developed to provide a robust and efficient framework for computing material properties based on their microstructures. The development was motivated by advancements in X-ray microtomography, an imaging technology that can resolve the structure of a material at a sub-micron scale, in 3D and even in 4D (over time). PuMA provides the capability of computing a comprehensive spectrum of properties, from the most fundamental geometric features of a microstructure, to advanced anisotropic thermo-elastic properties. In addition, the software can generate artificial microstructures, ranging from simple analytical shapes to complex fibrous woven and non- woven geometries, which can be used in performance optimization studies. This presentation will highlight many of the capabilities of the recent open-source release.
microtomography↗
Recent Developments to the Porous Microstructure Analysis (PuMA) Software
The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.
Tomography↗
Recent Developments to the Porous Microstructure Analysis (PuMA) Software
Introduction The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.
Tomography↗
The Porous Microstructure Analysis (PuMA) software
The open-source Porous Microstructure Analysis (PuMA) software was implemented to offer an efficient framework for determining material characteristics from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enable parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM). SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process.
Tomography↗
The Porous Microstructure Analysis (PuMA) software
The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.
Tomography↗
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)↗
Computer vision in microstructural analysis
The following is a laboratory experiment designed to be performed by advanced-high school and beginning-college students. It is hoped that this experiment will create an interest in and further understanding of materials science. The objective of this experiment is to demonstrate that the microstructure of engineered materials is affected by the processing conditions in manufacture, and that it is possible to characterize the microstructure using image analysis with a computer. The principle of computer vision will first be introduced followed by the description of the system developed at Texas A&M University. This in turn will be followed by the description of the experiment to obtain differences in microstructure and the characterization of the microstructure using computer vision.
Srinivasan, Malur N.↗
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↗
Microstructural analysis of solar cell welds
Parallel-gap resistance welding of silicon solar cells with copper interconnects results in complex microstructural variations that depend on the welding variables. At relatively low heat input solid-state welds are produced. At medium heat the Ag-Cu eutectic forms resulting in a braze joint. High heat produces a fusion weld with complete melting of the silver layer on the silicon solar cell. If the silicon is also melted, cracking occurs in the silicon cell below the weld nugget. These determinations were made using light microscopy, microprobe, and scanning electron microscopy analyses.
Moore, T. J.↗
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↗
High-temperature deformation and microstructural analysis for Si3N4-Sc2O3
It was indicated that Si3N4 doped with Sc2O3 may exhibit high temperature mechanical properties superior to Si3N4 systems with various other oxide sintered additives. High temperature deformation of samples was studied by characterizing the microstructures before and after deformation. It was found that elements of the additive, such as Sc and O, exist in small amounts at very thin grain boundary layers and most of them stay in secondary phases at triple and multiple grain boundary junctions. These secondary phases are devitrified as crystalline Sc2Si2O7. Deformation of the samples was dominated by cavitational processes rather than movements of dislocations. Thus the excellent deformation resistance of the samples at high temperature can be attributed to the very small thickness of the grain boundary layers and the crystalline secondary phase.
Cheong, Deock-Soo↗
High-temperature deformation and microstructural analysis for silicon nitride-scandium(III) oxide
It was indicated that Si3N4 doped with Sc2O3 may exhibit high temperature mechanical properties superior to Si3N4 systems with various other oxide sintered additives. High temperature deformation of samples was studied by characterizing the microstructures before and after deformation. It was found that elements of the additive, such as Sc and O, exist in small amounts at very thin grain boundary layers and most of them stay in secondary phases at tripple and multiple grain boundary junctions. These secondary phases are devitrified as crystalline Sc2Si2O7. Deformation of the samples was dominated by cavitational processes rather than movements of dislocations. Thus the excellent deformation resistance of the samples at high temperature can be attributed to the very small thickness of the grain boundary layers and the crystalline secondary phase.
Cheong, Deock-Soo↗
Microstructural analysis of the 2195 aluminum-lithium alloy welds
The principal objective of this research was to explain a tendency of 2195 Al-Li alloy to crack at elevated temperature during welding. Therefore, a study was made on the effect of welding and thermal treatment on the microstructure of Al-Li Alloy 2195. The critical roles of precipitates, boundaries, phases, and other features of the microstructure were inferred from the crack propagation paths and the morphology of fracture surface of the alloy with different microstructures. Particular emphasis was placed on the microstructures generated by the welding process and the mechanisms of crack propagation in such structures. Variation of the welding parameters and thermal treatments were used to alter the micro/macro structures, and they were characterized by optical and scanning electron microscopy. A theoretical model is proposed to explain changes in the microstructure of welded material. This model proposes a chemical reaction in which gases from the air (i.e., nitrogen) release hydrogen inside the alloy. Such a reaction could generate large internal stresses capable to induce porosity and crack-like delamination in the material.
Talia, George E.↗
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↗