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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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525005350 |
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| author | C. Bean M. Calvat Y. Nie R.L. Black N. Velisavljevic D. Anjaria M.A. Charpagne J.C. Stinville |
| author_facet | C. Bean M. Calvat Y. Nie R.L. Black N. Velisavljevic D. Anjaria M.A. Charpagne J.C. Stinville |
| author_sort | C. Bean |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c1c2e7e06b474b0aaa34e79818056f92 |
| institution | DOAJ |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-c1c2e7e06b474b0aaa34e79818056f922025-08-20T03:21:38ZengElsevierMaterials & Design0264-12752025-08-0125611411510.1016/j.matdes.2025.114115Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantificationC. Bean0M. Calvat1Y. Nie2R.L. Black3N. Velisavljevic4D. Anjaria5M.A. Charpagne6J.C. Stinville7Corresponding authors.; University of Illinois Urbana-Champaign, Urbana, USAUniversity of Illinois Urbana-Champaign, Urbana, USAUniversity of Illinois Urbana-Champaign, Urbana, USAUniversity of Illinois Urbana-Champaign, Urbana, USAUniversity of Illinois Urbana-Champaign, Urbana, USAUniversity of Illinois Urbana-Champaign, Urbana, USACorresponding authors.; University of Illinois Urbana-Champaign, Urbana, USACorresponding authors.; University of Illinois Urbana-Champaign, Urbana, USARecent 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.http://www.sciencedirect.com/science/article/pii/S0264127525005350Functionally graded materialsHigh-resolution digital image correlationAdditive manufacturingPlasticityFatigue strength |
| spellingShingle | C. Bean M. Calvat Y. Nie R.L. Black N. Velisavljevic D. Anjaria M.A. Charpagne J.C. Stinville Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantification Materials & Design Functionally graded materials High-resolution digital image correlation Additive manufacturing Plasticity Fatigue strength |
| title | Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantification |
| title_full | Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantification |
| title_fullStr | Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantification |
| title_full_unstemmed | Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantification |
| title_short | Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantification |
| title_sort | accelerated fatigue strength prediction via additive manufactured functionally graded materials and high throughput plasticity quantification |
| topic | Functionally graded materials High-resolution digital image correlation Additive manufacturing Plasticity Fatigue strength |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525005350 |
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