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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.
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.
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.
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.