Abstract Scope |
Heterogeneous nanostructured materials (HNMs) are designed using a newly launched novel processing platform, Data-driven Recursive AI-powered Generator of Optimized Nanostructured Superalloys (DRAGONS), that will utilize predictive models to infer microstructure features based on provided processing parameters (DRAGONS-Predict) and prescribe processing parameters required to achieve a desired microstructure through inverse design (DRAGONS-Prescribe). Experimentally, nanotwinned (NT) Inconel alloys are synthesized via magnetron sputtering and subjected to an aging treatment that ultimately induces a transformation from a highly NT structure to a heterogeneous nanostructured material with a unique and complex gradient grain topology. The heterogeneous microstructure contains domains consisting of columnar NTs, nano-grains, and abnormally large grains with diameters greater than 1 µm, M23C6 and δ precipitates. In this presentation, the microstructure of distinct domains with variable grain size, precipitate formation, and morphologies will be discussed and connected to accelerated materials discovery using combinatorial and high throughput characterization techniques. |