AMMap Tool for Additive Manufacturing Design, Alloy Discovery, and Path Planning

Abstract

Compositionally complex materials (CCMs), such as functionally graded materials (FGMs) made by additive manufacturing (AM) often form undesired phases or cracks, negatively affecting the build. Equilibrium thermodynamic calculations and solidification simulations, such as Scheil-Gulliver, can be used to predict feasible compositions or compositional paths, acting as constraints before empirical or machine learning models are applied to predict properties of interest. In addition, additional analysis of solidification simulations can be used to predict hot-cracking using various criteria to further account for manufacturability. To define and navigate the high order chemical systems of CCMs/FGMs, the open-source tool, AMMap, has been developed utilizing open models and CALPHAD methods for thermodynamic computation. AMMap explores spaces constructed with the nimplex library, using a novel algorithm to represent high-dimensional systems as graphs that can be joined into homogeneous structures and explored with graph traversal algorithms to automate the path-design process. This method allows the use of existing high-performance gradient descent, graph traversal search, and other path optimization algorithms to automate the path-design process with as little prior bias as possible.