# Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing
###### Abstract  
Recent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and to optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared with a commercial 316L stainless steel taken as a reference. The optimized alloy performs similarly to 316L in terms of coefficient of thermal expansion, hardness and elongation, but has a 17% lower yield strength and ultimate tensile strength (UTS), indicating that further optimization is required. alloy design; computational thermodynamics; laser powder bed fusion (LPBF); austenitic stainless alloys +
Footnote †: journal: _Applied Sciences_  
casting (e.g., 738), directional solidification (e.g., CMSX-4) or powder metallurgy (e.g., N18), so similar efforts should be undertaken towards the design of AM-specific alloys [4]. Nevertheless, few studies have focused on optimizing alloy composition to improve the overall properties of AM products and most alloy design efforts have been devoted primarily to aluminum and its alloys [5; 6]. Improving process robustness, minimizing defects and achieving superior mechanical properties can be undertaken by designing new alloys tailored for AM. This, in turn, has the potential to significantly improve the performance of 3D printed parts. In recent years, several authors have presented successful approaches to design novel compositions using a combination of different computational tools [7; 8; 9; 10]. This approach includes the use of computational thermodynamics to predict phases [11] and other physical models to predict material properties [12; 13]. However, depending on the specific property of interest, physical models may not always be readily available. In such cases, the use of machine learning (ML) algorithms to build and train models using existing data has been widely adopted [14; 15]. Most studies on alloy design and additive manufacturing focus primarily on the acceleration of alloy development by additive manufacturing. This is achieved by producing small quantities of materials for rapid screening and characterization of a wide range of compositions, as in the case of steels [16], or by producing out-of-equilibrium microstructures with unique properties, as in the case of titanium [17] or aluminum alloys [6] for additive manufacturing. Research dedicated to the development of alloys for additive manufacturing is less common and less well documented, with ongoing exploratory work on the precise methodologies and design criteria to be employed. Previous studies have often used a general approach to alloy selection and sorting [18; 19]. However, these studies usually do not rely on a comprehensive multi-criteria exploration. In a recent development, [PERSON] et al. designed titanium alloys using thermodynamic calculations [20], considering constraints to prevent solidification cracking.