Search

X-ray computed tomography (CT) provides a three-dimensional (3D) volume image of the scanned object and can be applied for nondestructive inspection of industrial materials. The object surface extracted from the X-ray CT volume is examined by comparing it with the pre-obtained computer-aided design (CAD) mesh model of the scanned object. However, industrial materials often contain heavy metals that significantly attenuate X-rays and cause CT artifacts in CT images, preventing an accurate shape extraction. This study proposes a method that extracts the geometric features of two-dimensional (2D) X-ray projection images and matches them with pre-obtained 3D CAD meshes. The proposed method is based on the principle that most of the geometric features of the object, such as edges, in the X-ray 2D projection images are captured and can be directly compared with CAD meshes. We first extract the features in each of the 2D projection images as X-ray feature projections. Subsequently, CAD mesh contours are projected onto 2D mesh feature projections and matched with the X-ray feature projections. Finally, 3D feature shapes of the CAD mesh are obtained by back-projecting the matching results. The proposed method requires only a small number of projection images, thus reducing computational time while maintaining shape accuracy. • A new shape inspection method which use X-ray projections is proposed. • The proposed method extracts and evaluate shape features of the CAD model. • The accuracy of the method is analyzed by simulations and real X-ray scanning data. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to reduce CT (computed tomography) artifacts by using multiple X-ray CT scanning in which CT data fusion is combined with optimized selection of a combination of scan angles. Industrial X-ray CT has been widely used for investigating industrial parts including metals, which cause various CT artifacts. These CT artifacts reduce the quality of X-ray CT images, and prevent precise measurements. Our multiple X-ray CT scanning approach focuses on CT artifacts distribution estimation, which including scan angle combination optimization, multiple CT volume registration, and CT volume fusion. To demonstrate our method, we selected multiple optimal scan angles and conducted volume fusion on industrial parts included metals and resins. Our approach effectively reduced CT artifacts. Our experiments also showed that additional scans successively reduced metal artifacts in various regions, suggesting that our scan angle selection method successfully selected an optimal combination of scan angles. ∙ This paper proposes metal artifacts reduction method using multiple X-ray CT scanning. ∙CT artifacts distribution estimation enable us to find optimal combination of scan angles. ∙Multiple CT volumes are used to obtain optimal CT volumes. ∙Pros, and cons, of the method are presented and discussed. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (CT) enables nondestructive evaluation and dimensional metrology using the reconstructed volumes of measured objects. Although the abundant applications of industrial X-ray CT exist, a high-quality reconstructed volume requires a long measurement time owing to sharp transmission images from dense views. Intensive X-rays with the exposure kept can be used to reduce the measurement time. However, the quality of the measured objects inevitably deteriorates as a result of the so-called penumbra effect caused by large focal spots in X-ray sources. This paper proposes rotational fine-tuning (RFT) for the acceleration of CT scanning with the quality kept. First, sharp transmission images from sparse views are obtained in appropriate imaging conditions. Subsequently, blurry transmission images from dense views are obtained using a larger focal spot size and a shorter exposure time. The acquired blurry images are deblurred by convolutional neural networks fine-tuned using several pairs of sharp and blurry images obtained at the corresponding projection angles, and linear interpolation integrates the deblurred images to generate the final RFT output. The proposed method indirectly reduces the measurement time because no lengthy acquisition time is required, and fine-tuning and blurring are rapid when using GPUs. In the experiments, the comparison of PSNRs between blurry and deblurred reconstructed volumes is shown. To reveal the effect of the proposed method on dimensional metrology, the improvement in the surface deviations of the stepped cylinder is also shown. Finally, the improvement of the pore spaces in a porous aluminum is shown to assess the aspect of nondestructive evaluation. Overall, experimental results demonstrate that the proposed method can perform the acceleration of X-ray CT scanning with the quality kept. • A new approach for the acceleration of high-quality CT scanning using machine learning. • Deblur the blurry X-ray transmission images with convolutional neural networks. • Empirical validation of the applicability of the proposed method for various materials. [ABSTRACT FROM AUTHOR]

Abstract The analysis of existing objects for developing new products is called reverse engineering and is a common process in industrial manufacturing. X-ray CT scanning is a powerful reverse-engineering tool that can acquire the entire geometry of an assembled object, including its inner structure, without disassembly. Before X-ray CT scanning can be used to accurately extract the parts of an assembled object from its CT volume, several challenges remain: CT artifacts, the non-uniqueness of the CT values, and limited resolution. Currently, an operator usually manually segments the target parts of the CT volume at voxel-level accuracy, which takes a long time. Hence, this paper proposes SegMo, a segmentation system which can perform segmentation of an assembled object to sub-voxel accuracy more quickly and with less effort than conventional methods. These advantages are achieved by suggesting the part boundary candidates that are coded as a hierarchical decomposition of the CT volume based on a Morse complex and polygonization at sub-voxel accuracy. SegMo consists of three steps: the generation of a hierarchical decomposition of a CT volume, segmentation using this hierarchy, and polygonization of the segmentation result. In this paper, we demonstrate how SegMo can achieve accurate and efficient segmentation on CT volumes of several complex assembled objects. Highlights • Practical segmentation system for X-ray CT volumes of assembles is proposed. • Accurate surface mesh of each segmented part can be obtained. • GUI assisting the efficiency of manual operation is proposed. • Examples of segmentation with CT volumes of complex object are shown. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel interpolation scheme for surface extraction from volume fraction data stored on an octree. Here the volume fraction data is a set of octants each of which stores the volume ratio of the object included in the octant. Based on marching cubes on the dual grid of an octree, we modified the computation of the mesh vertex positions to be more appropriate for volume fraction data. The key point of the proposed interpolation is that we approximate the shapes of the octants as spheres rather than as cubes. This approximation has a very simple computation and can improve the accuracy of the vertex positions. We demonstrate the effectiveness of the algorithm using octree-compressed CT volumes that can be treated as volume fraction data. [ABSTRACT FROM AUTHOR]

Fiber composite materials have unique, advantageous mechanical properties that have made them highly desirable in a range of industries. In particular, 3D woven-fiber composites are highly resistant to delamination compared with laminated 2D woven-fiber composites and have been adopted in various advanced products. This paper focuses on the design of 3D woven-fiber composite products and proposes a flattening simulation method for designed 3D models with constant thickness. The proposed method estimates the shape of a flat material and the fiber directions in the 3D model design; deformation phenomena of 3D woven-fiber materials are also considered in order to improve the accuracy of the proposed method. CT images are used to compare the simulation results with the actual deformation of 3D woven-fiber materials and confirm the ability of our method to effectively design the fiber direction base on the 3D model and to estimate the shape of flat materials. [ABSTRACT FROM AUTHOR]

Inspired by computed tomography (CT), this paper presents a novel surface reconstruction algorithm, tomographic surface reconstruction , to reconstruct a surface mesh from a point cloud equipped with oriented normals. In the process of scanning a real object using an X-ray CT system, it generates a sinogram consisting of projection images that are maps of X-ray transmission lengths, and then, a tomogram (CT volume) is reconstructed from the sinogram. A hole-free surface mesh is then easily obtained by polygonizing an isosurface. To adopt this CT paradigm to surface reconstruction from a point cloud, only a scheme to generate a sinogram from a point cloud is required. The value of a sinogram for surface reconstruction can be defined as the sum of the distances between the intersecting points of a ray and the underlying surface, which are defined as the maxima of the point density. While ordinary CT scanning uses projection directions which share a single rotation axis, tomographic surface reconstruction adopts randomly selected projection directions and successfully improved the reconstruction robustness. By applying an iterative CT reconstruction to the sinogram, the algorithm generates a tomogram whose boundary between the foreground and background approximates the surface of the object. The effectiveness for a point cloud with a lack of sampling and outliers is demonstrated from experimental results. [ABSTRACT FROM AUTHOR]

In industrial manufacturing, reverse engineering, which can be applied to analyze or re-design, is an essential process, whereby scanned data of real objects are used to generate a CAD model. CAD data have various features such as fillets; therefore, robust and versatile reverse engineering is still difficult. Particularly, an adequate segmentation from real scanned data is challenging, and it is a major obstacle for reverse engineering. In this study, we propose an innovative segmentation from CT scanned data, wherein user-friendly segmentation is achieved by directly computing the curvature gradient from a CT sinogram. We used the proposed method to patch a B-spline surface onto each segmented region. The experimental results show that the proposed method offers robust, versatile, and user-friendly reverse engineering that is faster than conventional manual modeling. In addition, as it is supported by X-ray CT simulation, the proposed method can be applied to various surface mesh data without requiring any actual X-ray CT equipment. • Robust curvature gradients are computed directly from CT sinogram. • Flexible and powerful segmentation is performed using multi-level Morse complex. • B-spline surfaces are patched onto each segmented region as a demonstration. • X-ray CT simulations are conducted and used for the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: We introduce a method for segmentation of materials segmented in volumetric models of mechanical parts created by X-ray CT scanning for the purpose of generating their boundary surfaces. When the volumetric model is composed of two materials, one for the object and the other for the background (Air), these boundary surfaces can be extracted as isosurfaces using a surface contouring method. For a volumetric model composed of more than two materials, we need to classify the voxel types into segments by material and then use a surface contouring method that can deal with both CT values and material types. Here we propose a method for precisely classifying the volumetric model into its component materials using a modified and combined method of two well-known algorithms in image segmentation, region growing and Graph-cut. We then apply our non-manifold iso-contouring method to generate triangulated mesh surfaces. In addition, we demonstrate the effectiveness of our method by constructing high-quality triangular mesh models of the segmented parts. [Copyright &y& Elsevier]

Augmented reality (AR) technology is a promising tool for field service applications such as assembly. AR can show computer-aided design (CAD) models of assembly parts at installation locations. This provides a means for delivering assembly instructions to an operator. Furthermore, evaluating the assembly process can be beneficial for preventing assembly errors. Field service engineers who perform assembly operations may choose to use a head-mounted display (HMD) for its good portability and hands-free operation. In this study, we propose a method for efficiently evaluating the misalignment between real and virtual objects, which is a mismatch between the two objects' poses, in an HMD-based AR assembly assistance system. By using a depth camera in the HMD, we evaluate the misalignment by comparing the depth maps of the real and virtual worlds. With our preliminary prototype, the system can evaluate misalignment with an accuracy of ±1 cm at a rate of 30 fps in real time. Moreover, we also propose a coordinate calibration method and establish a basic HMD-based AR assembly assistance system to demonstrate the methods. The efficient evaluation of misalignment could help to monitor operations to prevent assembly errors and decrease operation time. [ABSTRACT FROM AUTHOR]

Measurement quality of industrial cone beam X-ray computed tomography (CT) is influenced by many types of artefacts, therefore, industrial-level accuracy is difficult to attain. In order to avoid the influences of these artefacts, this paper proposes a new method for measuring cylindrical surface that is a common geometric structure in mechanical parts. The proposed method fits cylindrical surface from a sinogram image directly. Compared with a standard way for dimensional CT metrology, higher measurement accuracy and less measurement time can be achieved. Mainly, the method consists of edge points detection of a sinogram image and cylindrical surface fitting. The performance of the method is evaluated by use of both simulation data and actual data. [ABSTRACT FROM AUTHOR]

Abstract: Thanks to recent improvements, computational methods can now be used to convert triangular meshes into quadrilateral meshes so that the quadrilateral elements capture well the principal curvature directional fields of surfaces and intrinsically have surface parametric values. In this study, a quadrilateral mesh generated using the mixed integer quadrangulation technique of Bommes et al. is used for input. We first segment a quadrilateral mesh into four-sided patches. The feature curves inside these patches are then detected and are constrained to act as the patch boundaries. Finally, the patch configuration is improved to generate large patches. The proposed method produces bi-monotone patches, which are appropriate for use in reverse engineering to capture the surface details of an object. A shape control parameter that can be adjusted by the user during the patch generation process is also provided to support the creation of patches with good bi-monotone shapes. This study mainly targets shape models of mechanical parts consisting of major smooth surfaces with feature curves between them. [Copyright &y& Elsevier]

Abstract: Today’s quad-meshing techniques generate high-quality quadrilateral meshes whose extraordinary vertices (i.e., not four-valence vertices except on the boundary) are generally located in highly curved regions. The motorcycle graph (MCG) algorithm of Eppstein et al. can be used to generate structured partitions of such quadrilateral meshes. However, it is not always possible for it to capture feature curves in the highly-curved parts of the model on the partition boundaries because model geometry is not taken into account. This study investigated feature-aware algorithms representing extensions of the MCG algorithm. Initial partitioning is first performed using a speed control algorithm identical to the MCG algorithm except that it assigns variable rather than constant speed to particles. Partition boundaries are then improved via local path flipping operations. The MCG algorithm and the speed control algorithm are intended to trace as many feature curves as possible, but do not necessarily trace all of them. For this reason, feature curves are extracted and integrated into the proposed framework by adding seeds located at ordinary vertices in addition to extraordinary seeds. The proposed algorithm generates partitions that are still structured, and has been tested with quadrilateral mesh models generated using the mixed integer quadrangulation technique of Bommes et al. [Copyright &y& Elsevier]