Recent improvements in additive manufacturing and high-throughput material synthesis have enabled the discovery of novel metallic materials for extreme environments. However, high-fidelity testing of advanced mechanical properties such as fatigue strength, has often been the most time-consuming and resource-intensive step of material discovery, thereby slowing down the adoption of novel materials. This work presents a new method for rapid characterization of the fatigue properties of many compositions while only testing a single specimen. The approach utilizes high-resolution digital image correlation along with a computer vision model to extract the relationship between localized plastic deformation events and associated mechanical properties. The approach is initially validated on an additive manufactured 316L dataset, then applied to a functionally graded additive manufactured specimen with a composition gradient across the gauge length. This allows for the characterization of multiple compositions, orders of magnitude faster than traditional methods.
@article{arxiv.2502.13159,
title = {Accelerated Fatigue Strength Prediction via Additive Manufactured Functionally Graded Materials and High-Throughput Plasticity Quantification},
author = {C. Bean and M. Calvat and Y. Nie and R. L Black and N. Velisavljevic and D. Anjaria and M. A. Charpagne and J. C. Stinville},
journal= {arXiv preprint arXiv:2502.13159},
year = {2025}
}