Open Access

Synchrotron X-ray computed tomography (SXCT) is regularly used in materials science to correlate structural properties with macroscopic properties and to optimize manufacturing processes. The X-ray beam energy must be adapted to the sample properties, such as size and density. If both strongly and weakly absorbing materials are present, the contrast to the weakly absorbing materials is lost, resulting in image artifacts and a poor signal-tonoise ratio (SNR). One particular example is a low-temperature co-fired ceramics (LTCC), in which metal connections are embedded in a ceramic matrix and form 3-dimensional conducting structures. This article describes a method of combining SXCT scans acquired at different beam energies, significantly reducing metal artifacts, and improving image quality. We show how to solve the difficult task of merging the scans at low and high beam energy. Our proposed merging approach achieves up to 35 % improvement in SNR within ceramic regions adjacent to metallic conductors. In this way, previously inaccessible regions within the ceramic structure close to the metallic conductors are made accessible. The paper further discusses methodological requirements, limitations, and potential extensions of the presented multi-energy SXCT merging technique.

The microstructure of refractory materials is complex, featuring a variety of mineral phases, agglomerates, defects, and controlled porosity. The behavior of refractories at high temperatures adds another layer of complexity, as phase transitions and particle rearrangements can strongly affect their properties. To analyze such intricate microstructure, advanced imaging techniques such as Synchrotron X-ray Computed Tomography (SXCT) allow detailed 3D visualization and quantification of features up to 1 μm. However, the intricacy of these microstructures makes phase identification (known as image segmentation) in digital images a challenging process. X-ray images often contain noise and image artifacts, making the analysis more difficult. Therefore, this work describes image segmentation and artifact reduction methods to characterize refractories using X-ray imaging. We studied refractory ceramics used in the aerospace industry, primarily composed of fused silica. For image segmentation, the traditional approach of greyscale thresholding was compared with machine learning. Greyscale thresholding relies on predefined algorithms to assign phases based on intensity values. In contrast, machine learning extracts patterns from large datasets, enabling more adaptive and accurate segmentation. By combining high-resolution SXCT and machine learning analysis algorithms, we successfully segmented previously uncharacterized 3D microstructural key features of refractories, including agglomerates, grain boundaries, pore size distribution and interconnectivity. Compared to traditional methods, the machine learning-enhanced segmentation presented a more accurate quantification of porosity and defects. The integration of advanced imaging techniques with machine learning segmentation significantly improves the characterization of refractory materials, providing a more precise understanding of the relationship between microstructure and material performance, supporting the development of innovative industrial solutions.

In synchrotron X-ray radiography, achieving high image resolution and an optimal signal-to-noise ratio (SNR) is crucial for the subsequent accurate image analysis. Traditional methods often struggle to balance these two parameters, especially in situ applications where rapid data acquisition is essential to capture specific dynamic processes. For quantitative image data analysis, using monochromatic X-rays is essential. A double multilayer monochromator (DMM) is successfully used for this aim at the BAMline, BESSY II (Helmholtz Zentrum Berlin, Germany). However, such DMMs are prone to producing an unstable horizontal stripe pattern. Such an unstable pattern renders proper signal normalization difficult and thereby causes a reduction of the SNR. We introduce a novel approach to enhance SNR while preserving resolution: dynamic tilting of the DMM. By adjusting the orientation of the DMM during the acquisition of radiographic projections, we optimize the X-ray imaging quality, thereby enhancing the SNR. The corresponding shift of the projection during this movement is corrected in post-processing. The latter correction allows a good resolution to be preserved. This dynamic tilting technique enables the homogenization of the beam profile and thereby effectively reduces noise while maintaining high resolution. We demonstrate that data captured using this proposed technique can be seamlessly integrated into the existing radiographic data workflow, as it does not need hardware modifications to classical X-ray imaging beamline setups. This facilitates further image analysis and processing using established methods.

Synchrotron X-ray computed tomography (SXCT) is regularly used in materials science to correlate structural properties with macroscopic properties and to optimize manufacturing processes. The X-ray beam energy must be adapted to the sample properties, such as size and density. If both strongly and weakly absorbing materials are present, the contrast to the weakly absorbing materials is lost, resulting in image artifacts and a poor signal-to-noise ratio (SNR). One example is a low-temperature co-fired ceramics (LTCC), in which metal connections are embedded in a ceramic matrix and form 3-dimensional conducting structures. This article describes a method of combining SXCT scans acquired at different beam energies, significantly reducing metal artifacts, and improving image quality. We show how to solve the difficult task of merging the scans at low and high beam energy. Our proposed merging approach achieves up to 35% improvement in SNR within ceramic regions adjacent to metallic conductors. In this way, previously inaccessible regions within the ceramic structure close to the metallic conductors are made accessible.

Accurate battery diagnostic models often struggle in capturing complex degradation mechanisms. Here, we report a combined approach using distribution of relaxation time (DRT) analysis and machine learning (ML) of electrochemical impedance spectroscopy (EIS) data to diagnose and predict the performance of solid-state batteries. The dataset has 112 impedance measurements collected over 100 cycles from eight TDK CeraCharge batteries. Using EIS features and the applied charge-discharge rate, the models estimate battery capacity at a given cycle and predict the capacity after the subsequent ten cycles. ML methods, including Bayesian regression, Gaussian process regression, neural networks, and decision trees, achieve accurate diagnostics (mean absolute error 1.6%) and prediction (2.4%). DRT-based feature selection identifies the most informative frequency range (15 Hz–1.5 kHz) and improves training efficiency. Tomography reveals mechanical degradation, i.e., volume expansion and fractures, which increases impedance and contributes to capacity loss. This approach enables accurate, data-driven battery diagnostics and forecasting.