X-ray Computed Laminography (CL) is a well-known computed tomography technique to image the internal structure of flat objects. High-quality CL imaging requires, however, a large number of X-ray projections, resulting in long acquisition times. Reducing the number of acquired projections allows to speed up the acquisition process but decreases the quality of the reconstructed images. In this work, we investigate the use of Convolutional Neural Networks for processing volumes reconstructed from only four X-ray projections acquired at an inline CL scanning setup.

Abstract We present visual analysis methods for the evaluation of tomographic fiber reconstruction algorithms by means of analysis, visual debugging and comparison of reconstructed fibers in materials science. The methods are integrated in a tool (FIAKER) that supports the entire workflow. It enables the analysis of various fiber reconstruction algorithms, of differently parameterized fiber reconstruction algorithms and of individual steps in iterative fiber reconstruction algorithms. Insight into the performance of fiber reconstruction algorithms is obtained by a list‐based ranking interface. A 3D view offers interactive visualization techniques to gain deeper insight, e.g., into the aggregated quality of the examined fiber reconstruction algorithms and parameterizations. The tool was designed in close collaboration with researchers who work with fiber‐reinforced polymers on a daily basis and develop algorithms for tomographic reconstruction and characterization of such materials. We evaluate the tool using synthetic datasets as well as tomograms of real materials. Five case studies certify the usefulness of the tool, showing that it significantly accelerates the analysis and provides valuable insights that make it possible to improve the fiber reconstruction algorithms. The main contribution of the paper is the well‐considered combination of methods and their seamless integration into a visual tool that supports the entire workflow. Further findings result from the analysis of (dis‐)similarity measures for fibers as well as from the discussion of design decisions. It is also shown that the generality of the analytical methods allows a wider range of applications, such as the application in pore space analysis.

The comparison of many members of an ensemble is difficult, tedious, and error-prone, which is aggravated by often just subtle differences. In this paper, we introduce Dynamic Volume Lines for the interactive visual analysis and comparison of sets of 3D volumes. Each volume is linearized along a Hilbert space-filling curve into a 1D Hilbert line plot, which depicts the intensities over the Hilbert indices. We present a nonlinear scaling of these 1D Hilbert line plots based on the intensity variations in the ensemble of 3D volumes, which enables a more effective use of the available screen space. The nonlinear scaling builds the basis for our interactive visualization techniques. An interactive histogram heatmap of the intensity frequencies serves as overview visualization. When zooming in, the frequencies are replaced by detailed 1D Hilbert line plots and optional functional boxplots. To focus on important regions of the volume ensemble, nonlinear scaling is incorporated into the plots. An interactive scaling widget depicts the local ensemble variations. Our brushing and linking interface reveals, for example, regions with a high ensemble variation by showing the affected voxels in a 3D spatial view. We show the applicability of our concepts using two case studies on ensembles of 3D volumes resulting from tomographic reconstruction. In the first case study, we evaluate an artificial specimen from simulated industrial 3D X-ray computed tomography (XCT). In the second case study, a real-world XCT foam specimen is investigated. Our results show that Dynamic Volume Lines can identify regions with high local intensity variations, allowing the user to draw conclusions, for example, about the choice of reconstruction parameters. Furthermore, it is possible to detect ring artifacts in reconstructions volumes.

This work introduces a tool for interactive exploration and visualization using MetaTracts. MetaTracts is a novel method for extraction and visualization of individual fiber bundles and weaving patterns from X-ray computed tomography (XCT) scans of endless carbon fiber reinforced polymers (CFRPs). It is designed specifically to handle XCT scans of low resolutions where the individual fibers are barely visible, which makes extraction of fiber bundles a challenging problem. The proposed workflow is used to analyze unit cells of CFRP materials integrating a recurring weaving pattern. First, a coarse version of integral curves is used to trace sections of the individual fiber bundles in the woven CFRP materials. We call these sections MetaTracts. In the second step, these extracted fiber bundle sections are clustered using a two-step approach: first by orientation, then by proximity. The tool can generate volumetric representations as well as surface models of the extracted fiber bundles to be exported for further analysis. In addition a custom interactive tool for exploration and visual analysis of MetaTracts is designed. We evaluate the proposed workflow on a number of real world datasets and demonstrate that MetaTracts effectively and robustly identifies and extracts fiber bundles.

Abstract Material engineers use interrupted in situ tensile testing to investigate the damage mechanisms in composite materials. For each subsequent scan, the load is incrementally increased until the specimen is completely fractured. During the interrupted in situ testing of glass fiber reinforced polymers (GFRPs) defects of four types are expected to appear: matrix fracture, fiber/matrix debonding, fiber pull‐out, and fiber fracture. There is a growing demand for the detection and analysis of these defects among the material engineers. In this paper, we present a novel workflow for the detection, classification, and visual analysis of defects in GFRPs using interrupted in situ tensile tests in combination with X‐ray Computed Tomography. The workflow is based on the automatic extraction of defects and fibers. We introduce the automatic Defect Classifier assigning the most suitable type to each defect based on its geometrical features. We present a visual analysis system that integrates four visualization methods: 1) the Defect Viewer highlights defects with visually encoded type in the context of the original CT image, 2) the Defect Density Maps provide an overview of the defect distributions according to type in 2D and 3D, 3) the Final Fracture Surface estimates the material fracture's location and displays it as a 3D surface, 4) the 3D Magic Lens enables interactive exploration by combining detailed visualizations in the region of interest with overview visualizations as context. In collaboration with material engineers, we evaluate our solution and demonstrate its practical applicability.

This work introduces MetaTracts, a novel method for extracting and visualizing individual fiber bundles and weaving patterns from X-ray computed tomography (XCT) scans of endless carbon fiber reinforced polymers (CFRP). The proposed work flow is designed to analyze unit cells of CFRP materials integrating the recurring weaving pattern. It is designed to handle XCT scans of low resolution, in which individual fibers are not visible or are barely visible. First, a coarse version of integral curves is used to trace subsections of the individual fiber bundles in the woven CFRP materials. We call these sections MetaTracts. In the second step, these extracted fiber bundle sections (MetaTracts) are clustered using a two-step approach: first by orientation, then by proximity. The tool can generate volumetric representations as well as surface models of the extracted fiber bundles to be exported for further analysis. We evaluate the proposed work flow on a number of real world datasets and demonstrate that MetaTracts effectively and robustly identifies and separates different fiber bundles.

Advanced composites such as fiber reinforced polymers are promising candidate materials for future components as they allow integrating the continuously rising demands of industry regarding cost-effectiveness, function-orientation, integration and weight. The most important structures of fiber reinforced polymers are the individual fibers, as their characteristics (stiffness, strength, ductility, durability, etc.) to a large extent determine the properties of the final component. The main contribution of this paper is the introduction of a new system for interactive exploration and visual analysis of fiber properties in X-ray computed tomography data of fiber reinforced polymers. The presented tool uses parallel coordinates to define and configure initial fiber classes. Using a scatter plot matrix linked to the parallel coordinates the initial classification may be refined. This allows to analyze hidden relationships between individual fiber properties. 2D and 3D views depict the resulting fiber classifications. By using polar plots an intuitive rendering of the fiber orientation distribution is provided. In addition, two modules of higher abstraction are proposed: The Blob visualization creates a hull around fibers with similar characteristics. The fiber metadata visualization allows to calculate overlays for 2D and 3D views containing regional information of particular material characteristics. The proposed system has been evaluated by two groups of domain experts. Applying the presented concepts the user feedback shows that the domain experts are now able to efficiently perform tasks as classification of fibers, visualization of fiber lengths and orientations, and visualization of fiber regions. The insights gained can be forwarded to the design office as well as to material development and simulation, in order to speed up the development of novel composite components.

Abstract This paper addresses the increasing demand in industry for methods to analyze and visualize multimodal data involving a spectral modality. Two data modalities are used: high‐resolution X‐ray computed tomography (XCT) for structural characterization and low‐resolution X‐ray fluorescence (XRF) spectral data for elemental decomposition. We present InSpectr, an integrated tool for the interactive exploration and visual analysis of multimodal, multiscalar data. The tool has been designed around a set of tasks identified by domain experts in the fields of XCT and XRF. It supports registered single scalar and spectral datasets optionally coupled with element maps and reference spectra. InSpectr is instantiating various linked views for the integration of spatial and non‐spatial information to provide insight into an industrial component's structural and material composition: views with volume renderings of composite and individual 3D element maps visualize global material composition; transfer functions defined directly on the spectral data and overlaid pie‐chart glyphs show elemental composition in 2D slice‐views; a representative aggregated spectrum and spectra density histograms are introduced to provide a global overview in the spectral view. Spectral magic lenses, spectrum probing and elemental composition probing of points using a pie‐chart view and a periodic table view aid the local material composition analysis. Two datasets are investigated to outline the usefulness of the presented techniques: a 3D virtually created phantom with a brass metal alloy and a real‐world 2D water phantom with insertions of gold, barium, and gadolinium. Additionally a detailed user evaluation of the results is provided.

This paper describes an advanced visualization method for the analysis of defects in industrial 3D X-Ray Computed Tomography (XCT) data. We present a novel way to explore a high number of individual objects in a dataset, e.g., pores, inclusions, particles, fibers, and cracks demonstrated on the special application area of pore extraction in carbon fiber reinforced polymers (CFRP). After calculating the individual object properties volume, dimensions and shape factors, all objects are clustered into a mean object (MObject). The resulting MObject parameter space can be explored interactively. To do so, we introduce the visualization of mean object sets (MObject Sets) in a radial and a parallel arrangement. Each MObject may be split up into sub-classes by selecting a specific property, e.g., volume or shape factor, and the desired number of classes. Applying this interactive selection iteratively leads to the intended classifications and visualizations of MObjects along the selected analysis path. Hereby the given different scaling factors of the MObjects down the analysis path are visualized through a visual linking approach. Furthermore the representative MObjects are exported as volumetric datasets to serve as input for successive calculations and simulations. In the field of porosity determination in CFRP non-destructive testing practitioners use representative MObjects to improve ultrasonic calibration curves. Representative pores also serve as input for heat conduction simulations in active thermography. For a fast overview of the pore properties in a dataset we propose a local MObjects visualization in combination with a color-coded homogeneity visualization of cells. The advantages of our novel approach are demonstrated using real world CFRP specimens. The results were evaluated through a questionnaire in order to determine the practicality of the MObjects visualization as a supportive tool for domain specialists.

Abstract In this work a novel method for the characterization of porosity in carbon fiber reinforced polymers (CFRP) is presented. A visualization pipeline for the interactive exploration and visual analysis of CFRP specimens is developed to enhance the evaluation workflow for non‐destructive testing (NDT) practitioners based on specified tasks. Besides quantitative porosity determination and the calculation of local pore properties, i.e., volume, surface, dimensions and shape factors, we employ a drill‐down approach to explore pores in a CFRP specimen. We introduce Porosity Maps (PM), to allow for a fast porosity evaluation of the specimen. Pores are filtered in two stages. First a region of interest is selected in the porosity maps. Second, pores are filtered with parallel coordinates according to their local properties. Furthermore a histogram‐based best‐viewpoint widget was implemented to visualize the quality of viewpoints on a sphere. The advantages of our approach are demonstrated using real world CFRP specimens. We are able to show that our visualization‐driven approach leads to a better evaluation of CFRP components than existing reference methods.

Multi-material components, which contain metal parts surrounded by plastic materials, are highly interesting for inspection using industrial 3D X-ray computed tomography (3DXCT). Examples of this application scenario are connectors or housings with metal inlays in the electronic or automotive industry. A major problem of this type of components is the presence of metal, which causes streaking artifacts and distorts the surrounding media in the reconstructed volume. Streaking artifacts and dark-band artifacts around metal components significantly influence the material characterization (especially for the plastic components). In specific cases these artifacts even prevent a further analysis. Due to the nature and the different characteristics of artifacts, the development of an efficient artifact-reduction technique in reconstruction-space is rather complicated. In this paper we present a projection-space pipeline for metal-artifacts reduction. The proposed technique first segments the metal in the spatial domain of the reconstructed volume in order to separate it from the other materials. Then metal parts are forward-projected on the set of projections in a way that metal-projection regions are treated as voids. Subsequently the voids, which are left by the removed metal, are interpolated in the 2D projections. Finally, the metal is inserted back into the reconstructed 3D volume during the fusion stage. We present a visual analysis tool, allowing for interactive parameter estimation of the metal segmentation. The results of the proposed artifact-reduction technique are demonstrated on a test part as well as on real world components. For these specimens we achieve a significant reduction of metal artifacts, allowing an enhanced material characterization.

This paper describes a novel method for creating surface models of multi-material components using dual energy computed tomography (DECT). The application scenario is metrology and dimensional measurement in industrial high resolution 3D x-ray computed tomography (3DCT). Based on the dual source / dual exposure technology this method employs 3DCT scans of a high precision micro-focus and a high energy macro-focus x-ray source. The presented work makes use of the advantages of dual x-ray exposure technology in order to facilitate dimensional measurements of multi-material components with high density material within low density material. We propose a workflow which uses image fusion and local surface extraction techniques: a prefiltering step reduces noise inherent in the data. For image fusion the datasets have to be registered. In the fusion step the benefits of both scans are combined. The structure of the specimen is taken from the low precision, blurry, high energy dataset while the sharp edges are adopted and fused into the resulting image from the high precision, crisp, low energy dataset. In the final step a reliable surface model is extracted from the fused dataset using a local adaptive technique. The major contribution of this paper is the development of a specific workflow for dimensional measurements of multi-material industrial components, which takes two x-ray CT datasets with complementary strengths and weaknesses into account. The performance of the workflow is discussed using a test specimen as well as two real world industrial parts. As result, a significant improvement in overall measurement precision, surface geometry and mean deviation to reference measurement compared to single exposure scans was facilitated.

This paper describes a robust method for creating surface models from volume datasets with distorted density values due to artefacts and noise. Application scenario for the presented work is variance comparison and dimensional measurement of homogeneous industrial components in industrial high resolution 3D computed tomography (3D-CT). We propose a pipeline which uses common 3D image processing filters for pre-processing and segmentation of 3D-CT datasets in order to create the surface model. In particular, a pre-filtering step reduces noise and artefacts without blurring edges in the dataset. A watershed filter is applied on the gradient information of the smoothed data to create a binary dataset. Finally the surface model is constructed, using constrained elastic-surface nets to generate a smooth but feature preserving mesh of a binary volume. The major contribution of this paper is the development of the specific processing pipeline for homogeneous industrial components to handle large resolution data of industrial CT scanners. The pipeline is crucial for the following visual inspection of deviations.