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arXiv · 2502.13159

Accelerated Fatigue Strength Prediction via Additive Manufactured Functionally Graded Materials and High-Throughput Plasticity Quantification

C. Bean·M. Calvat·Y. Nie·R. L Black·N. Velisavljevic·D. Anjaria·M. A. Charpagne·J. C. Stinville
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Abstract

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.

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C. Bean, M. Calvat, Y. Nie, R. L Black, N. Velisavljevic, D. Anjaria, M. A. Charpagne, J. C. Stinville. 2025-02-16. Accelerated Fatigue Strength Prediction via Additive Manufactured Functionally Graded Materials and High-Throughput Plasticity Quantification. https://arxiv.org/abs/2502.13159

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