8.5 Röntgenbildgebung
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AbstractLithium‐ion cells connected in series are prone to an electrical safety risk called overdischarge. This paper presents a comprehensive investigation of the overdischarge phenomenon in lithium‐ion cells using operando nondestructive imaging. The study focuses on understanding the behavior of copper dissolution and deposition during overdischarge, which can lead to irreversible capacity loss and internal short‐circuits. By utilizing synchrotron X‐ray computed tomography (SXCT), the concentration of dissolved and deposited copper per surface area is quantified as a function of depth of discharge, confirming previous findings. The results also highlight for the first time a nonuniform distribution pattern for copper deposition on the cathode. This research provides insights for safer battery cell design.
Due to their increasing energy density, lithium-ion-batteries (LIBs) play a key role in the traffic energy transition. Regarding their safety behavior, the main challenge of LIB-cells remains the thermal runaway (TR) process. In situ/operando investigations of the TR on commercial cells is possible with radiographic and computer tomographic measurements. Nonetheless, high resolution visualization of the TR persists as a challenge due to the high progression speed of the TR-process itself. Generally, performing abuse tests at cryogenic temperatures allows to slow down or even prevent the TR. Nevertheless, not all abuse methods are suitable for TR investigations at low temperatures. Nail penetration is an appropriate option, however, contains numerous unknown parameters and therefore suffers regarding reproducibility.
Herein, a self-developed high precision nail-penetration-setup is introduced, approaching the necessary mechanically reproducibility with controlled temperatures down to -190°C. The setup allows the preparation of critically abused, however, at cryogenic temperatures stable LIB-cells. These cells were controlled rethermalized to room temperature during synchrotron x-ray computer tomography (SXCT) with a pixel size up to 0.7 μm. During this measurement, the temperature and voltage of the cell is monitored allowing the visualization of the initial internal cell reactions. This study reveals the relation between internal reactions and cell voltage. Finally, the developed set-up enables in-depth analysis of thermal runaway behavior down to material level for various commercial battery cells in the future.
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). By fully utilizing these KETs, advanced and sustainable economies will be created. It is considered that Metrology is a key enabler for the advancement of these KETs. EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network for Advanced Manufacturing. The EMN is made up of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The EMN aims to provide a high-level coordination of European metrology activities for the Advanced Materials and Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing and Advanced Materials (Large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider metrology community (including TCs) to provide input for the preparation of a Strategic Research Agenda (SRA) for Metrology for Advanced Manufacturing.
This presentation will describe the progress in the development of the SRA by the EMN for Advanced Manufacturing. The metrology challenges identified across the various key industrial sectors, which utilise Advanced Materials and Advanced Manufacturing will be presented.
The EMN for Advanced Manufacturing is supported by the project JNP 19NET01 AdvManuNet.
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). It is considered that Metrology is a key enabler for the advancement of these KETs. Consequently, EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network (EMN) for Advanced Manufacturing. The EMN is comprised of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The aim of the EMN is to provide a high-level coordination of European metrology activities for the Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing (large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider Metrology community, including Technical Committees, to provide input for the Strategic Research Agenda (SRA) on Metrology for Advanced Manufacturing.
This contribution will give an overview about the first version of the SRA prepared by the EMN for Advanced Manufacturing
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R2 score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.
Residual stresses Analysis in Additively Manufactured alloys using neutron diffraction (L-PBF)
(2023)
An overview of recent progress at BAM of residual stress analysis in additively manufactured, in particular Laser Powder Bed Fusion of metallics materials, using neutron diffraction will be presented. This will cover important topics of the stress-free reference, the diffraction elastic moduli and principal stress determination.
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). It is considered that Metrology is a key enabler for the advancement of these KETs. Consequently, EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network (EMN) for Advanced Manufacturing. The EMN is comprised of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The aim of the EMN is to provide a high-level coordination of European metrology activities for the Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing (large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider Metrology community, including Technical Committees, to provide input for the Strategic Research Agenda (SRA) on Metrology for Advanced Manufacturing.
This contribution will give an overview about the first version of the SRA prepared by the EMN for Advanced Manufacturing.
Ziel der Untersuchung: Evaluation der Eignung der Refraktions-Tomographie zur Analyse von Alterungserscheinungen innerhalb von adhäsiv verankerten Materialien vor und nach künstlicher Probenalterung.
Material und Methode: Vier humane mittlere Oberkieferfrontzähne wurden wurzelkanalbehandelt und ausgedehnte Klasse IV Defekte der Krone mit einer Aufbaufüllung aus dualhärtendem Komposit (Rebilda DC, VOCO) und einem Glasfaserstift (Rebilda Post, VOCO) restauriert. Alle Proben wurden feucht gelagert und in einer Synchrotron-Einrichtung (BAMline, BESSY II, HZB) mit einer Pixelgröße von 4,08 µm gescannt. Für diese Synchrotron-Refraktions-Tomographie (SXRCT) muss zunächst der Analysatorkristall (AK) zu einer Bragg-Reflexion (Beugung) angeregt werden, um eine SXRCT durchzuführen. Die SXRCT wird mit einer Transmissions-Tomographie (ohne AK) verrechnet. Darauf folgte eine künstliche Probenalterung mit 400.000 Kau- und 3.000 Thermozyklen (50N, 5-55°C). Anschließend wurden die Proben ein weiteres Mal gescannt. Die Datensätze beider SXRCT wurden mittels des „filtered back projection“ Algorithmus rekonstruiert, mit einer Bildanalyse-Software aufeinander registriert und ausgewertet. Hierdurch konnten die Veränderungen initialer Schädigungen analysiert werden.
Ergebnisse: Alle 4 Proben zeigten in den Scans Veränderungen des inneren Gefüges und an Grenzflächen nach Belastung. Es zeigten sich Ansammlungen von Wasser innerhalb eingeschlossener Hohlräume, subkritisches Risswachstum, Ausdehnung des Komposit und Quellung des Glasfaserstiftes um 0,4%. In den SXRCT Aufnahmen waren außerdem Dentintubuli, die bei Auflösungen von 4,08 µm sonst nicht deutlich sichtbar sind darstellbar. Dadurch konnte Eindringen von Wasser in die Tubuli im Anschluss an die Alterung belegt werden.
Zusammenfassung: SXRCT bietet die Möglichkeit, trotz einer vergleichbar geringen Auflösung, kleinste Strukturen und Veränderungen abzubilden. Dies ermöglicht unter anderem die detaillierte Analyse relativ großer Proben, wie eines vollständigen Zahnes in einem Scanvorgang.
We employ in-house generated synthetic Al-Si matrix composite XCT data for training deep convolutional neural networks for XCT data conditioning and automatic segmentation. We propose an in-house multilevel deep conditioning framework capable of rectifying noise and blur in corrupted XCT data sequentially. Furthermore, for automatic segmentation, we utilize a special in-house network coupled with a novel iterative segmentation algorithm capable of generalized learning from synthetic data. We report a consistent SSIM efficiency of 92%, 99%, and 95% for the combined denoising/deblurring, standalone denoising, and standalone deblurring, respectively. The overall segmentation precision was over 85% according to the Dice coefficient. We used experimental XCT data from various scans of Al-Si matrix composites reinforced with ceramic particles and fibers.