TY - CONF A1 - Bruno, Giovanni T1 - Procedures for quantitative characterization of periodic minimal surface structures (TMPSS) N2 - Additively manufactured (AM) triply periodic metallic minimum surface structures (TPMSS, from the English Triply Periodic Minimum Surface Structures) fulfill several requirements in both biomedical and engineering fields: tunable mechanical properties, low sensitivity to manufacturing defects, mechanical stability, and high energy absorption. However, they also present some quality control challenges that may prevent their successful application. In fact, optimization of the AM process is impossible without considering structural features such as manufacturing accuracy, internal defects, and surface topography and roughness. In this study, quantitative nondestructive analysis of Ti-6Al-4V alloy TPMSS was performed using X-ray computed tomography (XCT). Several new image analysis workflows are presented to evaluate the effects of buildup direction on wall thickness distribution, wall degradation, and surface roughness reduction due to chemical etching of TPMSS. It is shown that the fabrication accuracy is different for the structural elements printed parallel and orthogonal to the fabricated layers. Different strategies for chemical etching showed different powder removal capabilities and thus a gradient in wall thickness. This affected the mechanical performance under compression by reducing the yield stress. A positive effect of chemical etching is the reduction of surface roughness, which can potentially improve the fatigue properties of the components. Finally, XCT was used to correlate the amount of powder retained with the pore size of the TPMSS, which can further improve the manufacturing process. T2 - MSE 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Surface roughness KW - Additive manufacturing KW - Computed tomography KW - Wall thickness KW - Machine learning KW - Manufacturing defects PY - 2022 AN - OPUS4-56162 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Campos de Oliveira, Paula A1 - Markötter, Henning A1 - Zhang, Wen A1 - Eddah, Mustapha A1 - Widjaja, Martinus Putra A1 - Remacha, Clément A1 - Bruno, Giovanni T1 - Enhanced image segmentation of refractories using synchrotron X-ray computed tomography and machine learning techniques N2 - 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. T2 - The 19th Biennial International Technical Conference on Refractories (UNITECR 2025) CY - Cancún, Mexiko DA - 27.10.2025 KW - Synchrotron X-ray Tomography KW - Machine learning KW - Image segmentation KW - Ceramics KW - Refractories PY - 2025 SP - 478 EP - 481 AN - OPUS4-64803 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -