TY - JOUR A1 - De la Hoz Alford, L. A1 - Pecanha de Souza, C. G. A1 - Paciornik, S. A1 - d` Almeida, J. R. M. A1 - Santos Leite, B. A1 - Avila, H. C. A1 - Léonard, F. A1 - Bruno, Giovanni T1 - Three-Dimensional Characterization of Polyurethane Foams Based on Biopolyols JF - Three-Dimensional Characterization of Polyurethane Foams Based on Biopolyols N2 - Two biopolyol-based foams derived from banana leaves (BL) or stems (BS) were produced, and their compression mechanical behavior and 3D microstructure were characterized. Traditional compression and in situ tests were performed during 3D image acquisition using X-ray microtomography. A methodology of image acquisition, processing, and analysis was developed to discriminate the foam cells and measure their numbers, volumes, and shapes along with the compression steps. The two foams had similar compression behaviors, but the average cell volume was five times larger for the BS foam than the BL foam. It was also shown that the number of cells increased with increasing compression while the average cell volume decreased. Cell shapes were elongated and did not change with compression. A possible explanation for these characteristics was proposed based on the possibility of cell collapse. The developed methodology will facilitate a broader study of biopolyol-based foams intending to verify the possibility of using these foams as green alternatives to the typical petrol-based foams. KW - Compression mechanical KW - Biopolyol KW - Banana KW - 3D microstructure KW - X-ray microtomography PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-571982 DO - https://doi.org/10.3390/ma16052118 SN - 1996-1944 VL - 16 IS - 5 SP - 1 EP - 12 PB - MDPI CY - Basel AN - OPUS4-57198 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kupsch, Andreas A1 - Laquai, René A1 - Müller, Bernd R. A1 - Paciornik, S. A1 - Horvath, J. A1 - Tushtev, K. A1 - Rezwan, K. A1 - Bruno, Giovanni T1 - Evolution of Damage in All-Oxide Ceramic Matrix Composite After Cyclic Loading JF - Advanced Engineering Materials N2 - While structural ceramics usually display a brittle mechanical behavior, their composites may show nonlinearities, mostly due to microcracking. Herein, the stiffness evolution of a sandwich-like laminate of an Al2O3 15%vol. ZrO2 matrix reinforced with Nextel 610 fibers is studied as a function of number of cycles N in tension. The stiffness of the composite degrades with increasing N, indicating microcracking. However, synchrotron X-ray refraction radiography shows that the internal specific surface of such cracks varies differently. A modeling strategy is developed for the calculation of the equivalent stiffness of mixtures (first the matrix and then the sandwich), based on the Voigt and Reuß schemes. The Bruno–Kachanov model is then used to estimate the initial microcrack density in the matrix (due to the thermal expansion mismatch) and the amount of microcracking increase upon cyclic loading. The stiffness in the composite degrades dramatically already after 20 000 cycles but then remains nearly constant. The combination of mechanical testing, quantitative imaging analysis, and modeling provides insights into the damage mechanisms acting: microcrack propagation is more active than microcrack initiation upon cyclic loading, but the second also occurs. This scenario is similar but not equal to previous results on porous and microcracked ceramics. KW - Ceramic matrix composites KW - Homogenization schemes KW - Microcracking KW - Nonlinear behavior KW - Synchrotron X-ray refraction radiography PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-534541 DO - https://doi.org/10.1002/adem.202100763 SN - 1527-2648 SN - 1438-1656 VL - 24 IS - 6 SP - 2100763 -1 EP - 2100763 -13 PB - VCH GmbH CY - Weinheim AN - OPUS4-53454 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lorenzoni, R. A1 - Curosu, I. A1 - Léonard, Fabien A1 - Paciornik, S. A1 - Mechtcherine, V. A1 - Silva, F. A. A1 - Bruno, Giovanni T1 - Combined mechanical and 3D-microstructural analysis of strain-hardening cement-based composites (SHCC) by in-situ X-ray microtomography JF - Cement and Concrete Research N2 - The paper presents the results of a series of combined mechanical and in-situ morphological investigations on highstrength strain-hardening cement-based Composites (SHCC). Tension and compression experiments were performed in a CT scanner employing a dedicated mechanical testing rig. The in-situ microtomographic scans enabled correlating the measured specimen response with relevant microstructural features and fracture processes. The microstructural segmentation of SHCC was performed in the framework of Deep Learning and it targeted an accurate segmentation of pores, fibers and aggregates. Besides their accurate volumetric representation, these phases were quantified in terms of content, size and orientation. The fracture processes were monitored at different loading stages and Digital Volume Correlation (DVC) was employed to spatially map the strains and cracks in the specimens loaded in compression. The DVC analysis highlighted the effect of loading conditions, specimen geometry and material heterogeneity at the mesolevel on the strain distribution and fracture localization. KW - Digital Volume Correlation KW - High Strength Concrete KW - In-situ Computed Tomography KW - composites PY - 2020 DO - https://doi.org/10.1016/j.cemconres.2020.106139 VL - 136 SP - 106139 PB - Elsevier Ltd. AN - OPUS4-51054 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Evsevleev, Sergei A1 - Paciornik, S. A1 - Bruno, Giovanni T1 - Advanced Deep Learning-Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites JF - Advanced Engineering Materials N2 - The quantitative analysis of microstructural features is a key to understanding the micromechanical behavior of metal matrix composites (MMCs), which is a premise for their use in practice. Herein, a 3D microstructural characterization of a five-phase MMC is performed by synchrotron X-ray computed tomography (SXCT). A workflow for advanced deep learning-based segmentation of all individual phases in SXCT data is shown using a fully convolutional neural network with U-net architecture. High segmentation accuracy is achieved with a small amount of training data. This enables extracting unprecedently precise microstructural parameters (e.g., volume fractions and particle shapes) to be input, e.g., in micromechanical models. KW - Computed tomography KW - Convolutional neural networks KW - Deep learning KW - Metal matrix composites KW - Segmentations PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-504261 DO - https://doi.org/10.1002/adem.201901197 SN - 1438-1656 VL - 22 IS - 4 SP - 1901197 PB - WILEY-VCH Verlag GmbH & Co. KGaA CY - Weinheim AN - OPUS4-50426 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -