Quantitative Microstructural Analysis Research Articles

Challenges and possibilities of the manual metallographic serial sectioning process using the example of a quantitative microstructural analysis of graphite in cast iron

Abstract The 3D microstructure analysis presented in this study focuses on the nodular graphite of an EN-GJS grade cast iron. The shape is analyzed based on shape factor and aspect ratio as well as average particle size and distribution. The shape of graphites in cast iron materials is critical to the mechanical properties of these alloys and is essential for the characterization of the material. Metallographic serial sectioning creates a digital twin of the material, which is removed layer by layer and visualized layer by layer under an optical microscope. From this three-dimensional twin, any number of planes can be digitally projected and analyzed. Conventional quantitative 3D analysis examines the voxel count and composition of a body in 3D space. The analysis presented here applies stereological 2D analysis methods to three spatial planes of the material. Two of the planes are digital projections of the twin. The two reconstructed planes of the material are chosen so that the direction vectors of all three planes form an angle of 90° to each other. The methodology is described in detail and the challenges and opportunities of the serial section method presented here are discussed.

Grain orientation and shape evolution of ferroelectric ceramic thick films simulated by phase-field method

The orientation and shape of ceramics grains was always neglected, resulting in a lot of information during sintering has not been excavated. In this study, a modified phase-field model in order to express the anisotropy of grain boundary energy is developed. The effects of the anisotropy of grain boundary energy on the grain orientation and shape evolution are investigated in detail. The ferroelectric ceramic thick films are prepared by tape casting. The comparison of experiment and simulation results shows that the anisotropy of grain boundary energy results in uneven grain orientation and bimodal grain size distribution. The quantitative analysis of grain microstructures helps to establish a relationship with the degree of anisotropy of grain boundary energy. Our findings provide a new way to judge the degree of anisotropy by calculating the relevant parameters in the SEM images of ceramics materials.

Geometric deep learning for enhanced quantitative analysis of microstructures in X-ray computed tomography data

Quantitative microstructural analysis of XCT 3D images is key for quality assurance of materials and components. In this paper we implement a Graph Convolutional Neural Network (GCNN) architecture to segment a complex Al-Si Metal Matrix composite XCT volume (3D image). We train the model on a synthetic dataset and we assess its performance on both synthetic and experimental, manually-labeled, datasets. Our simple GCNN shows a comparable performance, measured via the Dice score, to more standard machine learning methods, but uses a greatly reduced number of parameters (less than 1/10 of parameters), features low training time, and needs little hardware resources. Our GCNN thus achieves a cost-effective reliable segmentation.

A framework for general-purpose microscopic image analysis via self-supervised learning

Combining materials science, artificial intelligence (AI) offers great potential for the extensive quantitative analysis and processing of material characterization associated with high-throughput experiments. However, due to the complex and diverse morphology of structural components, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications. Here, we present a universal self-supervised learning framework for microscopic images. Our framework learns generalizable representations from unlabelled images and provides a pixel-wise segmentation for quantitative microstructure analysis in a variety of materials science applications. Specifically, the framework learns feature from a single image by means of self-supervised learning, and adapts it to a series of related tasks. We show that our method consistently outperforms several comparisons supervised or weakly supervised learning models in the context of various applications. Our approach provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable practical AI applications from microscopic imaging.

Combining traditional and quantitative multiscale structural analysis to reconstruct the tectono-metamorphic evolution of migmatitic basements: the case of the Valpelline Series, Dent-Blanche Tectonic System, Western Alps

Due to the ongoing development of new technologies, many instruments are available to assist geological investigations at different scales. These techniques, including 3D outcrop modelling from aerial photogrammetry and quantitative microstructural analysis are useful in crystalline basements studies. This contribution combines traditional and quantitative multiscale structural analysis techniques to the migmatitic rocks of the Valpelline Series (Dent-Blanche Tectonic System, Western Alps). Conventional structural analysis is integrated with the extraction of structural data from 3D models of representative smooth outcrops. Quantitative microstructural and mineral-chemical analyses are combined to link structural and metamorphic evolution. This approach allows identifying and correlating foliations that developed during three tectono-metamorphic stages. The first (D1) includes solid-state deformation associated with an early foliation (S1) preserved within metabasite boudins enclosed in migmatite gneiss. The second (D2) is related to the dominant foliation in migmatite gneiss (S2), coeval with the regional scale anatexis and growth of garnet and cordierite. The third (D3) is related to the late folding of S2 and the development of a sillimanite-rich axial plane foliation (S3) which wraps around garnet and cordierite. Finally, this work discusses pros and cons of each innovative methodology, still emphasising the importance of using manually acquired field data as ground control.

Impact of local plastic deformation on thermo-magnetic instability of high-J c Nb3Sn strand

The future fusion reactor devices built by Institute of Plasma Physics, Chinese Academy of Science will be a compact burning plasma facility to fill the gap between ITER and the China Fusion Engineering Test Reactor, with the mission of studying the deuterium–tritium plasma in steady-state operation. The winding-package (WP) of its toroidal field coil is graded into high-field WP and low-field WP, and the high critical current density (J c) Nb3Sn strand will be applied to the high-field WP based on the current design. The thermo-magnetic instability is the main issue in the application of high-J c Nb3Sn strand, which may induce premature quenching in the low field regions. This issue can be improved by reducing the effective filament diameter (d eff) to suppress flux jumps and increasing the residual resistivity ratio (RRR) to enhance the release of heat generated by flux jumps. However, in the conductor manufacturing process, local plastic deformations (indentation) of strands can impact the sub-element layout and degrade the RRR of the strand, which would increase again the risk on instability. In this study, magnetization measurements and V–I tests were performed on samples with different indentation depths. The hysteresis loss and d eff of the indented sample was obtained from the test results. The impact of local plastic deformation on the thermo-magnetic instability of two types of Nb3Sn strand was confirmed by cross-sectional metallographic observation and quantitative microstructure analysis. It was shown that flux jumps were suppressed at indentation depths below 0.3 mm. Further indentation increase leads to severe flux jumps.

Highly efficient, coke-free electrolysis of dry CO2 in solid oxide electrolysis cells

Dry CO2 electrolysis in solid oxide electrolysis cells (SOECs), a highly efficient, versatile method for converting CO2 into value-added products, is critically limited by carbon deposition on Ni-based fuel electrodes. We achieved efficient, coke-free CO2 electrolysis using SOECs by elaborately controlling the local gas environments. Multilayered electrode microstructures were systematically engineered to facilitate mass transport and maintain the CO partial pressure below the threshold for solid carbon formation. A fuel-electrode-supported cell with an improved electrode microstructure operated stably for 500 h without coking at 0.50 A cm−2 and 700 °C, whereas conventional ones failed immediately. Multiphysics modeling coupled with three-dimensional quantitative microstructural analysis confirmed our improved electrode successfully mitigated carbon deposition. Furthermore, the enhanced electrode substantially lowered the overpotential and increased the CO production rate by > 50 %. These results highlight the feasibility of coke-free dry CO2 electrolysis in SOECs using commercially viable materials by controlling the electrode transport properties.

Investigating the impact of exopolysaccharides on yogurt network mechanics and syneresis through quantitative microstructural analysis

Exopolysaccharides produced by lactic acid bacteria are widely used to improve the sensory properties of yogurt. The relation between the physical properties of the microbial exopolysaccharides and the structural and rheological properties of the yogurt are incompletely understood to date. To address this knowledge gap, we studied how two distinct exopolysaccharides influence the microstructure, rheological properties, and syneresis of yogurt. The effect of a negatively charged, capsular exopolysaccharide produced by Streptococcus thermophilus and a neutral, non-capsular exopolysaccharide produced by Lactococcus lactis were investigated. Using quantitative microstructural analysis, we examined yogurt samples prepared with either the capsular or the non-capsular exopolysaccharide, and with mixtures of the two. Confocal laser scanning microscopy and stimulated emission depletion microscopy were employed to visualize the microstructures, revealing differences in pore size distribution, protein domain size, and casein interconnectivity that were not apparent through visual inspection alone. Additionally, variations in rheological properties were observed among the different yogurt types. In the yogurt fermented with both bacterial strains, we observed a combined impact of the two exopolysaccharide types on relevant microstructural and rheological properties. The negatively charged capsular exopolysaccharide enhanced casein interconnectivity and gel stiffness, while the neutral non-capsular exopolysaccharide led to thicker protein domains, an abundance of small pores, and a lower loss tangent. These factors collectively hindered syneresis, resulting in improved structural integrity. Our study not only provides valuable insights into the influence of different exopolysaccharides on yogurt properties, but also presents the first demonstration and quantification of the effect of multiple types of exopolysaccharides on casein interconnectivity. These findings offer guidance for the production of yogurts with customized microstructure, rheological properties, and resistance to syneresis.

Quantitative analysis of the correlation between gel strength and microstructure of shrimp surimi gel induced by dense phase carbon dioxide

The impact of treatment pressure, temperature and time of DPCD on the Pacific White Shrimp (Litopenaeus vannamei) surimi gel properties was studied and compared with the conventional heat treatment. The gel strength, crosslinking degree, and microstructure of shrimp surimi gels were investigated. Quantitative microstructural characteristics were investigated to elucidate the changes in microstructure during the formation of gel induced by DPCD. With increased DPCD treatment setting conditions, the gel strength and crosslinking degree of shrimp surimi gel significantly improved (P < 0.05) with similar variation trends. Quantitative microstructural analysis revealed that the fractal dimension (Df) and the pore equivalent diameter of gel microstructure increased with the increase of DPCD treatment conditions. The lacunarity decreased and then increased, whereas pore number increased and decreased. According to the microstructural characteristics results, the surimi gel with 51.48 % degree of crosslinking induced at 25 MPa, 50˚C, and 60 min showed the most complex and homogeneous microstructure with the highest (Df), smaller lacunarity, an average pore equivalent diameter, and a larger pore number. The correlation analysis demonstrated that the crosslinking degree was strongly positively correlated with the gel strength. The Df, pore equivalent diameter and number of pores significantly positively correlated with the crosslinking degree, whereas the lacunarity strongly negatively correlated with the crosslinking degree. The present study showed that the DPCD treatment with a crosslinking degree of 51.48 % is the most optimum condition for better gel formation. The study could provide a theoretical basis for processing shrimp surimi with improved gel properties.

Label-free grain segmentation for optical microscopy images via unsupervised image-to-image translation

Grain boundaries play an important role in governing the mechanical and physical properties of polycrystalline materials. Therefore, quantitative analysis of grain structure is an important prerequisite for establishing structure-property relationships. However, this quantitative analysis is hampered by the low contrast and non-uniform illumination of optical microscopy images. Although previous studies have used neural networks to detect grain boundaries in optical micrographs, their practical applications have been restricted due to labeling costs for supervised learning and their sensitivity to defects present in a grain structure image. In this paper, we approach grain boundary detection as a real-to-virtual (R2V) translation problem, mapping a single real microstructure to virtual microstructures. With this perspective, our framework benefits from two learning schemes: unsupervised learning and physics-inspired learning. Extensive experiments demonstrate the superiority and generality of our framework. We expect that our approach will facilitate the use of data-driven techniques in the field of quantitative microstructure analysis.

Coordination complexes as substitutes for metallic powders in ceramic-matrix-composites manufactured by aqueous colloidal processing: Enhanced fracture toughness and quantitative microstructure analysis

In fabrication of metal-reinforced ceramics, metallic powders are used as one of the raw materials, however, they hinder obtaining nanometallic particles in the matrix. The use of metallic nanopowders is inefficient, because they are highly flammable and require special storage and handling conditions (cool, dry inert gas, tightly sealed). Replacing metallic powders with coordination complexes allows to obtain nanocomposites in harmless manufacturing processes. The use of coordination complex of molybdenum (molybdenyl acetylacetonate) allowed to fabricate ZrO2/Mo composites by slip casting and gelcasting. The increase in fracture toughness (ca. 25%) was observed with the little concentration of the metallic phase (up to 1.5 wt%) in a composite. It results from gaining fine metallic particles (ca. 62 nm) in the ceramic matrix. SEM analysis revealed that crack propagation is inhibited by nano-sized metallic particles according to three mechanisms: crack deflection, crack blocking by plastic deformation and interface debonding.

Deep learning enhanced Watershed for microstructural analysis using a boundary class semantic segmentation

The mechanical properties of the materials are determined by the size and morphology of fine microscopic features. Quantitative microstructural analysis is a key factor to establish the correlation between the mechanical properties and the thermomechanical treatment under which material condition has been achieved. As such, microstructural analysis is a very important and complex task within the manufacturing sector. Published standards are used for metallographic analysis but typically involve extensive manual interpretation of grain boundaries, resulting in measurements that are slow to produce, difficult to repeat and highly subjective. Computer vision and the evolution of artificial intelligence in the past decade can offer solutions to such problems. Deep learning and digital image processing techniques allow digital microstructural analysis to be automated using a fast and repeatable method. This paper proposes a novel boundary class semantic segmentation approach (BCSS) to identify each phase of the microstructure and additionally estimate the location of the grain boundaries. The BCSS is then combined with more traditional segmentation techniques based on the Watershed Transform to improve the identification and measurement of each feature within the microstructure using a new, hybrid automated digital microstructure analysis approach. The new method is validated on a published dataset of two-phase titanium alloy microstructure pictures captured using a scanning electron microscope. Measurements match the level of accuracy of accepted manual standards, and the method is demonstrated to be more reliable than other automated approaches. The influence of the subjective nature of manual labelling, required to train the proposed network, is also evaluated.

Synergistic Application of Machine Learning to Microstructural Characterization on Electrode Composites of Solid Oxide Fuel Cells

Solid oxide fuel cells (SOFCs) have been recognized as one of the most potent next-generation energy conversion systems, owing to their higher efficiency, better fuel flexibility, and smaller amount of pollution, unlike their counterpart fuel cells. SOFCs can be analyzed as a highly-complex multi-component materials system consisting of porous electrodes (cathodes and anodes), densified electrolytes, interconnects, and sealing materials, which are sequentially layered and characteristic of heterogeneous and multi-phase materials. The resultant power generation is achieved through the electrochemical reactions involving electronic carriers, ionic carries, and gaseous transports at anodes and cathodes, influencing their electrical, mechanical, and thermal properties. In particular, both cathodes and anodes are typically constructed based on three components: mixed ionic/electronic electrodes, electrolytes, and a porous section. Those three constituents should be optimized at both electrodes with the aim of maximizing the electrochemical reaction sites, i.e., triple-phase boundaries in association with the gaseous transport involved in oxygen reduction reactions at cathodes and hydrogen oxidation reactions at anodes, which leads to high cell performance and guaranteed durability. The electrochemical performances are crucially dependent on their complex microstructures, demanding for highly informative analyses on the electron microscopy images under investigation.This work reports quantitative microstructural analysis methodology incorporating machine learning-assisted microstructural analysis strategy. Quantitative microstructural interpretations were carried out without human involvement through an integrated combination of deep learning and focused ion beam-scanning electron microscopy (FIB-SEM) analytics on Ni/Y2O3-stabilized ZrO2 (Ni/YSZ) cermets. The Ni/YSZ/pore composites were analyzed for the automated extraction of microstructural parameters with the aim to preventing the subjective analysis problems and unavoidable artifacts frequently encountered in lengthy image processing tasks and eliminate biased evaluations. Considering the high volume of image data and future expectations for electron microscopy usage, FIB-SEM was efficiently combined with semantic segmentation targeted at detailed phase recognition on three constituents, i.e, Ni, YSZ, and pores. Traditional image processing analysis tools are in the synergistic manner combined with phase separation predictions by semantic segmentation algorithms, allowing for a quantitative evaluation of microstructural parameters. The combined strategy enables one to significantly enhance poor image quality originating from artifacts in electron microscopy, including charging effects, curtain effects, out-of-focus problems, and unclear phase boundaries encountered in searching for high-efficiency solid oxide fuel cells (SOFCs).

Stabilizing Effect of High Pore Fluid Pressure on Fault Growth During Drained Deformation

AbstractDilatant hardening is an accepted model for the stabilizing effect of high pore fluid pressure on fault slip and operates when deformation is undrained. To test whether high pore fluid pressure impedes fault propagation under drained conditions, we deformed highly permeable Darley Dale sandstone using strain rates of 10−4 s−1, 10−5 s−1, and 10−6 s−1, respectively. For each strain rate, we compared the inelastic behaviors and faulting styles among rocks deformed under different pore fluid pressures (Pf) (2–180 MPa). The confining pressure (Pc) was attuned to the pore fluid pressure throughout deformation to maintain a constant differential pressure (Pc − Pf) of 10 MPa. In samples deformed at 10−4 s−1 and 10−5 s−1, faulting behaviors were similar regardless of the magnitude of pore fluid pressure. However, when the strain rate was lowered to 10−6 s−1, we observed prolonged stress drops and slower slip velocities in samples deformed under high pore fluid pressures. In samples deformed at 10−6 s−1, we demonstrate that chemically assisted subcritical crack growth played an important role during faulting. A quantitative microstructural analysis revealed that slow faulting at slow strain rates was accompanied by pervasive microcracking and diffuse shear bands, which suggests pervasive subcritical cracking enabled slow faulting under drained conditions at the sample length scale. High pore fluid pressure may have facilitated slow faulting chemically by increasing the rate of subcritical cracking, mechanically via localized dilatant hardening, or both. Our results provide insight into the mechanics of faulting in natural settings where subcritical cracking is prevalent.

Microstructural Analysis of Solid Oxide Electrochemical Cells via 3D Reconstruction Using a FIB-SEM Dual Beam System

Solid oxide electrochemical cells (SOCs) have attracted increasing attention as energy conversion devices due to their high efficiency. The microstructures of SOCs play a critical role in their electrochemical performance, however, characterizing them is challenging due to their heterogeneous microstructure. This paper describes a quantitative analysis of SOC microstructures via 3D reconstruction technique using a focused ion beam-scanning electron microscope (FIB-SEM) dual beam system. The reconstructed SOC electrodes offer microstructural characteristics, including particle and pore size, tortuosity, connectivity, and triple-phase boundary (TPB) density. These in-depth analyses contribute to better understanding of the electrochemical behavior of SOCs.

Real-time quantitative imaging of RTV silicone pyrolysis

Quantitative microstructural analysis of Room Temperature Vulcanized (RTV) silicone pyrolysis at high temperatures is presented. RTV is used as a bonding agent in multiple industries, particularly filling gaps in ablative tiles for hypersonic (re-)entry vehicles and fire prevention. Decomposition of RTV is resolved in real time using in situ high-temperature X-ray computed micro-tomography. Tomographies are acquired every 45 seconds for four different linear heating rates ranging from 7 to 54°C/min. The microstructure is resolved below 5 μm/pixel, allowing for a quantitative analysis of the micro-structural evolution and porous network development. Results are heating rate dependent, and are evaluated for bulk sample volume change, porosity, pore network size, and densification from X-ray attenuation. The outcome of this work is critical to develop multi-physics models for thermal response.

Quantitative Microstructural Analysis by Correlative EPMA/EBSD/BSE Electron Microscopies Applied to Nuclear Ceramic

Abstract Electron microscopy maps were acquired in a mixed-oxide nuclear ceramic to probe independently and optimally grain structure [by Electron BackScattered Diffraction (EBSD)], porosity [by high-resolution BackScattered Electron imaging (BSE)], and elemental distributions [by Electron Probe MicroAnalysis (EPMA) in a separate instrument]. The maps were programmatically correlated spatially and integrated into a single multi-dimensional dataset allowing a correlative approach to assess the interdependence of key ceramic microstructural properties. Through the extraction of statistically representative non-random quantitative correlations between grain size, elemental enrichment, and intra-granular porosity that can be related to solid-state processes, the potency of the method is illustrated. The multi-layered hybrid maps produced by fusing data from the different acquisitions offer novel and unique insights into contiguous crystallographic, microstructural, and chemical features.

Analysis of the microstructural features of phase transformation during hardening processes of 3 martensitic stainless steels

The present paper investigates the microstructural features and associated hardening state of three different martensitic stainless steels (CX13, XD15 and MLX17 produced by Aubert&amp;Duval), subjected to three different thermomechanical treatments, aimed at producing hard materials for tribological applications. It is thus shown that all treatments (cementation, HF quenching or Age Hardening) are efficient to produce hard surfaces. The bulk martensitic state is also studied. Although the three martensites look somewhat different, it is shown that the transformation always obeys the KS orientation relationship with some variant selection, which produces a significant amount of twin boundaries. These results are quite different from those found in low C steels. Based on a quantitative analysis of the EBSD microstructures, a quantification of the various relative hardening contributions (phase transformation, grain size, dislocation density, solid solution effect or precipitation) is then proposed.

Permeability anisotropy in sandstones from the Soultz-sous-Forêts geothermal reservoir (France): implications for large-scale fluid flow modelling

The successful exploitation of geothermal reservoirs relies upon the understanding of fluid circulation in the subsurface. However, large-scale fluid flow modelling often assumes that the permeability of the layers of rock within the model are isotropic. We present here a laboratory study in which we assessed the permeability anisotropy of seven Buntsandstein sandstone cores taken from the geothermal reservoir at Soultz-sous-Forêts (France) in the Upper Rhine Graben. The porosity and permeability of our samples, cored parallel and perpendicular to bedding, ranged from 5.2 to 16.3% and from 2.48 × 10−18 to 7.66 × 10−14 m2, respectively. Our data show that permeability anisotropy can be up to four orders of magnitude in sandstones from the Buntsandstein, and that permeability anisotropy increases as a function of increasing porosity. Quantitative microstructural analysis combined with permeability modelling shows that the permeability anisotropy is the result of fine-grained and low-permeability laminations that are parallel or sub-parallel to bedding. We suggest, based on our data, that permeability anisotropy should be considered in future fluid flow modelling at geothermal sites within the Upper Rhine Graben.

Quantitative Microstructural Analysis of Cellular and Tissue Remodeling in Human Glaucoma Optic Nerve Head.

PurposeTo measure quantitatively changes in lamina cribrosa (LC) cell and connective tissue structure in human glaucoma eyes.MethodsWe studied 27 glaucoma and 19 age-matched non-glaucoma postmortem eyes. In 25 eyes, LC cross-sections were examined by confocal and multiphoton microscopy to quantify structures identified by anti-glial fibrillary acidic protein (GFAP), phalloidin-labeled F-actin, nuclear 4′,6-diamidino-2-phenylindole (DAPI), and by second harmonic generation imaging of LC beams. Additional light and transmission electron microscopy were performed in 21 eyes to confirm features of LC remodeling, including immunolabeling by anti-SOX9 and anti-collagen IV. All glaucoma eyes had detailed clinical histories of open-angle glaucoma status, and degree of axon loss was quantified in retrolaminar optic nerve cross-sections.ResultsWithin LC pores, the proportionate area of both GFAP and F-actin processes was significantly lower in glaucoma eyes than in controls (P = 0.01). Nuclei were rounder (lower median aspect ratio) in glaucoma specimens (P = 0.02). In models assessing degree of glaucoma damage, F-actin process width was significantly wider in glaucoma eyes with more damage (P = 0.024), average LC beam width decreased with worse glaucoma damage (P = 0.042), and nuclear count per square millimeter rose with worse damage (P = 0.019). The greater cell count in LC pores represented 92.3% astrocytes by SOX9 labeling. The results are consistent with replacement of axons in LC pores by basement membrane labeled by anti-collagen IV and in-migrating astrocytes.ConclusionsAlteration in LC structure in glaucoma involves migration of astrocytes into axonal bundles, change in astrocyte orientation and processes, production of basement membrane material, and thinning of connective tissue beams.