Bulk nanostructured AlCoCrFeMnNi chemically complex alloy synthesized by laser-powder bed fusion process

We report the synthesis of a bulk nanostructured alloy using the laser-powder bed fusion process. The equiatomic AlCoCrFeMnNi chemically complex alloy forms a nanoscale modulated structure, which is homogeneously distributed in the as-built condition. The nanostructure consisted of Al & Ni-rich ordered (B2) and Cr & Fe-rich disordered (A2) BCC phases. The two phases form an interconnected phase-network with coherent interface boundaries. Atom-probe-tomography and aberration-corrected scanning transmission electron microscopy analysis of the spatial distribution of the modulated structure suggests the occurrence of nano-scale spinodal decomposition. These results introduce a direct synthesis of bulk nanostructured alloys with promising geometric flexibility.Comment: 21 pages, 7 Figure

Mikrostrukturentwicklung während Phasenumwandlungen : Simulation und Experiment

This thesis is concerned with the interplay of the kinetics of solid-state phase transformations and the microstructure of materials: The kinetics of phase transformations depend on the parent microstructure and the resulting, product microstructure depends on the kinetics of the phase transformation. These interactions are investigated by the example of nucleation-and-growth, solid state transformations in elemental metals; in particular allotropic and recrystallisation transformations. In the analysis, kinetic models and mesoscopic computer simulations are employed.Diese Arbeit handelt vom Wechselspiel zwischen der Kinetik von Festkörperphasenumwandlungen und dem Gefüge von Materialien: Die Kinetik von Phasenumwandlungen hängt vom Ausgangsgefüge des Materials ab und das resultierende Produktgefüge hängt von der Kinetik der Phasenumwandlung ab. Diese Wechselwirkungen werden anhand von Keimbildungs-und -wachstumsumwandlungen in elementaren Metallen untersucht; insbesondere werden allotrope Umwandlungen und Rekristallisationsumwandlungen betrachtet. Bei der Analyse werden kinetische Modelle und mesoskopische Computersimulationen eingesetzt

Materials Characterization at High Strain Rates

The identification of material properties at high strain rates is of considerable technical interest, e.g., in applications for passenger safety-related parts which are expected to absorb energy in case of collisions. To additionally meet the requirements of a rapid and resource-efficient materials characterization, a novel highspeed hardness testing method based on laser-induced shock waves was investigated. The principal applicability of this laser-induced shockwave indentation technique for materials characterization at high strain rates is shown.Deutsche Forschungsgemeinschaft DFG151

New Frontiers in Materials Design for Laser Additive Manufacturing

ISSN:1996-194

Parameter study of an Al–Cr–Mo–Sc–Zr alloy processed by laser powder bed fusion reaching high build rates

Market availability of aluminum alloys for laser powder bed fusion (L-PBF) is still highly limited in comparison to conventional manufacturing processes. The demand for high-strength but inexpensive alloys specifically designed for L-PBF is high. This demand has led to research on a variety of adapted conventional alloys which are still limited to utilize the full potential of L-PBF. Scalmalloy® (Al–Mg–Sc–Zr) satisfies the demand for high-strength L-PBF-alloys but needs a high energy input and has troubles with evaporation of Mg. Scancromal® (Al–Cr–Sc–Zr) is a novel alloying system for L-PBF and was first introduced in 2019 with the possibility of higher build rates and comparable strengths to Scalmalloy®. In this paper, a more economic low Sc-containing version of Scancromal® is presented. A parameter study was performed for 100 μm layer thickness reaching high build rates of about 47 cm3 h−1 . Hardness tests for different parameters were carried out and showed a stable process window with a hardness comparable to AlSi10Mg. Additionally, two-dimensional multilayer process simulations showed a potential for increasing the layer thickness to 150 μm and therefore a significant increase in build rate of up to 70 cm3 h−1 highlighting the high productivity potential of Al–Cr alloys for L-PBF

On strong-scaling and open-source tools for analyzing atom probe tomography data

The development of strong-scaling computational tools for high-throughput methods with an open-source code and transparent metadata standards has successfully transformed many computational materials science communities. While such tools are mature already in the condensed-matter physics community, the situation is still very different for many experimentalists. Atom probe tomography (APT) is one example. This microscopy and microanalysis technique has matured into a versatile nano-analytical characterization tool with applications that range from materials science to geology and possibly beyond. Here, data science tools are required for extracting chemo-structural spatial correlations from the reconstructed point cloud. For APT and other high-end analysis techniques, post-processing is mostly executed with proprietary software tools, which are opaque in their execution and have often limited performance. Software development by members of the scientific community has improved the situation but compared to the sophistication in the field of computational materials science several gaps remain. This is particularly the case for open-source tools that support scientific computing hardware, tools which enable high-throughput workflows, and open well-documented metadata standards to align experimental research better with the fair data stewardship principles. To this end, we introduce paraprobe, an open-source tool for scientific computing and high-throughput studying of point cloud data, here exemplified with APT. We show how to quantify uncertainties while applying several computational geometry, spatial statistics, and clustering tasks for post-processing APT datasets as large as two billion ions. These tools work well in concert with Python and HDF5 to enable several orders of magnitude performance gain, automation, and reproducibility