A Review of X‐Ray Imaging at the BAM<i>line</i> (BESSY II)

Markötter, Henning; Müller, Bernd R.; Kupsch, Andreas; Evsevleev, Sergei; Arlt, Tobias; Ulbricht, Alexander; Dayani, Shahabeddin; Bruno, Giovanni

doi:10.1002/adem.202201034

Cited by 9 publications

(8 citation statements)

References 87 publications

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“…Both setups at BAMline [ 72 ] and P05 [ 73 ] are using a combination of a scintillator and a sCMOS camera as detectors. Table 5 summarizes the experimental parameters.…”

Section: Methodsmentioning

confidence: 99%

Impact of the Carbon Matrix Composition on the S/C Cathode Porosity and Performance in Prototype Li–S Cells

et al. 2023

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The lithium–sulfur battery is a promising electrochemical storage solution, especially for aviation and aeronautical applications, due to its high‐gravimetric energy density (specific energy) and the abundance of sulfur. In recent years, the number of reported prototype cells and their realized energy have increased. This underlines the progress of technology readiness of the lithium–sulfur system. However, the influence of the cathode porosity as well as the porosity of the carbon material on the performance of prototype cells is still not fully understood. Consequently, in this study, the porosity of solvent‐free processed cathodes is investigated, with varying carbon matrix composition, via mercury intrusion porosimetry and synchrotron tomography. Moreover, the swelling behavior of the S/C dry‐film cathodes is investigated and mitigated. These cathodes are then electrochemically evaluated at pouch cell level with ether‐based electrolytes with varying E/S ratios. The combination of the gained findings in pouch cells enables specific energies of 425 Wh kg−1 and 558 Wh L−1 at cell level.

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“…Both setups at BAMline [ 72 ] and P05 [ 73 ] are using a combination of a scintillator and a sCMOS camera as detectors. Table 5 summarizes the experimental parameters.…”

Section: Methodsmentioning

confidence: 99%

Impact of the Carbon Matrix Composition on the S/C Cathode Porosity and Performance in Prototype Li–S Cells

et al. 2023

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“…During image acquisition, the X-ray photons that reach the imaging receptor, e.g., the digital detector array (DDA) used in this study, are sensed by the pixels of the DDA and then are converted to gray values that can be digitally processed and visualized [39]. It is important to mention that the equation above, which follows the Lambert-Beer exponential law could only be realized with a monochromatic X-ray source, e.g., synchrotron [40]. With the polychromatic nature of the X-ray tube used more commonly for image acquisition in NDT radiography applications, the generated X-rays have different energy levels, and this affects the intensity distributions during image acquisition [41].…”

Section: How Exposure Factors Affect Image Qualitymentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Hena

Wei

Ibarra-Castanedo

et al. 2023

Sensors

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In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.

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“…During image acquisition, the X-ray photons that reach the imaging receptor, e.g., the digital detector array (DDA) used in this study, are sensed by the pixels of the DDA, and then are converted to grey values that can be digitally processed and visualized [38]. It is important to mention that the equation above which follows the Lambert-beer exponential law could only be realized with a monochromatic X-ray source, e.g., synchrotron [39]. With the polychromatic nature of X-ray tube used more commonly for image acquisition both in NDT radiography applications, the generated X-rays have different energy levels and this affects the intensity distributions during image acquisition [40].…”

Section: How Exposure Factors Affect Image Qualitymentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

Hena¹,

Wei²,

Ibarra-Castanedo³

et al. 2023

Preprint

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In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

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A Review of X‐Ray Imaging at the BAMline (BESSY II)

Cited by 9 publications

References 87 publications

Impact of the Carbon Matrix Composition on the S/C Cathode Porosity and Performance in Prototype Li–S Cells

Impact of the Carbon Matrix Composition on the S/C Cathode Porosity and Performance in Prototype Li–S Cells

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

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