X-ray Computed Tomography Imaging Research Articles

Investigations of the failure mode for additive manufactured interlocked interface structure based on X-Ray CT image guided finite element analysis and experimental comparisons

Suture-inspired interlocked interfaces in nature exhibit promising industrial application potentials for advanced mechanical structures, devices and equipment. It is of significant scientific interest for studying the pull-out response and failure mode of interlocked structure as well as internal stress variations during the loading process. In this paper, finite element analysis (FEA) simulation based on X-Ray tomographic images is proposed for understanding the effects of manufacturing defects on the performances of interlocked structures, which are of critical importance for controlling the stress evolution of the interface structure and corresponding failure modes. Firstly, X-ray micro computed tomography (micro-CT) was applied for non-destructive scanning inspection of aluminum alloy additive manufacturing samples, high spatial resolution and calculation efficiency can be realized through tomographic image guided FEA scheme for generating high fidelity simulation results. Afterwards, non-uniform rational B-splines fitting method was employed for optimizing sample boundaries harvested from CT images, which allows smooth conversion from CT pixel images to high precision fitted finite element model. The B-spine fitting model can significantly improve the computational efficiency for finite element simulation without losing calculation reliability and resolution, overcoming the inherent contradictions between efficiency and resolution of the traditional pixel image finite element method. Finally, considering the mechanical contributions and interactions of external and internal porosity defects, the failure modes of the interlocked structures were further analyzed based on the results of finite element analysis.

Data processing and visualization of X-ray computed tomography images of a JOYO MK-III fuel assembly

ABSTRACT A method for processing and visualizing X-ray computed tomography (CT) images of a fuel assembly is developed and applied to a JOYO MK-III fuel assembly. The method provides vertical-section-like images to observe the spatial distribution of CT values in fuel pins and also supplies images that show the relationship between the linear heat rate (LHR) and radial CT-value distribution. In addition, an attempt to analyze the radial cracks in the CT images is proposed, and the results demonstrate the correlation between LHR and the radial cracks.

Evolution of Mineral-Accessible Surface Area Induced by Geochemical Reactions

Mineral dissolution and precipitation reactions occur in a wide range of porous media systems, driven by deviations from an equilibrium between solid and fluid phases. Reactions occur at mineral surfaces in contact with reactive fluids or accessible mineral surface areas. These mineral surface areas can be determined using a multiscale imaging approach and have been shown to improve the simulation of mineral reaction rates in porous media compared to other estimates of reactive surface area. As reactions progress, mineral surface area evolves, and reactive transport simulations often use a simplified model assuming spherical grains to estimate mineral surface area evolution. This, however, does not depict the evolution of reactive surfaces in porous media systems with varying mineral accessibility. This work aims to quantitatively assess the evolution of accessible mineral surface area in porous media for a multimineralic system undergoing mineral dissolution reactions induced by acid exposure. Before and after the reaction, accessible mineral surface areas are determined from 2D scanning electron microscopy, and 3D X-ray computed tomography imaging of a sandstone sample. The quantified evolution of accessible surface area is compared to the calculated mineral surface area evolution using current approaches. Results show an overall increase in total surface area due to the reaction; however, individual mineral surface areas may increase or decrease. Variation is observed in mineral surface area values measured from imaging and equations used in models. The evolution of mineral surface area is largely impacted by the total surface area as well as the pore connectivity rather than porosity and volume fraction evolution.

Fractal Informed Generative Adversarial Networks (FI-GAN): Application to the generation of X-ray CT images of a self-similar partially saturated sand

The recent increase in data availability fostered the development of new and more powerful data-driven numerical models, particularly machine learning. With a limited quantity of data in geomechanics applications, machine learning often suffers from unstable training and lack of convergence. Physics informed machine learning models have been proposed in the literature to mitigate the issue as an alternative to data-only approaches. In the present work, a new machine learning method is proposed: Fractal Informed Generative Adversarial Networks (FI-GAN) and applied to a case of X-ray CT images of partially saturated sand. This method consists of training an informed adversarial model using pore fractal dimension. The semi-supervision provided by pore fractal metrics presents two advantages, firstly, it regularises the training; secondly, it improves the distribution of pore fractal dimension, which is correlated with permeability. Results show that the generated images improve the fractal distribution of the informer phase in the partially saturated material (i.e., water) and the other two phases. The approach is validated with physics flow simulations. The generated images can be applied in the calibration of image processing tools, filling missing data in X-ray CT scans, generating microscales in multiscale applications, and data augmentation, among others.

Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations

X-ray computed tomography (XCT) is one of the most powerful imaging techniques in non-destructive testing (NDT) for detecting, analysing and visualising defects such as pores, fibres, cracks etc. in industrial specimens. Detecting defects in X-ray images, however, is still a challenging problem, as it strongly depends on the quality of the XCT images. Numerical XCT simulation proved to be valuable in order to increase both image quality and detection performance. In this work, we thus analyse the differences between traditional segmentation techniques (i.e., k-means, watershed, Otsu thresholding) and deep learning-based methods (i.e., U-Net, V-Net, modified 3D U-Net) in terms of their defect detection capacity using virtual XCT images. For this purpose, we apply the probability of defect detection (POD) approach on simulated X-ray computed tomography data from aluminium cylinder heads. The XCT simulation tool SimCT was used to generate X-ray radiographs and respective reconstructions from a specimen series which features different well-defined defects with varying sizes, shapes and locations. To generate POD curves and to specify detection limits, the segmentation algorithms are used in predefined regions for defect detection via a hit/miss approach. A comparison and visualisation of six different types of defects is illustrated in 2D and 3D images, together with their POD curves and detection limits.

Evaluation of XCT image quality

The direct visual quality of an X-ray computed tomography (XCT) image is very subjective. Metrics to quantify the image quality have been defined for X-ray radiography, but some of these no longer seem appropriate for XCT images. This has been highlighted through an XCT interlaboratory comparison campaign, performed in the XCT working group of the French Confederation for Non-destructive Testing (COFREND), on an additively manufactured part at medium-energy XCT (230-520 kV). The calculation of several of these metrics has been performed on the images acquired with nine different XCT systems to demonstrate their representativeness and compared to visual inspections. This comparison campaign confirms that several of them are no longer representative of the image quality.

Multifunctional amino functionalized graphene quantum dots wrapped upconversion nanoparticles for photodynamic therapy and X-ray CT imaging

Multifunctional amino functionalized graphene quantum dots wrapped upconversion nanoparticles for photodynamic therapy and X-ray CT imaging

Multiscale Finite-Element Analysis of Damage Behavior of Curved Ramp Bridge Deck Pavement Considering Tire–Bridge Interaction Effect

The present study developed a three-dimensional (3D) multiscale modeling approach to investigate the mesoscale damage behavior of curved ramp bridge deck pavement subjected to tire loading. First, a full-scale tire–bridge interaction finite element (FE) model was established to solve the macroscopic response of deck pavement during vehicle turning. Subsequently, the 3D mesostructure of the asphalt concrete layer was reconstructed from X-ray computer tomography (CT) images through a digital image processing (DIP) technology. Finally, a homogenization method was adopted to link the mechanical properties of deck pavement at two different scales, and a mapping procedure was employed to transfer the boundary displacements from macroscale pavement target element to the mesoscale representative volume element (RVE) model. Also, the bilinear cohesive elements were applied to simulate damage initiation in the RVEs. The results showed that smaller curvature radius and higher travel speed can promote unbalanced stress and local damage of the outer loading zone. From the mesoscale viewpoint, reducing curvature radius or increasing travel speed could result in crack direction shifts and increase the irregularity of microcrack distribution. In addition, transverse microcracks are commonly found in the center of the tire load, while longitudinal microcracks are distributed on the deck pavement surface around the tire edges and the dual-tire clearance center.

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.

COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.

An in-situ X-ray computed tomography imaging apparatus with stack pressures for rechargeable batteries

Herein, a novel in-situ X-ray computed tomography (CT) imaging apparatus with stack pressures is proposed to detect the micro-structural evolution at the interface during the electrochemical process. And the relationship between the electrochemical performance and the interface morphology of lithium metal batteries has been effectively investigated through this in-situ technology. It can be observed that the growth of Li dendrites and their morphology evolution are directly dependent on the stack pressures. The Li dendrite grows from the needle-structure to the island-structure, accompanied by the tip-growth mode transferring to the base-growth mode in the range of the critical pressure. Moreover, the growth of Li dendrites can also be effectively inhibited when loading a suitable stack pressure. This novel in-situ CT imaging technology provides a feasible way to underlying the interface micro-structure evolution, electrochemical-pressure complexions, and safety of next-generation rechargeable batteries.

Machine Learning Estimation of Plateau Stress of Aluminum Foam Using X-ray Computed Tomography Images

Owing to its lightweight and excellent shock-absorbing properties, aluminum foam is used in automotive parts and construction materials. If a nondestructive quality assurance method can be established, the application of aluminum foam will be further expanded. In this study, we attempted to estimate the plateau stress of aluminum foam via machine learning (deep learning) using X-ray computed tomography (CT) images of aluminum foam. The plateau stresses estimated by machine learning and those actually obtained using the compression test were almost identical. Consequently, it was shown that plateau stress can be estimated by training using the two-dimensional cross-sectional images obtained nondestructively via X-ray CT imaging.

MCC-Net: A class attention-enhanced multi-scale model for internal structure segmentation of rice seedling stem

Internal structural parameters of rice seedling stems are of great significance for rice growth detection, rice selection, breeding, and damage examination. Aiming at the problems of non-repeatability and low detection accuracy in the existing plant internal structure phenotypic traits detection methods, this paper presents a non-destructive segmentation method for examining the internal structure of rice seedling stems based on deep learning. We use a standard X-ray CT imaging technology to obtain non-destructive tomographic images of rice seedling stems and then design a class attention-enhanced multi-scale segmentation model (MCC-Net), where UNet is used as the backbone network. Specifically, the proposed MCC-Net mainly consists of three core components: multi-scale convolutional block (MCB), coordinate spatial attention (CSA) module, and class attention enhancement (CAE) module. MCB is the main component of the encoder to improve the feature extraction ability of the model for regions of different sizes in the internal structure. CSA is embedded into the UNet skip connections to enhance the expression of effective features and automatically locate the regions with different structures of rice seedling stems. CAE is designed to calculate the dependencies between image pixels and categories, which can enhance the feature expression from the perspective of categories and correct the category errors in the segmentation results. The experimental results show that MIOU, average dice coefficient and average precision of our proposed MCC-Net model on the self-built rice seedling stem CT image dataset are 92.56%, 96.33% and 96.59% respectively. Compared with several state of the art models, the proposed model achieves better segmentation performance on the rice seedling stem CT image dataset.

Quantitative estimation of contribution of pore size and the distance between nearest neighbour pores on high cycle fatigue (HCF) crack initiation in Ti-6Al-4V MIM-sintered materials

The fatigue damages due to pores of MIM-sintered Ti64 were investigated using the synchrotron X-ray CT imaging. The effect of pores on the fatigue strength limits was analysed independently from that of grain size by introducing the fatigue limit ratio RF. The pores near to the maximum-stress area were identified by elastic FEM. The maximum stress intensity factor KI-max at the edge of the identified pores showed a negative correlation with RF. Moreover, the initial cleavage in the crack was influenced by the mutual interference of both stress fields by the proximity of the identified pore and its adjacent pores.

Enhanced flexural performances of cementitious composite beams with continuously graded steel fiber distribution

This paper focuses on improving the flexural behavior of steel fiber-reinforced beams with continuously graded structure. Successive vibrating after casting is assumed to be a feasible way to produce graded fiber-reinforced beams (GFRBs), resulting in fibers purposefully distributed in the tensile region. The fiber orientation and distribution are quantified through X-ray computed tomography (XCT). The porosity and pore size distribution are evaluated on the basis of XCT images and mercury intrusion porosimetry. Influences of fiber re-arrangement and microstructure on the compressive strength, the first-cracking strength, the ultimate flexural strength and the flexural toughness index are then investigated. The results show that: (1) the fibers deflect towards the longest side of the beam, and distribute more concentrated at the beam bottom, i.e. more fibers are available to withstand tension, due to the successive vibrating treatment; (2) the high vibration time does not induce a negative influence on the mortar matrix, but rather refines its microstructure with lower porosity and smaller pore size; (3) the flexural behavior is enhanced for fiber re-arrangement and microstructure improvement, revealing that the designed GFRB is capable of sustaining higher flexural strength and energy absorption capacity than the typically vibrated beam with the same fiber content.

Characterization of two-phase flow in a faulted sandstone

Fault zones present in some underground CO2 storage formations can impact on injectivity, storage capacity, and the path taken by migrating CO2, including possible leakage to overlying formations and the escape of a plume beyond the area of a demarcated storage lease licence. A thorough understanding of the 2-phase flow in fault zones is therefore essential to de-risk any storage location where faults could be present. Many previous studies have addressed top-seal risks from fault reactivation, but the influence of intra-reservoir faults on flow within the target storage formations has received less attention. Studies of intraformational faults in sandstones have typically used plugs cored from outcrops or from subsurface boreholes, where important aspects of the geological history such as the stress conditions during faulting, and the degree of post-faulting compaction and cementation may be poorly constrained. Moreover, only intact fault rocks can be cored and re-loaded under stress with this approach, so that it is very difficult to establish the interacting effects or rock fractures and cataclastic gouge in a single experiment using borehole/outcrop samples.This study investigates newly created fault rock in sandstone that has been deformed under controlled conditions and without the effects of secondary cementation. We used a large direct shear rig to deform a large, water-saturated Bentheimer sandstone block to sufficient displacement (40mm) and under enough overburden pressure (25 MPa) to create a zone of cataclastic gouge several mm thick bounded by a damage zone of fractured rock. The sheared block was cored at an inclined angle while deep frozen to extract an oriented plug containing a complete and intact section of fault rock that could be reloaded under hydrostatic conditions in an x-ray transparent core holder. The faulted plug of Bentheimer was used for running steady-state drainage and imbibition 2-phase flow experiments. The fluid saturation and its spatial distribution in 3D inside the faulted plug were dynamically monitored during all stages of the experiment in a medical x-ray CT scanner.Analysis of the flow patterns revealed from the X-ray CT images collected over time enables us to characterize in detail, the influence of recently formed fault core and damage zones on fluid flow both during drainage as gas breaks across the fault zone, and as water re-invades during imbibition. The fault core acts as a semi-permeable membrane that is only permeable to the wetting phase (water) under pressure differentials less than the capillary entry pressure (Pce) of the membrane. During drainage stages, the high Pce of the fault core led to backfilling of the upstream end of the core and almost to compartmentalization of the sample. The fracture network that exists in the damage zone played a critical role in keeping the upstream side of the fault core connected to the downstream side. During imbibition stages, the high Pce of the fault core led to significant gas trapping at the upstream side of the fault core adjacent to the fault core-damage zone interface. While the fault core formed a barrier to flow, the fracture network can provide bypass pathways for gas flow. In this particular sample bypassing through fractures was sufficient such that for a particular value of average gas saturation the flow rate was faster in the faulted rock than in the unfaulted reservoir rock.

Laboratory hydraulic fracturing in layered tight sandstones using acoustic emission monitoring

Understanding the propagating mechanism of complex fracture network is essential for optimizing the hydraulic fracturing design scheme. Laboratory hydraulic fracturing experiments were performed on three tight sandstone specimens with bedding plane perpendicular, oblique and parallel to the axial direction, respectively. Before hydraulic fracturing, three specimens were first loaded close to the critical stress state under 22.5 MPa confining pressure. The entire hydraulic fracturing process was monitored by acoustic emission (AE) and ultrasonic measurement. The experimental results show that the hydraulic fracturing behaviors were significantly influenced by bedding structure and water infiltration. The bedding plane plays an important role on breakdown pressure, distribution characteristics of AE activities and hydraulic fracture networks. The bedding-oblique specimen exhibits lower breakdown pressure than those of the bedding-parallel and bedding-perpendicular specimens. AE activities in the bedding-oblique specimen is characterized by lower b-value, while higher b-value is found in other two specimens. X-ray computed tomography images of the broken specimens show an excellent agreement between AE hypocenters and macroscopic fractures, indicating geometrical morphology of macroscopic fractures is closely related to the bedding plane and well imaged by AE hypocenters. A bedding-parallel shear fault formed in the bedding-oblique specimen. Complex shear fault linking several bedding-parallel echelon arrays or branches formed in the bedding-parallel and bedding-perpendicular specimens. Water infiltration affects the hydraulic fracturing behaviors by inducing AE events initiate at transition region of wet-dry boundary around the central borehole. In view of the formation of fracture network and the risk of induced seismicity, the influence of bedding structure and water infiltration should be taken into consideration under given in-situ stress regime during the hydraulic fracturing design scheme.

Volumetric quantification of melting and solidification of phase change materials by in-situ X-ray computed tomography

Solid–liquid phase transitions in phase change materials (PCM) usually exhibit a complex dynamics, which critically determines the performance of latent heat thermal energy storage systems (LHTES). Experimental approaches enabling to track the dynamics of the entire PCM solid–liquid phase transition are thus crucial to provide relevant data for PCM material development and to validate PCM-models used to design efficient LHTES. In this work, we show how in-situ dynamic X-ray computed tomography (XCT) can be used to track the dynamics of the solid–liquid phase transition in PCMs. The method based on time-lapse XCT is illustrated based on data collected for two PCMs (ice/water and calcium chloride hexahydrate) during melting and solidification processes, respectively. Key enabler of the method is the density difference between solid and liquid PCM, which facilitates a clear identification of the solid and liquid PCM phases and, thus, tracking their spatial evolution in time. Analysis of the transient liquid fraction curves reveals a volumetric melting rate of 0.55 (% vol)/min for ice with a total melting time of 165 min, whereas calcium chloride hexahydrate undergoes a partial solidification at −0.30 (% vol)/min, with an overall volumetric shrinkage of 12.7 %. Furthermore, an image-based approach to quantify the time-dependent liquid volume fraction curve from the sequences of XCT imaging data, is presented. The extracted liquid volume fraction curves capture the entire dynamics of the solid–liquid phase transition and can either be directly inserted in the mass and energy balance equations of currently existing PCM models to predict LHTES performance or used for PCM-models validation.

Simulating Knee-Stress Distribution Using a Computed Tomography-Based Finite Element Model: A Case Study

This study aimed to evaluate the mechanism of progression involved in knee osteoarthritis (OA). We used the computed tomography-based finite element method (CT-FEM) of quantitative X-ray CT imaging to calculate and create a model of the load response phase, wherein the greatest burden is placed on the knee joint while walking. Weight gain was simulated by asking a male individual with a normal gait to carry sandbags on both shoulders. We developed a CT-FEM model that incorporated walking characteristics of individuals. Upon simulating changes owing to a weight gain of approximately 20%, the equivalent stress increased extensively in both medial and lower leg aspects of the femur and increased medio-posteriorly by approximately 230%. As the varus angle increased, stress on the surface of the femoral cartilage did not change significantly. However, the equivalent stress on the surface of the subchondral femur was distributed over a wider area, increasing by approximately 170% in the medio-posterior direction. The range of equivalent stress affecting the lower-leg end of the knee joint widened, and stress on the posterior medial side also increased significantly. It was reconfirmed that weight gain and varus enhancement increase knee-joint stress and cause the progression of OA.

Phase field method with strain orthogonal decompositions for modelling of damage in heterogeneous materials obtained by X-ray computed tomography images

The phase-field method has become a robust tool to describe the complex crack nucleation and propagation in heterogeneous materials. In these materials, an interaction between bulk brittle damage and interfacial damage through the splitting the strain tensor into a bulk strain part and a jump strain part at the interfaces. Moreover, the material response of the heterogeneous materials in tension and compression, the bulk strain part needs to be decomposed into a positive part and a negative part. However, most of the previous works, these decompositions do not verify the negative part be orthogonal the positive part, which lead to the inaccuracy in the material mechanical behavior. To solve the shortcomings, the strain orthogonal decompositions problem that is applied to decompose the bulk strain in our work. In the present work, author combined the phase-field modeling taking into account interfacial damage with the problem of the bulk strain orthogonal decompositions by two proposed solutions. The successful prediction of the crack propagation for the inclusion-matrix structures and the multi-phases materials of X-ray computed tomography (XR-CT) images by two proposed solutions will be demonstrated in several numerical examples.

Titanium porous-transport layers for PEM water electrolysis prepared by tape casting

While the porous-transport layer (PTL) is a key component in PEM electrolyzers, it is one of the most underexplored due to limited available structures. In this work, we present a novel PTL design for PEM water electrolyzers enabled by a cost-effective, scalable tape-casting technique. A precise control of the PTL pore structure is achieved by incorporating poreformers of various sizes, and by varying the titanium and poreformer ratio. The structures are characterized with SEM and synchrotron X-ray computed tomography imaging techniques. Comprehensive electrochemical performance analysis demonstrates that higher titanium loading provides improved contact at the catalyst-layer/PTL interface but suffers from severe mass-transport losses due to gas bubbles. We solve this mass-transport problem by mixing in large poreformer beads that produce a highly porous structure with excellent gas removal properties yet still maintaining mechanical integrity. The PTL fabricated with 60:40 Ti:PMMA ratio and 60 μm PMMA bead size outperformed the standard commercial Ti powder-based PTL by 62 mV at 4 A/cm2.