| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Fatigue and Fracture: Developing Predictive Capabilities
|
| Presentation Title |
Experiments to Enable Expert-informed Machine Learning of Fatigue Performance of DMLM Ti-6Al-4V |
| Author(s) |
Samuel J. Present, Laura Dial, Thomas Straub, Chris Eberl, Kevin 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, resonate high-cycle micro-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 nanoCT scans and EBSD maps, of surface roughness and 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 |
Additive Manufacturing, Mechanical Properties, Characterization |