Tillage effects on topsoil structural quality assessed using X-ray CT, soil cores and visual soil evaluation

The intensification of soil use, as in integrated croplivestock system (ICLS), might promote soil structural degradation. A field method to evaluate the soil structural quality is the Visual Evaluation of Soil Structure (VESS). Studies on the application of this method to ICLS are few. This work aimed to evaluate the structural quality of a Dystrudept under ICLS and different grazing intensities through VESS. Thus, the soil structure was evaluated in light grazing (LG) and heavy grazing (HG) in comparison to a neighboring native forest (NF) as reference area. After the grazing period, 10 trenches were dug in each area to collect soil monoliths. The identification of structural differences and the attribution of visual scores were carried out according to the VESS. In NF and HG soils, up to 30 cm deep, it was possible to identify 2 layers with distinct structures, while, in the LG, up to 3 layers were identified. The NF soil presented the best structural quality. Regarding both grazing intensities, there was degradation of the structural quality between soil layers when compared to the NF. The increase in grazing intensity in ICLS promoted in-depth degradation of the soil structural quality. The VESS method was shown suitable to evaluate soils under ICLS management with LG and HG.

Assessing the effects of land use change from rice to vegetable on soil structural quality using X-ray CT

Soil structure changes induced by tillage and reduction of machinery traffic on sugarcane – A diversity of assessment scales

A statistical description of X-ray CT (computerized tomography) imaging, from the projection data to the reconstructed image, is presented. The Gaussianity of the pixel image generated by the convolution (image reconstruction) algorithm is justified. The conditions for two pixel images to be statistically independent (for a given probability) and the conditions for a group of pixel images to be a spatial stationary random process and ergodic in mean and autocorrelations are derived. These properties provide the basis for establishing the stochastic image model and conducting the statistical image analysis of X-ray CT images.

The relationship between the behavior of water in snow and its microstructure is crucial to improve the prediction of wet snow disasters. X-ray computed tomography (X-ray CT) is frequently used to observe snow microscopically. However, distinguishing between ice and water in the X-ray images is difficult because ice exhibits an X-ray absorption coefficient similar to that of water. In contrast, magnetic resonance imaging (MRI) acquires nuclear magnetic resonance (NMR) signals of protons in a liquid and visualizes the NMR signal intensity, enabling discrimination between water and ice signals. However, snow grains and pore spaces cannot be distinguished in MRI images because they do not generate NMR signals. To investigate the relationship between the microstructure of snow and the distribution of liquids in snow, we developed a novel method that combines X-ray CT and MRI images to compensate for the disadvantages associated with each method. Using this method, we successfully visualized where liquid (C12H24) occupied pore spaces. We also showed the possibility of using C12H24 instead of water to obtain water retention curve of snow cover, which is a fundamental aspect of hydraulic properties. Although there is room for improvement in the visualization of water in snow, such as shortening the imaging time to escape snow metamorphosis and image superimposition methods, this method is expected to effectively elucidate the behavior of water in snow and clarify the characteristics of wet snow.

Investigations of biphasic monodisperse soft (rubber) and stiff (glass) particle mixtures under hydrostatic conditions show an interesting behavior with regard to the effective stiffness. P‐wave modulus measured by acoustic wave propagation at ultrasonic frequencies showed a significant decline while more soft particles are added, that is, higher rubber volume fractions, due to a change in the microstructure of the granular medium. However, for small volume fractions of soft particles, it could be observed that the P‐wave modulus is increasing. This result cannot be explained by classical mixture rules or effective medium theories. For the understanding of those effects, a detailed insight into the microstructure of the granular medium is necessary. To gain this information and link it later back to the measured effective mechanical properties, high‐resolution micro X‐ray computed tomography (XRCT) imaging is a well‐established tool. With XRCT imaging, the granular microstructure can be visualized in 3D and characterized subsequently. Combining classical effective characterization methods with XRCT imaging can help to solve a variety of multi‐scale problems. Performing the characterization step in situ, meaning inside the laboratory‐based XRCT scanner, has the advantage that exactly the same samples are mechanically characterized and visualized. To address the mentioned observation above, we designed a low X‐ray absorbing oedometer cell with integrated broadband piezoelectric P‐wave transducers which enables this kind of investigation inside a laboratory‐based XRCT scanner. The focus of this contribution is on the general experimental methodology which can be transferred to other multi‐scale problems. It starts with a description of the image acquisition and ends with the post‐processing of the in situ acquired image data. To demonstrate this, cylindrical samples consisting of the same monodisperse rubber and glass particle mixtures that were studied before under hydrostatic stress conditions are considered. Selected results are presented to explain the single steps.

Tillage-induced effects on SOC through changes in aggregate stability and soil pore structure

Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive inspection technique often used for AM parts. Although defects within AM specimens can be identified and segmented by manually thresholding the XCT images, the process can be tedious and inefficient, and the segmentation results can be ambiguous. The variation in the shapes and appearances of defects also poses difficulty in accurately segmenting defects. This paper describes an automatic defect segmentation method using U-Net based deep convolutional neural network (CNN) architectures. Several models of U-Net variants are trained and validated on an AM XCT image dataset containing pores and cracks, achieving a best mean intersection over union (IOU) value of 0.993. Performance of various U-Net models is compared and analyzed. Specific to AM porosity segmentation with XCT images, several techniques in data augmentation and model development are introduced. This work demonstrates that, using XCT images, U-Net can be effectively applied for automatic segmentation of AM porosity with high accuracy. The method can potentially help improve quality control of AM parts in an industry setting.

X-ray computed tomography (CT) imaging plays an essential role in disease diagnosis due to its noninvasive, painless mode and superior penetration depth. However, the resolution of the soft tissue ...

This paper presents the characterization of bearing failure mechanisms in composite joints with countersunk bolt by applying an X-ray Computed Tomography (XCT) technique and a developed bearing failure model to build the physical mechanisms into the framework of continuum damage mechanics (CDM) in our composite bolted and bonded analysis tool for Abaqus (CB2ATA). The high-fidelity XCT was explored for the detection and characterization of bearing failure in bolted composite components without removing the fastener, which could introduce significant scatter in XCT scan due to its high density, compared with lightweight carbon fiber reinforced polymer (CFRP) composite laminates. A static bearing model was also developed for the damaged material response in the bearing region based on a micromechanics analysis in the longitudinal and transverse directions. In this study, single shear bearing (SSB) tests was firstly executed with XCT scan, and then progressive failure analyses were performed to explore the effects of bolt failure on the interaction of the intra- and inter-ply damages. The predicted load-displacement response was compared with experimental measurement, and the simulated failure patterns were compared with the XCT images. A new design was proposed using the enhanced analysis tool to achieve a dominant bearing failure mechanism on the basis of the current SSB test.

While X-ray computed tomography (XCT) is pushed further into the micro- and nanoscale, the limitations of various tool components and object motion become more apparent. For high-resolution XCT, it is necessary but practically difficult to align these tool components with sub-micron precision. The aim is to develop a novel reconstruction methodology that considers unavoidable misalignment and object motion during the data acquisition in order to obtain high-quality three-dimensional images and that is applicable for data recovery from incomplete datasets. A reconstruction software empowered by sophisticated correction modules that autonomously estimates and compensates artefacts using gradient descent and deep learning algorithms has been developed and applied. For motion estimation, a novel computer vision methodology coupled with a deep convolutional neural network approach provides estimates for the object motion by tracking features throughout the adjacent projections. The model is trained using the forward projections of simulated phantoms that consist of several simple geometrical features such as sphere, triangle and rectangular. The feature maps extracted by a neural network are used to detect and to classify features done by a support vector machine. For missing data recovery, a novel deep convolutional neural network is used to infer high-quality reconstruction data from incomplete sets of projections. The forward and back projections of simulated geometric shapes from a range of angular ranges are used to train the model. The model is able to learn the angular dependency based on a limited angle coverage and to propose a new set of projections to suppress artefacts. High-quality three-dimensional images demonstrate that it is possible to effectively suppress artefacts caused by thermomechanical instability of tool components and objects resulting in motion, by center of rotation misalignment and by inaccuracy in the detector position without additional computational efforts. Data recovery from incomplete sets of projections result in directly corrected projections instead of suppressing artefacts in the final reconstructed images. The proposed methodology has been proven and is demonstrated for a ball bearing sample. The reconstruction results are compared to prior corrections and benchmarked with a commercially available reconstruction software. Compared to conventional approaches in XCT imaging and data analysis, the proposed methodology for the generation of high-quality three-dimensional X-ray images is fully autonomous. The methodology presented here has been proven for high-resolution micro-XCT and nano-XCT, however, is applicable for all length scales.

Liquid sprays play a key role in many engineering processes (e.g., food processing, coating and painting, 3D printing, fire suppression, agricultural production, combustion systems, etc.). Spray characteristics can easily be assessed in the mid- and far-field regions, well after liquid sheet breakup and droplet formation, using various optical/laser diagnostic techniques. The conditions in the near-field region, near the spray nozzle exit, can influence mid- and far-field characteristics; however, near-field measurements are extremely challenging because the spray in this region is typically optically dense and optical/laser diagnostics are ineffective in this region. This study presents the advantages and challenges of using broadband X-rays to characterize the near-field region of a spray. A generic spiral jet hollow cone spray nozzle is used to demonstrate near-field spray imaging for three different flow conditions. Radiographic movies of the spray show a very dynamic near-field region. X-ray computed tomography (CT) reconstructions of the spray display a 3D map of the time-average spray distribution. The two X-ray imaging modes provide qualitative information of the near-field spray region. A discussion of the quantitative measures that can be acquired from X-ray radiographs and CT images is provided.

The X-ray CT technology previously developed by JAEA was upgraded. The shape of the X-ray source beam was changed from a circular shape to an elliptical one and the collimator slit width was decreased from 0.3 to 0.1 mm. The X-ray detector was improved by changing a CdWO4 scintillator to a highly sensitive silicon semiconductor detector. The analysis code of X-ray CT image was revised with respect to the number of points by using two kinds of experimental results and taking into account the effects of crack existence and deviation of the central void position from the radial center of a fuel pellet. As a result, high resolution X-ray CT images could be obtained on the transverse cross section of irradiated fuel assemblies. The error of the dimensional measurement was improved from ±0.1 to ±0.03 mm by upgrading the instrument and revising the analysis code of X-ray CT image. The discriminating accuracy of density difference could be increased, and the low density region (undisturbed region) and high density region (equi-axial and columnar regions) in the X-ray CT image on the cross section of irradiated fuel could be discriminated from each other. The reliability of fuel performance analysis improves because a large number of PIE data can be collected, compared with the conventional destructive PIE.

Soil aggregate stability (AS) and organic matter (OM) are key indicators of soil physical quality and ecosystem functioning, yet their long-term responses to contrasting land use systems under varying management intensities remain insufficiently quantified. This study evaluated the effects of long-term land use on AS and OM under natural ecosystem–forest land (NEFL), horticultural land (HL), and arable land (AL). Soil samples were collected from multiple representative sites within each land use type, and differences in AS and OM were statistically assessed to elucidate land use–induced structural changes. The results showed that land use type significantly influenced both AS and OM. Soils under NEFL consistently exhibited the highest AS and OM values, reflecting the stabilizing effects of continuous vegetation cover and minimal soil disturbance. Compared to NEFL, AS and OM decreased markedly by 31.6% and 42.9% in HL and by 54.4% and 67.0% in AL, respectively, indicating a progressive decline in soil structural quality with increasing land use intensity. Horticultural lands displayed intermediate values, suggesting a partial preservation of soil structure under perennial systems, whereas arable lands showed the lowest AS and OM values, consistent with the cumulative effects of frequent soil disturbance, reduced residue retention, and accelerated organic matter mineralization. Overall, the findings demonstrate that long-term soil structural stability is governed not only by land use type but also by management intensity and organic matter input pathways. Strategies that promote residue retention and minimize disturbance are essential to sustain soil structure and prevent physical degradation in agricultural systems.

Defects shape, volume, and orientation all have a direct impact on the mechanical properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to evaluate and analyze the three-dimensional (3D) geometrical characteristics of these defects. X-ray Computed Tomography (XCT) can reveal an object's internal structure by volumetric scanning through its building direction. Point clouds are 3D data that can be extracted from the stack of XCT images taken from a part to perform further analysis. This study presents a novel approach for 3D segmentation and geometrical analysis of L-PBF defect structures from XCT images. The proposed method integrates Voronoi labeling and 3D point cloud reconstruction to reveal individual defect characteristics from the XCT image stack of a part. A case study showed the proposed methodology's effectiveness in identifying and characterizing defect regions in L-PBF-ed Cobalt-Chrome (CoCr) parts.