Abstract Scope |
Alloy development and optimization have advanced thanks to multi-scale computer simulations and high-throughput experiments. The recent rise of machine learning has given the materials designers another powerful tool for developing new materials, but much has been undiscovered or utilized beyond research. This work demonstrates how a particular machine learning program is embedded in the traditional alloy development workflow to shorten the timeline and save costs for industry users significantly. Multiple material properties, typically time-consuming to measure or calculate, can be predicted with high accuracy. Unlike other traditional methods, the new alloy development workflow nearly acts as a ‘black box’ process, requiring minimal materials science expertise from users. We will demonstrate alloy development programs in the metal industry that successfully leverage this new toolset. Lastly, we will outline current limitations, including data consolidation, to be addressed in the future to capture the power of machine learning for alloy development fully. |