Industrial Computed Tomography Research Articles

Solid waste-based low-carbon autoclaved aerated concrete: Relationship between engineering properties and microstructure

A novel low-carbon autoclaved aerated concrete (AAC) was developed to enhance the high valuable use of solid waste in production, incorporating furnace slag, metro shield sand, and quartz tailings sand as 100 % siliceous raw materials, constituting 60 % of the total weight. In this study, comprehensive analyses were conducted on its engineering properties, including density, compressive, flexural, and splitting tensile strengths, and thermal conductivity. Advanced techniques such as X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and high-resolution Industrial CT scanner (X-CT) were employed to examine AAC's material composition, hydration products, and pore structure characteristics at the microscale. The results indicated that the addition of 10 % furnace slag resulted in a specific increase of 5.5 % in compressive strength. However, escalating the furnace slag content to 30 % proved detrimental to the toughness and dry shrinkage of AAC. The microstructure analysis indicates that the use of furnace slag can improve the connectivity and roundness of pores and reduce the probability of large pores (>0.01 mm3), which will reduce the risk of damage localization. Furthermore, furnace slag in AAC enhanced sustainability and cost-effectiveness compared to metro shield sand and quartz tailings sand. Based on the TOPSIS model, an optimal AAC mixture, considering engineering performance, economy, and sustainability, was derived by blending the three types of solid wastes.

Characterization of road surface topography changes during polishing process with characteristic roughness parameter

In this paper, three-dimensional structure feature points of the asphalt mixture were obtained based on the industrial CT. The surface morphology of the asphalt mixture was then extracted through image morphology processing and evolutionary matching algorithms. A series of accelerated polishing tests were utilized to simulate the long-term polishing effect of the tire load on the asphalt pavement. Combining with digital image analysis, the full-life morphology evolution trend of the asphalt pavement was analyzed, and the full-life surface morphology datasets of the asphalt pavement were constructed. By comparing the power spectrum density of the asphalt pavement morphology at different wear stages, a segmented evaluation method based on the spectral fractal method was proposed. Combining the spectral fractal parameters and statistical geometric parameters, a multi-scale asphalt pavement surface morphology feature evaluation index was proposed. This provides a more intricate and detailed analysis of the surface structure of asphalt mixtures.

Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework

Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction.

Semi-supervised segmentation of metal-artifact contaminated industrial CT images using improved CycleGAN.

Accurate segmentation of industrial CT images is of great significance in industrial fields such as quality inspection and defect analysis. However, reconstruction of industrial CT images often suffers from typical metal artifacts caused by factors like beam hardening, scattering, statistical noise, and partial volume effects. Traditional segmentation methods are difficult to achieve precise segmentation of CT images mainly due to the presence of these metal artifacts. Furthermore, acquiring paired CT image data required by fully supervised networks proves to be extremely challenging. To address these issues, this paper introduces an improved CycleGAN approach for achieving semi-supervised segmentation of industrial CT images. This method not only eliminates the need for removing metal artifacts and noise, but also enables the direct conversion of metal artifact-contaminated images into segmented images without the requirement of paired data. The average values of quantitative assessment of image segmentation performance can reach 0.96645 for Dice Similarity Coefficient(Dice) and 0.93718 for Intersection over Union(IoU). In comparison to traditional segmentation methods, it presents significant improvements in both quantitative metrics and visual quality, provides valuable insights for further research.

Correcting Hardening Artifacts of Aero-Engine Blades with an Iterative Linear Fitting Technique Framework.

Aero engines are the key power source for aerospace vehicles. Cermet turbine blades are the guarantee for the new-generation fighters to improve aero-engine overall performance. X-ray non-destructive reconstruction can obtain the internal structure and morphology of cermet turbine blades. However, the beam hardening effect causes artifacts in objects and affects the reconstruction quality, which is an issue that needs to be solved urgently. This study proposes a hardening-correction framework for industrial computed tomography (ICT) images based on iterative linear fitting. First, an iterative binarization was performed to improve the penetration length accuracy of the forward projection. Then, the proposed linear fitting technology combined with the Hermite function model is derived and analyzed to obtain suitable parameters of blade data. Finally, the fitting curves of the blade data, using the proposed method and the traditional polynomial fitting method, were analyzed and compared and were used to correct the engine turbine blade projection data to reconstruct different groups of tomographic images. Different groups of tomographic images were analyzed using three quantitative image quality evaluation indicators. The results show that the root-mean-square error (RMSE) of the tomographic image obtained by the proposed framework is 0.0133, which is lower than that of the compared method. The peak signal-to-noise ratio (PSNR) is 37.7050 dB and the feature structural similarity (FSIM) is 0.9881, which are both higher than that of the compared method. The proposed method improves the hardening-artifact-correction capability and can obtain higher-quality images, which provides new ideas for the development of imaging and detection of new-generation aero-engine turbine blades.

Characterization on the mesostructural properties of asphalt mixtures based on meso-element equivalence method

The randomness and variation of material composition at the meso-scale lead to some limitations in the application of asphalt mixture. When all features are taken into account, the computational effort increases significantly. Therefore, the relationship can be established using the meso-element equivalence method (MEEM). In this paper, the effects of aggregate-asphalt interface, initial void defects and material damage were introduced and MEEM of asphalt mixture was established based on industrial computed tomography (ICT) and finite element method (FEM). The rationality of the model was verified by laboratory performance tests, and the effects of aggregate type, mixture gradation and mesostructural parameters on the mechanical response of MEEM were compared and analyzed. The results showed that the MEEM model captures the mechanical behavior of asphalt mixtures well, the input of visco-elastic material parameters is an important step of MEEM simulation accuracy. The aggregate type, the discrete degree of the skeleton structure and the distribution characteristics of mesostructural parameters are important factors affecting the mechanical properties of asphalt mixture in MEEM.

A Fractal-Based Quantitative Method for the Study of Fracture Evolution of Coal under Different Confining Pressures

To study the dynamic crack evolution process of loaded coal from the perspective of fractals, we carried out in situ industrial CT scanning tests of loaded coal under different confining pressures, visualizing loaded coal fracturing. Combined with fractal theory, the temporal and spatial evolution law of coal cracks is described quantitatively. The results provide two findings: (1) from the perspective of two-dimensional images and three-dimensional space, the evolution characteristics of cracks in coal under different confining pressures were basically the same in each loading stage. During the loading stages, the cracks exhibited a change rule of a slow reduction, initiation/development, rapid increase, expansion, and penetration. (2) The fractal dimension of coal was calculated by introducing fractal theory, and its change law was in good agreement with the dynamic changes of the cracks, which can explain the influence of the confining pressure on the loaded coal. The fractal dimension showed three stages: a slight decrease, a stable increase, and then a significant increase. The larger the confining pressure, the more obvious the limiting effect. Thus, our approach provides a more accurate method for evaluating the spatial and temporal evolution of cracks in loaded coal. This study can be used to predict the instability failure of loaded coal samples.

A level-set method with the 3D multiplicative-additive model for CT volume data segmentation

3D industrial computed tomography (ICT) technology plays a vital role in non-destructive testing and evaluation of complex components. However, CT images often suffer from artifacts and noise because the CT imaging system is non-ideal. To achieve high-precision segmentation of ICT volume data, a level set (LS) method with a 3D multiplicative-additive (MA) model was proposed. First, according to the cluster property of local intensity and the 3D-MA image model, we developed a local energy. The global energy was established by integrating the local energy. Next, the LS function was embedded into the global energy to form a variational LS energy functional. Finally, the proposed energy functional was minimized to achieve segmentation. The experimental results demonstrate that compared with the state-of-the-art models our model has superior accuracy. In particular, the measurement results of the forest ball also show that the proposed method excels in geometric measurement based on CT images.

CT image reconstruction via industrial CT fast scanning

In automated manufacturing and safety inspection, there is a high demand for fast computed tomography (CT) scanning and image reconstruction. Currently, faster scanning can be achieved by reducing the X-ray exposure time within sparse view CT. The faster scanning strategy introduces significant streak artefacts and noise during the sampling process. Consequently, streak artefacts and noise need to be simultaneously suppressed, which is poses a challenge for existing reconstruction methods. This paper presents a fast iterative reconstruction algorithm that can simultaneously suppress both streak artefacts and noise. This method can not only reconstruct high-fidelity images from rapidly acquired projection data, but also has a faster reconstruction speed than the existing iterative reconstruction algorithms. First, we present a high-order multi-directional total variation (HOM-TV) method that specifically focuses on preserving edge details of the image. Then, we present a fast iterative reconstruction model by incorporating HOM-TV and non-local means into the objective function. Finally, the effectiveness of the presented reconstruction model is validated by simulation and real experiments. The faster scanning method can complete the scan in only 5 seconds, and the structural similarity index (SSIM) of the CT image reconstructed by our method is 0.9755, which is higher than 0.0175 of the Fast Null Space Reconstruction (FNSR) algorithm. The peak signal-to-noise ratio (PSNR) index is 1.656, which is higher than that of the contrast algorithm. In terms of reconstruction time, our algorithm can achieve reconstruction in as little as 36 seconds, outperforming the baseline algorithms.

Examination of ray casting in Unity 3D as a fast pose prediction tool for industrial CT scans of multi-material specimens

Examination of ray casting in Unity 3D as a fast pose prediction tool for industrial CT scans of multi-material specimens

Oxidation mechanism of Al–Si–SiC composite at elevated temperature

Resin-bonded Al–Si–SiC composite was sintered in the air, and the oxidation mechanism was investigated utilizing industrial CT, XRD, and SEM, combined with thermodynamic calculations, as well as a reaction model, was established. The generation of Al4C3 is avoided by constructing the Al-Sialloy(l) intermediate phase. Simultaneously, AlN exists as a solid solution, which improves the stability of the composite. With temperature increasing, the antioxidant properties of the Al–Si–SiC composite are enhanced by forming a dense layer, and the oxidation mechanism varies at different temperatures. At 1100 °C, the outer layer of Al and Si oxidized to generate Al2O3 and SiO2, respectively, while the inner layer generated irregular granular AlN–SiC solid solution; At 1300 °C, the outer pores were filled densely with Al2O3 and SiO2, while the high temperature and low Po2/Pθ induced active oxidation of SiC in the inner layer so that Po2/Pθ was further reduced and the AlN–SiC solid solution particles grew up; At 1500 °C, Al-Sialloy(l) rapidly seals the outer pore channels, accompanied by the in-situ formation of mullite and dense layers, where only N2(g) is free to diffuse inward, followed by the continuous dissolution of C and N in a form of atoms in the Al-Sialloy(l) to generate hexagonal granular AlN–SiC solid solution.

Manufacturing errors detection and visualization system for parts with complex internal cavity structural through industrial CT image

To address the challenge of detection and visualization manufacturing errors in part with complex internal cavity structures, this paper present a method based on industrial computed tomography (CT) image. First, a two-dimensional (2D) CT sequence slice images are obtained using fan-beam high energy industrial CT scanning, which is then reconstructed into a three-dimensional (3D) measurement model. The measurement 3D model is registered with the original design model, and the surface information of the part is obtained by segmenting the surfaces. The error between reference surface of original design model and test surface of measurement model is calculated. By comparing the error value with the tolerance, the detection result is obtained and analyzed. The developed software system enables areas that do not meet the tolerance requirements to be visualized. This method can detect manufacturing errors for part with complex internal cavity structures, such as planeness error, cylindricity error, verticality error, and parallelism error. This method provides a basis for assessing the manufacturing and processing quality of part with complex internal cavity structures.

Study on the permeability behaviour of hydrogen doped natural gas in polyethylene pipeline

The use of non-metallic PE pipes to transport hydrogen energy can avoid the problem of hydrogen embrittlement in metal pipes, but due to material characteristics, PE pipes exhibit gas leakage behavior. Therefore, this article conducted a study on the permeability of polyethylene (PE100) as a gas pipeline material for hydrogen-doped gas, mainly analyzing the effects of hydrogen content and gas pressure on the permeability of PE100 gas. We ensure that the raw materials are uniform and free from pore defects through microstructure observation and industrial CT characterization. The experimental results show that with the increase of hydrogen content in the hydrogen-doped gas, the gas permeability coefficient of the sample significantly increases. When the hydrogen content is 60%, the gas permeability coefficient increases by 83% compared to when no hydrogen is added. Further increase in hydrogen content results in no significant change in the gas permeability coefficient; As the gas pressure increases, the gas permeability coefficient of the s ample gradually decreases. This study will provide data support for the safe transportation of hydrogen energy and promote the development of the hydrogen energy industry.

Tibia-YOLO: An AssistedDetection System Combined with Industrial CT Equipment for Leg Diseases in Broilers

With the continuous improvement of broiler production performance, the frequent occurrence of leg problems has caused serious economic losses in many factories. In order to more efficiently detect and prevent broiler leg diseases, we propose an auxiliary detection system for broiler leg diseases based on deep learning. The system is divided into two parts. First, a digital radiography (DR) image of a broiler is taken through industrial computed tomography (CT), and then the improved deep-learning network Tibia-YOLO is used to detect the tibia; the detected results are then extracted and measured. Our improved Tibia-YOLO network uses the Content-Aware ReAssembly of Features (CARAFE) upsampling operator to avoid checkerboard artifacts and increase the generalization capabilities. Efficient multi-scale attention (EMA) and parallel network attention (ParNet) were added to the Tibia dataset at multiple scales (COCO2016), and there were improvements when testing on the three VOC2012 datasets. The mean average precision of tibia detection reached 90.8%, and the root mean square error (RMSE) for the tibia length was 3.37 mm.

Industrial CT image reconstruction for faster scanning through U-Net++ with hybrid attention and loss function

ABSTRACT To address this issue of streak artifacts and noise caused by faster scanning in this paper, this paper presents a prunable U-NET++ with hybrid attention and loss function (HAL-UNET++) deep learning network, which is a U-shaped framework-based network for industrial CT image reconstruction. In this reconstruction network, the prior knowledge that CT images are sparse in the gradient domain is utilized, and a hybrid loss function is designed to combine the L0 norm of gradient image and the image signal-to-noise ratio. This ensures that the network’s learning process places greater emphasis on the noise content and sparsity of the image gradient domain. Subsequently, an adaptable convolutional block attention module has been incorporated into the shallow network layers to ensure the preservation of finer details in the CT images that are reconstructed. Finally, experimental objects comprising cables and rock samples with internal defects were used. The results indicate that the present reconstruction network, through pruning techniques, achieves rapid image reconstruction. Under the constraint of maintaining reconstruction accuracy, the pruned network demonstrates a reduction of approximately fourfold in network parameter volume compared to the unpruned network, resulting in a time saving of 0.24 seconds for the reconstruction of a single CT image.

NeXtResUNet: A neural network for industrial CT image denoising

Image denoising is a critical issue in industrial computed tomography (CT) inspection. Most existing noise reduction algorithms are based on synthetic data, resulting in the loss of fine details, local region distortions, and other problems that arise during application in practical scenarios. As such, a fusion network, combining ConvNeXt, ResNet, and UNet is proposed in this study to address these issues. This algorithm was applied to a self-constructed industrial CT image denoising dataset, achieving a peak signal-to-noise ratio (PSNR) of 47.20 dB, which is 6% (44.48 dB) higher than that of the benchmark DnCnn. This also represents a 0.5% improvement over the current state-of-the-art (SOTA) SCUNet (46.94 dB). More importantly, the proposed network is superior to other existing models in terms of the overall visual effects and degree of image detail preservation. The network structure is also concise yet simple to expand. As such, it can be applied not only to image denoising, but also to CT image segmentation, super resolution, and related tasks.

Analysis of the off-focal source in transmission geometry X-ray systems

For X-ray tubes in lab based computed micro-tomography systems, off-focal X rays are those emitted from the source but not produced at the primary focal spot of the electron beam. They can be attributed to both electron and photon scatter within the X-ray tube. Off-focal X rays can represent a significant fraction of the total X-ray flux and lead to artefacts in the radiographs, and reconstructed tomographic volumes, degrading contrast and resolution. Therefore, they are an undesirable feature in industrial and medical X-ray CT scanners. In this work, a general model of an X-ray tube with a transmission target has been developed in TOPAS, a Monte Carlo (MC) simulation extension to GEANT4. MC simulations enable: (1) the analysis of the origin of various types of off-focal X rays; (2) quantification of the prevalence of each type for a specific tube geometry and (3) replication and understanding of experimental results affected by off-focal X rays. Our MC analysis herein shows that the amount of off-focal X rays can represent up to 25% of total X-ray flux. The most prevalent off-focal X rays were found to be the X rays created by the electrons back-scattered from the target into the aperture and generating X rays.

Accurate reconstruction of multiple basis images directly from dual-energy data in CT.

We develop optimization-based algorithms to accurately reconstruct multiple ( 2) basis images directly from dual-energy (DE) data in CT. In medical and industrial CT imaging, some basis materials such as bone, metals, and contrast agents of interest are confined often spatially within regions in the image. Exploiting this observation, we develop an optimization-based algorithm to reconstruct, directly from DE data, basis-region images from which multiple ( 2) basis images and virtual monochromatic images (VMIs) can be obtained over the entire image array. We conduct experimental studies using simulated and real DE data in CT, and evaluate basis images and VMIs obtained in terms of visual inspection and quantitative metrics. The study results reveal that the algorithm developed can accurately and robustly reconstruct multiple ( 2) basis images directly from DE data. The developed algorithm can yield accurate multiple ( 2) basis images, VMIs, and physical quantities of interest from DE data in CT. The work may provide insights into the development of practical procedures for reconstructing multiple basis images, VMIs, and physical quantities from DE data in applications. The work can be extended to reconstruct multiple basis images in multi-spectral CT or/and photon-counting CT.

Surface Evaluation of Gyroid Structures for Manufacturing Rubber-Textile Conveyor Belt Carcasses Using Micro-CT.

Gyroid structures are among the most widely used three-dimensional elements produced by various additive manufacturing technologies. This paper focuses on a metrological analysis of Flexfill 92A material specimens with a relative density (25 to 85%) using industrial computer tomography. The results show that for a given structure, the best method is to use surface determination with the closure of internal defects in the material. The analysis implies that the smallest deviations of the specimens' external dimensions were achieved with respect to the CAD model at the highest relative densities. The wall thickness shows the smallest percentage change of 0.5685 at 45% relative density and the largest at 25% and 85% relative density. The nominal-actual comparison of manufactured specimens to the CAD model shows the smallest cumulative deviation of 0.209 mm at 90% and 25% relative density, while it slightly increases with increasing relative density. All produced specimens have a smaller material volume than their theoretical volume value, while the percentage change in volume is up to 8.6%. The surface of specimens is larger compared with the theoretical values and the percentage change reaches up to 25.3%. The percentage of pores in the specimens increases with increasing relative density and reaches 6%. The acquired knowledge will be applied in the framework of research focused on the possibilities of using additive manufacturing to produce a skeleton of rubber-textile conveyor belts. This paper presents initial research on the possibility of replacing the carcass of rubber-textile belts with an additive technology use.

Evolution of pore structure of microcapsule self-healing concrete during ultrasonic triggering

To investigate the microstructure of self-healing concrete before and after ultrasonic triggering, mercury intrusion porosimetry (MIP) test and industrial CT technology were applied to analyze the pore structure of self-healing concrete. The results show that ultrasonic triggering, on one hand, can improve the triggering of microcapsules, reducing less harmful pores, harmful pores and extremely harmful pores. On the other hand, excessive ultrasonic triggering will slightly damage the microstructure of self-healing concrete. The total porosities of self-healing concrete with 6 % microcapsules after ultrasonic and mechanical triggering were 8.6 % and 15.9 %, respectively. The simulation results of porosity are consistent with the MIP results (8.7 % and 15.6 %, respectively). The gray values of microcapsules and the cement matrix are 55 and 126, respectively. Finally, based on the CT reconstruction simulation, the substrate fluid velocity after ultrasonic triggering is lower than that triggered by mechanics.