TY - CONF A1 - Bruno, Giovanni A1 - Evsevleev, Sergei A1 - Mishurova, Tatiana A1 - Meinel, Dietmar A1 - Koptyug, A. A1 - Surmeneva, M. A1 - Khrapov, D. A1 - Paveleva, A. A1 - Surmenev, R. 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 -