The computer-aided materials design process, otherwise known as Integrated Computational Materials Engineering (ICME), is highly iterative and as such requires flexible tools that have the ability to link processing, properties, and performance not only in the usual forward direction but also in the inverse direction more associated with a goal-oriented/design framework of ICME. While many techniques exist that relate properties to performance in both forward/inverse directions, tools that prescribe a process when given a desired microstructure have not been developed in detail. This research fills that gap by coupling physics-based precipitation models with "mesh adaptive direct search" optimization techniques. The tool is used to prescribe heat treatments in Ni-rich NiTi shape memory alloys that will result in a desired size distribution of Ni4Ti3 precipitates. This predictive technique provides a rigorous strategy for the identification of materials processing schedules-provided the forward models connecting processing and microstructure are available-that can significantly reduce the experimental search space that needs to be explored, accelerating the materials development process.
