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In den letzten zwei Jahren wurden 11 neue Standards/Standardrevisionen zur RT veröffentlicht (ohne Strahlenschutz) und 5 werden dafür gestrichen. Es wird über die neuen Anforderungen ausgewählter Normen bzw. Normrevisionen informiert und insbesondere auch über die veränderten Parameter, die bei Prüfpraxis und bei der Klassifizierung zu berücksichtigen sind. Das wichtigste Projekt ist die Revision der DIN EN ISO 17636-1, -2, RT von Schweißverbindungen, in ISO TC 44 SC 5 WG 1. Die Standards zur Schweißnahtprüfung in der Kerntechnik, DIN 25435, wurden ins Englische übertragen und bei ISO TC 85 SC 6 bearbeitet. Hauptaktivität bei ASTM ist gegenwärtig die Überarbeitung der CT-Standards und die Berücksichtigung der Anforderungen an das dimensionelle Messen. Der Guide ASTM E 1441 zur Bestimmung von MTF, Kontrast-Detail-Funktion (CDF) und Kontrast-Diskrimination-Diagramm (CDD) wird revidiert. E 2445 zur CR Long Term Stability wird ebenfalls revidiert. Die Revision soll auch in die ISO 16371-1 eingehen. Dazu ist ein Round Robin-Test geplant. Auch die Standards zur Durchstrahlungsprüfung auf Korrosion und Ablagerungen in Rohren mit Röntgen- und Gammastrahlen (EN 16407-1, -2, 2014) wurden revidiert. Diese Revision wurde als DIN EN ISO 20769-1, -2 2018 veröffentlicht und EN 16407 wird zurückgezogen. Die Revision der Standards EN 12543 und EN 12679 zur Messung der Brennfleckgröße und der Strahlergröße ist mit Verzögerung in Überarbeitung, um die digitalen Detektoren und Messmöglichkeiten mit Bildverarbeitungsprogrammen zu berücksichtigen. Ein Entwurf zur Messung der Brennfleckgröße von nano-Fokusröhren wird derzeit vom Europäischen Metrologie-Programm EMPIR gefördert. Zur radiographischen Gussteilprüfung wurde der Standard EN 12681 revidiert und 2018 veröffentlicht. Er wurde um Zulässigkeitsgrenzen erweitert und die digitale Radiographie wurde in Teil 2 berücksichtigt.
In the field of optically excited thermography, flash lamps (impulse shaped planar heating) and halogen lamps (modulated planar heating) have become established for the specific regimes of impulse and lock-in thermography. Flying-spot laser thermography is implemented by means of a rasterized focused laser, e. g. for crack detection (continuous wave operation) and photothermal material characterization (high-frequency modulated). The availability of novel technologies, i. e. fast and high-resolution IR cameras, brilliant innovative light sources and high-performance data acquisition and processing technology will enable a paradigm shift from stand-alone photothermal and thermographic techniques to uniform quantitative measurement and testing technology that is faster and more precise. Similar to an LED array, but with irradiance two orders of magnitude higher, a new type of brilliant laser source, i. e. the VCSEL array (vertical-cavity surface-emitting laser), is now available. This novel optical energy source eliminates the strong limitation to the temporal dynamics of established light sources and at the same time is spectrally clearly separated from the detection wavelength. It combines the fast temporal behavior of a diode laser with the high optical irradiance and the wide illumination area of flash lamps. In addition, heating can also be carried out in a structured manner, because individual areas of the VCSEL array can be controlled independently of each other. This new degree of freedom enables the development of completely new thermographic NDT methods.
Mit Laserlicht kann man eine Materialoberfläche berührungslos und schnell moduliert aufheizen. Dabei entsteht eine stark gedämpfte Wärmewelle, die tief ins Material eindringen kann. Erzeugt und überlagert man solche thermischen Wellen auf kohärente Weise, dann kann man damit versteckte Materialfehler zerstörungsfrei und sehr präzise aufspüren. Sogar eine bildgebende Tomografie ist denkbar.
AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.
Tri-structural Isotropic (TRISO)-coated particle is the fission energy source and the first safety barrier in high temperature gas-cooled reactors (HTGRs). The integrity of TRISO particle should be carefully tested before operation because the shape may affect the failure possibility of the particles, leading to increased risk of fission product release. Due to the large difference in density between the kernel and the coating layers in TRISO particles, traditional X-ray radiography cannot achieve a good image quality in terms of identifying coating layers reliably, while phase-contrast CThas the advantageof being sensitive to boundaries. This paperpresents a non-destructive test and evaluation (NDT&E) method to facilitate 3-dimensional (3D) measurement of a TRISO particle's structure, using a synchrotron phase-contrast CT. After reconstructed, the TRISO particle was rendered in a 3D space and the thickness and asphericity of the TRISO particle's layers were measured. It was found that the thickness of coating layers of the tested particle obeys Gaussian distribution. The deviation of thicknesses of the kernel and the other four layers is −2.42%, −16.32%, 26.51%, 0.98% and 7.49% compared with the design parameters. The deviation of asphericity of the kernel, IPyC and OPyC layers is −11.51%, −0.41% and 3.39%, respectively. The effect of the deviations on the temperature distribution and failure probability calculation of the particle will be investigated in the future.
We investigate the pore space of rock samples with respect to different petrophysical parameters using various methods, which provide data on pore size distributions, including micro computed tomography (µ-CT), mercury Intrusion porosimetry (MIP), nuclear magnetic resonance (NMR), and spectral-induced polarization (SIP). The resulting cumulative distributions of pore volume as a function of pore size are compared. Considering that the methods differ with
regard to their limits of resolution, a multiple-length-scale characterization of the pore space is proposed, that is based on a combination of the results from all of these methods.
The approach is demonstrated using samples of Bentheimer and Röttbacher sandstone. Additionally, we compare the potential of SIP to provide a pore size distribution with other commonly used methods (MIP, NMR). The limits of Resolution of SIP depend on the usable frequency range (between 0.002 and 100 Hz). The methods with similar Resolution show a similar behavior of the cumulative pore volume distribution in the verlapping pore size range. We assume that µ-CT and NMR provide the pore body size while MIP and SIP characterize the pore throat size. Our study Shows that a good agreement between the pore radius distributions can only be achieved if the curves are adjusted considering the resolution and pore volume in the relevant range of pore radii. The MIP curve with the widest range in Resolution should be used as reference.
In this study, the optimized fabrication and evolution of the microstructure and magnetic Transition behavior of the melt-extraction LaFe11.2Si1.8 microwires have been studied. After the optimization of extraction technique (heating power 22 KW, feeding rate 30-50 mm/s, rotation velocity 1700 r/min), the content of La Fe,Si)13 phase in the as-extracted microwires was 54 wt% due to the high solidification velocity, which was increased to 85 wt% via annealing at 1373 K for 20 min. The amount of La(Fe,Si)13 phase was increased and the composition of La(Fe,Si)13 phase became more homogenized through peritectic reaction and short-distance diffusion in the microwires during annealing process. The coexistence of the nanocrystalline and amorphous structures contributed to the broad magnetic Transition temperature range of the as-extracted and annealed microwires. The annealed microwires exhibited a second-order magnetic transformation behavior and showed a maximum magnetic entropy Change jDSMjmax of 6.2 J/kgK and working temperature interval of 36.0 K under a magnetic field of 20 kOe.
An observation of the fracture process in front of the crack tip inside a dentin sample by means of ex-situ X-ray computed tomography after uniaxial compression at different deformation values was carried out in this work. This ex-situ approach allowed the microstructure and fracturing process of human dentin to be observed during loading. No cracks are observed up to the middle part of the irreversible deformation in the samples at least visible at 0.4μm resolution. First cracks appeared before the mechanical stress reached the compression strength. The growth of the cracks is realized by connecting the main cracks with satellite cracks that lie ahead of the main crack tip and parallel its trajectory. When under the stress load the deformation in the sample exceeds the deformation at the compression strength of dentin, an appearance of micro-cracks in front of the main cracks is observed. The micro-cracks are inclined (~60°) to the trajectory of the main cracks. The further growth of the main cracks is not realized due to the junction with the micro-cracks; we assume that the micro-cracks dissipate the energy of the main crack and suppressed its growth. These micro-cracks serve as additional stress accommodations, therefore the samples do not break apart after the compression test, as it is usually observed under bending and tension tests.
To date, there are very few technologies available for the conversion of low-temperature waste heat into electricity. Thermomagnetic generators are one approach proposed more than a century ago. Such devices are based on a cyclic change of magnetization with temperature. This switches a magnetic flux and, according to Faraday’s law, induces a voltage. Here we demonstrate that guiding the magnetic flux with an appropriate topology of the magnetic circuit improves the performance of thermomagnetic generators by orders of magnitude. Through a combination of experiments and simulations, we show that a pretzel-like topology results in a sign reversal of the magnetic flux. This avoids the drawbacks of previous designs, namely, magnetic stray fields, hysteresis and complex geometries of the thermomagnetic material. Our demonstrator, which is based on magnetocaloric plates, illustrates that this solid-state energy conversion technology presents a key step towards becoming competitive with thermoelectrics for energy harvesting near room temperature.
Strong coupling effects in magnetocaloric materials are the key factor to achieve a large magnetic entropy change. Combining insights from experiments and ab initio calculations, we review relevant coupling phenomena, including atomic coupling, stress coupling, and magnetostatic coupling. For the investigations on atomic coupling, we have used Heusler compounds as a flexible model system. Stress coupling occurs in first‐order magnetocaloric materials, which exhibit a structural transformation or volume change together with the magnetic transition. Magnetostatic coupling has been experimentally demonstrated in magnetocaloric particles and fragment ensembles. Based on the achieved insights, we have demonstrated that the materials properties can be tailored to achieve optimized magnetocaloric performance for cooling applications.
Strong coupling effects in magnetocaloric materials are the key factor to achieve a large magnetic entropy change. Combining insights from experiments and ab initio calculations, we review relevant coupling phenomena, including atomic coupling, stress coupling, and magnetostatic coupling. For the investigations on atomic coupling, we have used Heusler compounds as a flexible model system. Stress coupling occurs in first‐order magnetocaloric materials, which exhibit a structural transformation or volume change together with the magnetic transition. Magnetostatic coupling has been experimentally demonstrated in magnetocaloric particles and fragment ensembles. Based on the achieved insights, we have demonstrated that the materials properties can be tailored to achieve optimized magnetocaloric performance for cooling applications.
In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design.
This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development.
Potentialfeldmessung (PM) ist die beliebteste Methode der Zerstörungsfreien Prüfung (ZfP) zur Lokalisierung von aktiver Betonstahlkorrosion. PM wird durch Parameter wie z. B. Feuchtigkeits- und Chloridgradienten im Bauteil beeinflusst, so dass die Sensitivität gegenüber der räumlich sehr begrenzten, aber gefährlichen Lochkorrosion gering ist. Wir zeigen in dieser Studie, wie zusätzliche Messinformationen mit Multisensor-Datenfusion genutzt werden können, um die Detektionsleistung zu verbessern und die Auswertung zu automatisieren. Die Fusion basiert auf überwachtem maschinellen Lernen (ÜML). ÜML sind Methoden, die Zusammenhänge in (Sensor-) Daten anhand vorgegebener Kennzeichnungen (Label) erkennen. Wir verwenden ÜML um „defekt“ und „intakt“ gelabelte Bereiche in einem Multisensordatensatz zu unterscheiden. Unser Datensatz besteht aus 18 Messkampagnen und enthält jeweils PM-, Bodenradar-, Mikrowellen-Feuchte- und Wenner-Widerstandsdaten. Exakte Label für veränderliche Umweltbedingungen wurden in einer Versuchsanordnung bestimmt, bei der eine Stahlbetonplatte im Labor kontrolliert und beschleunigt verwittert. Der Verwitterungsfortschritt wurde kontinuierlich überwacht und die Korrosion gezielt erzeugt. Die Detektionsergebnisse werden quantifiziert und statistisch ausgewertet. Die Datenfusion zeigt gegenüber dem besten Einzelverfahren (PM) eine deutliche Verbesserung. Wir beschreiben die Herausforderungen datengesteuerter Ansätze in der zerstörungsfreien Prüfung und zeigen mögliche Lösungsansätze.
This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm.
It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling.
The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.
Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.
The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.
Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 µm) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion.
Durch den großflächigen Einsatz der Computertomographie (CT) in unterschiedlichen Industriebereichen steigen auch die Anforderungen an die quantitative Bildanalyse. Subjektive Bildwahrnehmung muss durch objektive Algorithmen ersetzt werden. In diesem Artikel stellt die Bundesanstalt für Materialforschung und -prüfung (BAM), die seit den 1980er Jahren an der Entwicklung der industriellen CT beteiligt ist, anhand ausgewählter Beispiele den aktuellen Stand ihrer Analysemethoden an verschiedenen Anwendungsbeispielen der CT vor.
Rapid cooling rates and steep temperature gradients are characteristic of additively manufactured parts and important factors for the residual stress formation. This study examined the influence of heat accumulation on the distribution of residual stress in two prisms produced by Laser Powder Bed Fusion (LPBF) of austenitic stainless steel 316L. The layers of the prisms were exposed using two different border fill scan strategies: one scanned from the centre to the perimeter and the other from the perimeter to the centre. The goal was to reveal the effect of different heat inputs on samples featuring the same solidification shrinkage. Residual stress was characterised in one plane perpendicular to the building direction at the mid height using Neutron and Lab X-ray diffraction. Thermography data obtained during the build process were analysed in order to correlate the cooling rates and apparent surface temperatures with the residual stress results. Optical microscopy and micro computed tomography were used to correlate defect populations with the residual stress distribution. The two scanning strategies led to residual stress distributions that were typical for additively manufactured components: compressive stresses in the bulk and tensile stresses at the surface. However, due to the different heat accumulation, the maximum residual stress levels differed. We concluded that solidification shrinkage plays a major role in determining the shape of the residual stress distribution, while the temperature gradient mechanism appears to determine the magnitude of peak residual stresses.