Tomography of Materials and Structures最新文献

Synchrotron-based X-ray microtomography (μCT) is a valuable technique to study the internal structure of heterogeneous samples with high spatial and temporal resolution. However, synchrotron X-ray imaging, such as many microscopy methods, is solidly limited by its Field of View (FOV): a challenge when approaching large and/or highly detailed volumes at high spatial imaging resolution. In this study, we consider two techniques used to increase the FOV in µCT for studying Li-ion batteries, Local Tomography Stitching (LTS) and Sinogram Oriented Stitching (SOS), and compare in terms of scan time, processing efficiency and computing storage. We complement our study by estimating the impact of different battery geometries on the stitching performance for similar scanning parameters. Evaluation of the two presented techniques reveals that both provide equally satisfyingly stitched volumes. Nevertheless, it is demonstrated that SOS is predominantly more efficient for the considered battery geometries, requires fewer scans in total, and hence has a lower time and storage demand in comparison to LTS. However, technical constraints make the SOS technique more difficult to implement. Further discussed are differences in acquisition and reconstruction for the two techniques, addressing the processing efficiency for both SOS and LTS techniques and we shall provide indicators for selecting the most suitable stitching technique.

Our ability to visualize and quantify the internal structures of objects via computed tomography (CT) has fundamentally transformed science. As tomographic tools have become more broadly accessible, researchers across diverse disciplines have embraced the ability to investigate the 3D structure-function relationships of an enormous array of items. Whether studying organismal biology, animal models for human health, iterative manufacturing techniques, experimental medical devices, engineering structures, geological and planetary samples, prehistoric artifacts, or fossilized organisms, computed tomography has led to extensive methodological and basic sciences advances and is now a core element in science, technology, engineering, and mathematics (STEM) research and outreach toolkits. Tomorrow's scientific progress is built upon today's innovations. In our data-rich world, this requires access not only to publications but also to supporting data. Reliance on proprietary technologies, combined with the varied objectives of diverse research groups, has resulted in a fragmented tomography-imaging landscape, one that is functional at the individual lab level yet lacks the standardization needed to support efficient and equitable exchange and reuse of data. Developing standards and pipelines for the creation of new and future data, which can also be applied to existing datasets is a challenge that becomes increasingly difficult as the amount and diversity of legacy data grows. Global networks of CT users have proved an effective approach to addressing this kind of multifaceted challenge across a range of fields. Here we describe ongoing efforts to address barriers to recently proposed FAIR (Findability, Accessibility, Interoperability, Reuse) and open science principles by assembling interested parties from research and education communities, industry, publishers, and data repositories to approach these issues jointly in a focused, efficient, and practical way. By outlining the benefits of networks, generally, and drawing on examples from efforts by the Non-Clinical Tomography Users Research Network (NoCTURN), specifically, we illustrate how standardization of data and metadata for reuse can foster interdisciplinary collaborations and create new opportunities for future-looking, large-scale data initiatives.

The present study delivers a methodology for investigating the gradual damage development in a carbon fibre-reinforced cross-ply polymer composite during a sequence of thermo-mechanical loadings with the help of X-ray computed tomography. The procedure allows an in-depth analysis of the occurrence and nature of the multiple cracks that form within layers oriented perpendicular, or transverse, to the loading direction. This is achieved by using Attention U-Net architecture for semantic segmentation of the transverse cracks. The model shows promising results, through an ability to identify all the transverse cracks and reflect the damage progression. The described method provides a robust routine for analysing challenging polymer composite tomographic datasets.

Mono- and poly-disperse assemblies of spherical particles are investigated in terms of their average and partial coordination numbers by means of X-ray microtomography using a novel morphology-based image processing method to statistically distinguish true contacting particles from very close ones having apparent contacts arising from image artefacts. This technique is shown to reduce overestimations given by the laborious liquid-bridge method while corroborating theoretical predictions such as $\overline{Z}\le 6$ for random beds of mono-sized spheres and trends of the partial coordination numbers in binary mixtures of spheres. This method also provides a detailed and unbiased visualization of the long-range connectivity between similar particles which suggests that for partial coordination numbers ${\overline{Z}}_{ii}>3$, chains of particular contact-category are formed throughout the assembly.

Computed tomography spans a versatile set of techniques that range several length scales and modalities. It is common to them all that the sample must be prepared in a way which allows for addressing the scientific question(s) posed as well as being suitable for the specific setup of the experiment. We present two lathe-based sample preparation workflows developed to prepare biomineralized samples (here bone) for two very different experiments in terms of setup, types of questions asked, and sample requirements. The first experiment, involving the measurement of high throughput (synchrotron) micro-computed tomography, required the preparation of many samples with homogeneity in size, shape, and bone site. This was achieved through a particular sequence of cutting and embedding steps finalized by lathe milling. The resulting samples were cylindrical in shape with diameters close to the field of view of the ensuing tomography experiment, which allowed maximizing the investigated sample volumes. The second experiment was a combined ptychography and X-ray fluorescence nano-computed tomography experiment, which required preparation of a few-micrometer-sized sample. Moreover, the scientific interest was in a specific, localized feature in bone. Thus, the sample had to be extracted from a precise location from within the whole bone. Again, the developed workflow comprised many steps, including both lathe milling and focused ion beam milling. Importantly, localized preparation was enabled by measuring in-house X-ray micro-computed tomography at crucial points in the workflow. The presented workflows provide examples of preparation pathways that can be standardized and strongly increase the throughput, quality, and success rate of tomography experiments.

3D experimental data of simultaneously high temporal and spatial resolution are key to validating computational models of materials phenomena. In this study, we exploit lab-based X-ray imaging, combining absorption and diffraction contrast tomography, to capture the evolution of grain structure over a series of interrupted heat treatments of a semisolid Al-Cu alloy. The time resolved response measured on the present Al-Cu model system provides insights into the rearrangement, densification and coarsening of powder compacts at late-stage sintering. The initial Al-Cu microstructure containing 1934 grains dropped to 934 grains after ten annealing steps, while the mean grain size increased from 194 µm to 247 µm. The grain maps of all eleven temporal states are made publicly available to the scientific community for further analysis via the Materials Data Facility. Preliminary statistical investigations of the growth of individual grains show a clear tendency for disappearing grains to be among the smaller grains at the beginning of the experiment. In addition, the rotations of individual grains are generally small fluctuations, but when an abruptly large rotation is observed, it is more likely to occur for a smaller grain at the last annealing step(s) before the grain vanishes. The nature of the data also enables interrogating a few grains that display rotation bursts within the context of their entire local environment to reveal the impact of crystallography and grain contacts upon the microstructural evolution.

Currently, biofilms colonizing surfaces are mainly imaged in 2D by conventional techniques, such as optical or scanning electron microscopy. Confocal laser scanning microscopy or optical coherence tomography can visualize biofilms in 3D, but they suffer from a limited penetration depth and cannot visualize biofilms in opaque materials. Micro-computed tomography (µCT) can overcome these issues, but µCT cannot easily distinguish biofilm structures from water due to a lack of contrast difference. Within this research, five contrast-enhancing staining agents (CESAs) were evaluated for their staining potential of cyanobacterial biofilms, aiming to visualize these biofilms in 3D. Isotonic Lugol and 1:2 hafnium(IV)-substituted Wells-Dawson polyoxometalate (Hf-WD 1:2 POM) were the most promising, as they allowed visualization of the biofilms and revealed structures in the stained biofilms. Staining with isotonic Lugol could clearly visualize bundles of filaments within the biofilm, while Hf-WD 1:2 POM revealed a smooth biofilm. It is assumed that both CESAs have a different affinity towards the biofilms and could thus be used complementary. Monolacunary Wells-Dawson polyoxometalate (Mono-WD POM) showed moderate discrimination while staining with cationic iodinated CA4+ and Hexabrix® (Guerbet) containing anionic ioxaglate did not allow to distinctly visualize the biofilms. These results indicate that µCT, together with CESAs such as isotonic Lugol and Hf-WD 1:2 POM, can be used as a tool to image extensive biofilms or microbial mats in 3D. Further research will determine whether these CESAs are suitable for visualizing biofilms within opaque porous media.

A method for semantic segmentation of microstructure evolution from 4D imaging data is described and demonstrated. The method is based on a joint histogram describing the time history of the grayscale in each voxel of the images. After identifying and labeling clusters in the joint histogram, the labels are mapped back to the image. The results demonstrate accurate segmentation and characterization of sample evolution. The advantages of the proposed method include automatic segmentation of many time steps and the ability to track grayscale evolution over time and thereby discriminate similar evolution in different material phases. The method is demonstrated through application to 4D X-ray tomography datasets of temperature cycling in cement mortar and tensile testing of a cast iron sample. Water and air exchange in a pore inside the cement mortar is successfully segmented as a function of temperature. In the case of the deforming cast iron sample, several damage mechanisms are identified and segmented. The method is implemented in an open-source Python package called evoSegment.

This work investigates the impact response of a hybrid two-dimensional (2D) tubular braided composite through an ex-situ examination of its internal structure. An inter-ply hybrid laminate of biaxial braided carbon and aramid layers was manufactured. The sample underwent split Hopkinson pressure bar (SHPB) impact testing, where the applied energy induced barely visible impact damage (BVID). Volumetric representations of the sample were created before and after impact testing using micro-computed tomography (µCT). Novel algorithms were developed to assess properties of the braided composite sample. First, voids in the sample before and after impact were identified through a series of image processing techniques, after which various void properties were extracted. Each void was then matched between datasets to track property change caused by impact. Next, damage in the sample after impact was identified through another series of image processing techniques, then quantified and characterized in the context of the full sample. Statistical analyses in the form of paired-sample t-tests were performed for void properties and overall surface topologies. Additionally, measurement of 3D strain within selected regions of the sample was performed using digital volume correlation (DVC). Results showed that the impact damage did not cause significant void enlargement, but instead caused surface topology shifts, particularly at the inner surface, owing to plastic deformations caused by impact damage, which were primarily caused by delamination and developed from large voids at the site of impact. The hybrid material configuration exhibited a phenomenon where the inner and outer layers minimized cracking in the middle layer, causing plastic deformation to be the primary impact response of the middle layer. Through the analysis processes developed in this work, quantitative assessment of tubular braided composites was achieved by measuring changes in voids caused by impact at the individual level, and by examining the damage profile while accounting for the voids. The methodology can be applied to various material configurations to provide meaningful insight into the effects of hybridization in the impact response of braided composites.

Proper ore characterisation is essential for understanding ore deposits and developing efficient mineral processing flow sheets. Conventional mineralogical and chemical techniques are usually used to study ores, but they can be destructive and, in some cases, provide only 2D information. Computed tomography (CT) is an emerging technology in the raw materials sector enabling the non-destructive 3D analysis of the ore mineralogy and microstructure. However, single-energy CT (SECT) has some limitations concerning the accurate imaging and differentiation of polyphase geomaterials comprising a broad range of attenuation properties. By contrast, dual-energy CT (DECT) uses two different X-ray energies to acquire data, which can be used to distinguish between materials with similar attenuation properties. This study explored the application of DECT for the analysis of a polyphase graphitic ore. A sequential fusion approach was utilized to combine data obtained from different X-ray energy scans at high spatial resolution, and varying weighting factors were applied to determine the optimal contribution of each energy level and spectrum. Both, SECT and DECT datasets were quantitatively evaluated based on the contrast-to-noise-ratio (CNR) and Q factor. The findings demonstrate that DECT significantly improves image contrast compared to SECT while further increases image sharpness. As a result, DECT may enable more accurate segmentation and, therefore, more accurate quantitative 3D analysis of graphite ores.