•X-ray computed tomography (CT) is studied under changes of sample angular tilt θ.•Dimensional accuracy of CT volumetric reconstruction is evaluated in function of θ.•Ray-casting and Radon-space ...analysis can be used to identify optimal sample tilt θ.•Simulation tools are useful for optimizing the sample tilt θ for X-ray CT metrology.•The dimensional deviations of the CT data are under 10 µm when scanning at optimal θ.
This paper evaluates the dimensional accuracy of industrial X-ray computed tomography (CT) measurements in function of the sample’s orientation within the measurement volume. Experimental measurements are contrasted against predictions from simulations that, based on Radon-space analysis and computation of X-ray traversal-path lengths through ray-casting, estimate the optimal orientation of samples that would minimize both cone-beam distortions and beam hardening artifacts in CT data reconstructions. The simulated data were obtained with the Dreamcaster, a tool for processing and visual analysis of industrial CT datasets. For the experimental data, the tilt angles from the CT rotation axis that resulted in the smallest deviations between dimensional CT data and reference measurements—obtained from contact coordinate measuring machines (CMMs)—were determined for each sample. Tilt angles that resulted in large dimensional measurement errors were also determined. In most of the cases, the data observed in the experimental measurements agree with the trends predicted by simulated data. For slender workpieces (with high aspect ratios), three basic zones of axial angular tilt were identified: a ‘green zone’ from 10 to 35° in which deviations between CT and CMM are minimum, a ‘red zone’ from 0 to 5° and from 65 to 90° in which the largest deviations are measured, and ‘yellow zones’ between the two. When optimal orientations from the ‘green zone’ are used for scanning, the dimensional deviations of CT data from reference measurements are under 10 µm.
This study demonstrates the utilization of deep learning techniques for binary semantic segmentation of pores in carbon fiber reinforced polymers (CFRP) using X-ray computed tomography (XCT) ...datasets. The proposed workflow is designed to generate efficient segmentation models with reasonable execution time, applicable even for users using consumer-grade GPU systems. First, U-Net, a convolutional neural network, is modified to handle the segmentation of XCT datasets. In the second step, suitable hyperparameters are determined through a parameter analysis (hyperparameter tuning), and the parameter set with the best result was used for the final training. In the final step, we report on our efforts of implementing the testing stage in open_iA, which allows users to segment datasets with the fully trained model within reasonable time. The model performs well on datasets with both high and low resolution, and even works reasonably for barely visible pores with different shapes and size. In our experiments, we could show that U-Net is suitable for pore segmentation. Despite being trained on a limited number of datasets, it exhibits a satisfactory level of prediction accuracy.
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.
Visual Parameter Space Analysis: A Conceptual Framework Sedlmair, Michael; Heinzl, Christoph; Bruckner, Stefan ...
IEEE transactions on visualization and computer graphics,
12/2014, Letnik:
20, Številka:
12
Journal Article
Recenzirano
Various case studies in different application domains have shown the great potential of visual parameter space analysis to support validating and using simulation models. In order to guide and ...systematize research endeavors in this area, we provide a conceptual framework for visual parameter space analysis problems. The framework is based on our own experience and a structured analysis of the visualization literature. It contains three major components: (1) a data flow model that helps to abstractly describe visual parameter space analysis problems independent of their application domain; (2) a set of four navigation strategies of how parameter space analysis can be supported by visualization tools; and (3) a characterization of six analysis tasks. Based on our framework, we analyze and classify the current body of literature, and identify three open research gaps in visual parameter space analysis. The framework and its discussion are meant to support visualization designers and researchers in characterizing parameter space analysis problems and to guide their design and evaluation processes.
Understanding and analyzing univariate distributions of data in terms of their shapes as well as their specific characteristics, regarding gaps, spikes, or outliers, is crucial in many scientific ...disciplines. In this paper, we propose a design space composed of the visual channels position and color for representing accumulated distributions. The designs are a mixture of color-coded stripes with density lines. The width and coloring of the stripes is based on the applied binning technique. In a crowd-sourced experiment we explore a subspace, called the AccuStripes (i.e., “accumulated stripes”) design space, consisting of nine representations. These AccuStripes designs integrate three composition strategies (color only, overlay, filled curve) with three binning techniques, one uniform (UB) and two adaptive methods, namely Bayesian Blocks (BB) and Jenks’ Natural Breaks (NB). We evaluate the accuracy, efficiency, and confidence ratings of the nine AccuStripes designs for structural estimation and comparison tasks. Across all study tasks, the overlay composition was found to be most accurate and preferred by observers. Furthermore, the results demonstrate that while no binning method performed best in both identification and comparison, detection of structures using adaptive binning was the most accurate one. For validation we compared the best AccuStripes’ design, i.e., the overlay composition, to line charts. Our results show that the AccuStripes’ design outperformed the line charts in accuracy for all study tasks.
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•Novel design space to represent and compare univariate data distributions.•A subspace is introduced combining color-coded stripes with density lines.•The stripes’ width and coloring are defined by adaptive and uniform binning methods.•Evaluation and validation of the subspace through two crowdsource studies.
The incorporation of silica nanostructures in polymer membranes is beneficial for the chemical, mechanical and the life time stability of high temperature proton exchange membrane fuel cells. Since ...the properties are closely related to the morphology and chemical composition of the constituents, their detailed investigation is of importance. In this paper transmission electron microscopy is utilized to study cross-linked silica nanoparticles in phosphoric acid doped polybenzimidazole membranes, which are formed by an in-situ sol–gel reaction using varying reaction parameters. The results show that by an additional heating step before membrane casting, several hundred nanometer large, ellipsoidal silica particles are formed in the membrane. Small, elongated nanoparticles with sizes in the range of about 20nm are present in all samples independent of the heat treatment. In-depth energy dispersive X-ray spectroscopy and electron energy loss spectroscopy analysis proved the homogeneous distribution and composition of the amorphous silica nanoparticles and revealed that no enrichment occurs at the interface between nanoparticles and polymer. The ex-situ membrane investigations reveal a higher chemical and mechanical stability for membranes containing the large, ellipsoidal particles. However, the membrane with only smaller silica nanoparticles shows a better degradation behavior than the membrane with large particles.
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•Sol–gel synthesis of silica reinforced polybenzimidazole (PBI) based membranes.•Transmission electron microscopy allows to determine PBI based membrane structure.•Size and distribution of silica nanoparticles depend on the synthesis temperature.•Ex- and in-situ properties correlate to the silica particle size and distribution.
We present a novel smart visibility system for visualizing crowded volumetric data containing many object instances. The presented approach allows users to form groups of objects through membership ...predicates and to individually control the visibility of the instances in each group. Unlike previous smart visibility approaches, our approach controls the visibility on a per-instance basis and decides which instances are displayed or hidden based on the membership predicates and the current view. Thus, cluttered and dense volumes that are notoriously difficult to explore effectively are automatically sparsified so that the essential information is extracted and presented to the user. The proposed system is generic and can be easily integrated into existing volume rendering applications and applied to many different domains. We demonstrate the use of the volume conductor for visualizing fiber-reinforced polymers and intracellular organelle structures.
Platinum cobalt catalysts (Pt–Co) have attracted much interest as cathode catalysts for proton exchange membrane fuel cells (PEMFCs) due to their high activity toward oxygen reduction reaction (ORR). ...Many of the reported catalysts show outstanding performance in ex-situ experiments. However, the laborious synthesis protocols of these Pt–Co catalysts disable an efficient and economic production of membrane electrode assemblies (MEAs). We present an economic, flexible and continuous Pt-M/C catalyst preparation method as part of a large scale membrane electrode assembly manufacturing. In comparison, the as-prepared Pt–Co/C based high temperature (HT)-PEM MEA showed an equal performance to a commercially available HT-PEM MEA during 600 h of operation under constant load, although the commercial one had a significantly higher Pt loading at the cathode.
•Platinum cobalt catalysts are synthesized by a scalable straightforward process.•Stability increasing post-preparation treatments are developed and optimized.•RDE experiments and HT-PEM single cell tests are conducted.•Platinum loading is reduced without loss of performance and durability.
A new framework for the parametric reconstruction of curved fibres from glass fibre-reinforced composite X-ray computed tomography data is proposed. It allows us to detect fibres in a ...fibre-reinforced polymer sample from a low-dose, low resolution computed tomography scan. An efficient curve representation is then used for each detected fibre, of which the parameters are estimated directly from few 2D high-resolution projection images. The framework is validated on both simulated and real data of glass fibre-reinforced polymers. The generated results demonstrate that it is robust to noise and requires less than 10 high-resolution projections to obtain reasonable fibre estimates. The method can also improve upon existing estimation frameworks relying on full 3D scans.
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