# Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing
###### Abstract
They specifically tested compositions suitable for LPBF while maintaining associated process conditions and while considering surface defects, phase structure and cracking susceptibility. A similar approach was developed by [PERSON] and [PERSON] for the design of crack-free aluminum alloys, by reducing the solidification range [18; 21]. Similarly, [PERSON] developed a computational method to design new nickel superalloys that are resistant to hot cracking by minimizing the solidification range using the Scheil model [22]. In addition, they incorporated criteria to reduce strain-age cracking and improve mechanical strength and creep resistance. Their approach is multi-objective, focusing on the selection of alloy compositions by setting bounds on various property tables. However, the global objective space has not been fully explored using an optimization algorithm, suggesting the potential for further improvements through such exploration. It was only with the work of [PERSON] et al. [23; 24] that a comprehensive multi-objective exploration of alloy compositions for additive manufacturing processes was established and successfully applied. They focused on the printability of stainless steel, proposing a defect prevention method that integrates material-dependent properties. Their approach, validated on 316L stainless steel, enhanced printability when compared with other alloys, but was limited to compositions near the nominal composition of 316L. Despite promising results in existing studies, a method is needed that focuses on material composition while addressing various material-dependent phenomena that are related to defects in current AM parts, including porosity formation, cracking, surface roughness and element loss by selective evaporation. A comprehensive alloy design methodology is still lacking, particularly in three key areas. First, an exhaustive list of optimization criteria to reduce defects, better control microstructure, and improve melt pool stability is lacking. Second, no global optimization has been performed over a wide range of compositions, making it difficult to identify optimal alloys that may differ significantly from commercial alloys. This work addresses these two issues by proposing a general methodology and validating it experimentally. The discussions will provide valuable insights into potential improvements in AM design techniques. This work aims to contribute to the current progresses in alloy design approaches for the discovery of new austenitic alloys optimized for additive manufacturing processes. These alloys may belong to the family of austenitic stainless steels or austenitic high entropy alloys. In Section 1, we establish optimization criteria to guide the alloy design process. These criteria fall into two categories. The first group consists of criteria specific to the LPBF process, aimed at minimizing the risk of defects. A second group of criteria is related to the microstructure and the desired mechanical properties, with a focus on maintaining properties suitable for mechanical engineering applications. These criteria can be determined through the combined use of computational thermodynamics, physical models and machine learning algorithms for predicting relevant alloy properties. In the second part, we integrate these criteria into a multi-objective genetic algorithm (GA) to automate the optimization process. This algorithm ultimately proposes a set of optimized alloy compositions. Finally, we validate the models through experimental evaluation. An alloy composition selected from the optimized set is produced in powder form, processed using an AM technique (LPBF) and evaluated experimentally for defects, microstructure, as well as physical and mechanical properties. The resulting microstructure and properties are compared with those of 316L, selected as a reference alloy, to determine the potential improvements in printability and performance resulting from the design process.