|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Machine Intuitive Development of Army Steels - MIDAS
||Heather Murdoch, Levi McClenny, Benjamin Szajewski, Daniel Field, Berend Chris Rinderspacher, Mulugeta Haile, Krista Limmer, Andrew Garza
|On-Site Speaker (Planned)
Recent advances in data science and high-throughput materials simulations are being evaluated to accelerate advanced steel alloy development. Here we use machine learning (ML) to take advantage of the large amounts of historic data available for martensitic steels. A series of methods using varying amounts of data are used to develop predictive ML models. Our hypothesis is that the composition and processing variables are insufficient to inform a robust machine learning model, and the incorporation of CALPHAD inputs will provide the additional information required to inform the models and make a more robust framework that is generalizable to other systems. These intermediate variables are synthetic microstructures and thermodynamic properties generated using high-throughput CALPHAD simulations. We present a preliminary integrated machine learning and alloy design workflow based on steel hardness and toughness response during tempering. Model uncertainty and databasing challenges will also be discussed.