| Abstract Scope |
Additive manufacturing (AM) is a powerful alloy design and prototyping tool. However, the rapid heating and cooling cycle often induces texture with large columnar grains, leading to anisotropic properties and significant residual stress. Furthermore, many alloys necessitate heat treatments, such as aging, to achieve full-strength development. Hence, there exists an urgent requirement to create a tool capable of swiftly optimizing post-heat treatment profiles for newly designed alloys for AM. Through Integrated Computational Materials Engineering (ICME) modeling and high-throughput simulation, a screening model targeting the highest yield strength considering various contributions was developed. This tool was effectively applied with a mixture of 23 wt.% Stainless Steel 316L and 77 wt.% Inconel 718, as a medium-high entropy alloy, fabricated using laser powder bed fusion. Following the optimized post-heat treatment guided by the model, the experimental yield strength of the mixture aligns closely with the simulation result, exhibiting errors of less than 4%. |