Data Driven Insights into Composition Property Relationships in FCC High Entropy Alloys
This work addresses the problem of limited predictive modeling for HEAs in aerospace, automotive, and defense industries, but it is incremental as it builds on existing methods with specific improvements.
The study tackled the challenge of predicting mechanical properties in FCC High Entropy Alloys (HEAs) by developing encoder-decoder models that map alloy composition to properties, achieving competitive or superior performance compared to conventional regressors, especially for yield strength and the UTS/YS ratio.
Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors, including aerospace, automotive, and defense industries. However, the scarcity of integrated chemistry, process, structure, and property data presents significant challenges for predictive property modeling. Given the vast design space of these alloys, uncovering the underlying patterns is essential yet difficult, requiring advanced methods capable of learning from limited and heterogeneous datasets. This work presents several sensitivity analyses, highlighting key elemental contributions to mechanical behavior, including insights into the compositional factors associated with brittle and fractured responses observed during nanoindentation testing in the BIRDSHOT center NiCoFeCrVMnCuAl system dataset. Several encoder decoder based chemistry property models, carefully tuned through Bayesian multi objective hyperparameter optimization, are evaluated for mapping alloy composition to six mechanical properties. The models achieve competitive or superior performance to conventional regressors across all properties, particularly for yield strength and the UTS/YS ratio, demonstrating their effectiveness in capturing complex composition property relationships.