Intelligent Automation, Inc. — Department of Defense STTR Phase I: N20B-T026

Description

NAVY seeks high strength, low density, and high corrosion resistant alloys for structural components which can be processed by additive manufacturing (AM). Magnesium (Mg) alloys are candidates for fuel-efficiency applications, especially the aircrafts. They satisfy density, strength, and stiffness for many designs. However, their low corrosion resistance cannot ensure design lifetimes.  This limits them to non-/semi-structural applications in Navy aircrafts. To overcome this limitation, Intelligent Automation, Inc. proposes development of AM processable magnesium alloy(s) by data driven Artificial Neural Networks (ANN), their powder production, and property characterizations. Alloy design is a multi-criteria decision-making procedure that matches materials traits with design requirements. At this juncture, ICME emerges as an overarching approach aiming to link processing, microstructure, properties, and performance (PMPP) of materials to design expectations. Present computational alloy design approaches, such as PHAse COMPutations (PHACOMP) and CALculations of PHAse Diagrams (CALPHAD), require a sequential treatment of several microstructural constituents, thermodynamic properties, mechanical properties, and employment of a multitude number of phase diagrams. Consequently, high fidelity computational thermodynamics/mechanics simulations for multicomponent alloys are still in their infancy. In such a context, ANN appears as a fast and reliable alloy design approach, which can uncover complex correlations between material traits and design criteria.