Way To Go! — Designing Gradients for Joining Nitinol to Dissimilar Structural Alloys Via a Novel Pathfinder Framework

Functionally graded materials (FGMs) have received significant attention for their ability to create components with locally tailored properties. Computational methods have developed to design optimal gradients for a variety of applications. One popular set of approaches uses equilibrium phase data mined using CALPHAD and path planning algorithms to construct composition gradients that are free of intermetallics. However, these approaches query CALPHAD blindly, sampling the entire search space. Thus, they scale very poorly to systems with many elements. We have developed a computational framework that improves on these approaches by directly coupling CALPHAD sampling with modified path planning algorithms to intelligently search the composition space. Our method requires orders of magnitude fewer calculations than existing methods to create optimal composition gradients. The greatly improved scalability of our framework allows gradients to be designed in systems of 5+ elements on practical timescales. We have successfully applied this framework to create gradients between nitinol and various structural alloys. Determining these gradients paves the way for joining of nitinol to dissimilar alloys, something that was previously impractical since the vast majority of gradients are bound to have brittle phases unless carefully controlled.