Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantification
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...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Materials & Design |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525005350 |
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| Summary: | 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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| ISSN: | 0264-1275 |