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Additive Manufacturing (AM) in terms of laser powder-bed fusion (L-PBF) offers new prospects regarding the design of parts and enables therefore the production of complex structures. The quality of the feedstock material receives increasing attention, as it depicts the first part of the L-PBF process chain. The powder quality control in terms of flowability and powder bed packing density is therefore mandatory.
In this work, a workflow for quantitative 3D powder analysis in terms of particle size, particle shape, particle porosity, inter-particle distance and packing density was established. Synchrotron computed tomography (CT) was used to correlate the packing density with the particle size and particle shape for three different powder batches. The polydisperse particle size distribution (PSD) was transformed into a statistically equivalent bidisperse PSD. The ratio of the small and large particles helped to understand the powder particle packing density. While the particle shape had a neglectable influence, the particle size distribution was identified as major contributor for the packing density.
Over the course of the last decades, concrete construction has significantly changed from mass application towards sophisticated more innovative, future oriented, and performance based materials technology. Today concrete no longer consists of cement, water, and aggregates, but can be enhanced significantly by using supplementary additions and chemical admixtures. Advancements in admixtures eventually facilitated the development of new concrete types such as self-compacting concrete (SCC) and high performance concrete (HPC) by uncoupling the workability from the water-cement ratio (w/c).
While for traditional concrete types the major mixture parameter for the workability and strength were the w/c and the grading of the aggregates, for SCC and HPC the performance parameters are more complicated and the performance depends highly on the powder components. This makes the system more sophisticated and in parallel more prone to scatter during the production. The complexity and difficulty to predict the effects of changes in the process and mixture makes it very difficult to identify the right potentials for savings.