The design of new alloys by and for metal additive manufacturing (AM) is an emerging field of research. Currently, pre-alloyed powders are used in metal AM, which are expensive and inflexible in terms of varying chemical composition. The present study describes the adaption of rapid alloy development in laser powder bed fusion (LPBF) by using elemental powder blends. This enables an agile and resource-efficient approach to designing and screening new alloys through fast generation of alloys with varying chemical compositions. This method was evaluated on the new and chemically complex materials group of multi-principal element alloys (MPEAs), also known as high-entropy alloys (HEAs). MPEAs constitute ideal candidates for the introduced methodology due to the large space for possible alloys. First, process parameters for LPBF with powder blends containing at least five different elemental powders were developed. Secondly, the influence of processing parameters and the resulting energy density input on the homogeneity of the manufactured parts were investigated. Microstructural characterization was carried out by optical microscopy, electron backscatter diffraction (EBSD), and energy-dispersive X-ray spectroscopy (EDS), while mechanical properties were evaluated using tensile testing. Finally, the applicability of powder blends in LPBF was demonstrated through the manufacture of geometrically complex lattice structures with energy absorption functionality.
Density functional theory (DFT) calculations were performed on Al x C y CoFeMnNi multi-principal element alloys (MPEAs) to understand the influence of Al and C on the stacking-fault energy (SFE). C addition to CoFeMnNi resulted in increased SFE, while it decreased in Al-alloyed CoFeMnNi. For experimental verification, Al 0.26 C y CoFeMnNi with 0, 1.37 and 2.70 at% C were designed by computational thermodynamics, produced by additive manufacturing (AM) and characterized by tensile tests and microstructure analysis. Twinning-induced plasticity (TWIP) was enhanced with increased C, which confirmed a decreased SFE. The combination of these methods provides a promising toolset for mechanism-oriented design of MPEAs with advanced mechanical properties.
We present our latest results on linking the process-structure-propertiesperformance (PSPP) chain for metal additive manufacturing (AM), using a multi-scale and multi-physics integrated computational materials engineering (ICME) approach. The abundance of design parameters and the complex relationship between those and the performance of AM parts have so far impeded the widespread adoption of metal AM technologies for structurally critical load-bearing components. To unfold the full potential of metal AM, establishing a full quantitative PSPP linkage is essential. It will not only help in understanding the underlying physics but will also serve as a powerful and effective tool for optimal computational design. In this work, we illustrate an example of ICME-based PSPP linkage in metal AM, along with a hybrid physics-based data-driven strategy for its application in the optimal design of a component. Finally, we discuss our outlook for the improvement of each part in the computational linking of the PSPP chain.
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