Wissenschaftliche Artikel der BAM
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An ultrasonic vibration post-treatment procedure was suggested for additively manufac-tured lattices. The aim of the present research was to investigate mechanical properties andthe differences in mechanical behavior and fracture modes of Ti6Al4V scaffolds treated withtraditional powder recovery system (PRS) and ultrasound vibration (USV). Scanning electronmicroscopy (SEM) was used to investigate the strut surface and the fracture surface mor-phology. X-ray computed tomography (CT) was employed to evaluate the inner structure,strut dimensions, pore size, as well as the surface morphology of additively manufacturedporous scaffolds. Uniaxial compression tests were conducted to obtain elastic modulus,compressive ultimate strength and yield stress. Finite element analysis was performedfor a body-centered cubic (BCC) element-based model and for CT-based reconstructiondata, as well as for a two-zone scaffold model to evaluate stress distribution during elasticdeformation. The scaffold with PRS post treatment displayed ductile behavior, while USVtreated scaffold displayed fragile behavior. Double barrel formation of PRS treated scaffoldwas observed during deformation. Finite element analysis for the CT-based reconstructionrevealed the strong impact of surface morphology on the stress distribution in comparisonwith BCC cell model because of partially molten metal particles on the surface of struts,which usually remain unstressed.
X-ray computed tomography has many applications in materials science and non-destructive testing. While the standard filtered back-projection reconstruction of the radiographic data sets is fast and simple, it typically fails in returning accurate results from missing or inconsistent projections. Among the alternative techniques that have been proposed to handle such data is the Direct Iterative REconstruction of Computed Tomography Trajectories (DIRECTT) algorithm. We describe a new approach to the algorithm, which significantly decreases the computational time, while achieving a better reconstruction quality than that of other established algorithms.
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.