About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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Additive Manufacturing Fatigue and Fracture: Effects of Surface Roughness, Residual Stress, and Environment
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Presentation Title |
Experiments to Enable Machine Learning of Fatigue Performance in DMLM Ti-6Al-4V with Respect to Microstructure |
Author(s) |
Samuel J. Present, Monica Soare, Johan Reimann, Laura Dial, Kevin J. Hemker |
On-Site Speaker (Planned) |
Samuel J. Present |
Abstract Scope |
Understanding and predicting fatigue performance is paramount for aerospace applications, and rapid qualification and certification of metal alloys for use in cyclic loading environments is necessary for widespread adoption of additively manufactured components. Fatigue studies of additively manufactured metals and alloys have elucidated the fact that surface roughness and microstructural features can profoundly affect fatigue life. In the current study, four-point bending fatigue experiments were employed to identify the number of cycles to, and specific location for, crack nucleation in direct metal laser melted (DMLM) Ti-6Al-4V samples. Cross-correlation with EBSD maps of the underlying microstructure facilitated identification of critical nucleation sites. These experimental results are being used to underpin finite element simulations and to create training sets for expert-informed machine learning protocols, to enable rapid simulation of thin-wall fatigue performance. |
Proceedings Inclusion? |
Planned: |
Keywords |
Titanium, Mechanical Properties, Additive Manufacturing |