About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Fatigue and Fracture
|
| Presentation Title |
High-Throughput Characterization of the Effects of Surface Treatments on Additively Manufactured Metals Using Vision Transformers |
| Author(s) |
Can Uysalel, Jackelin Cotrina, Alexander Haken, Maziar Ghazinejad |
| On-Site Speaker (Planned) |
Can Uysalel |
| Abstract Scope |
Post-sintering surface treatments, particularly shot peening, can alter defect-induced fatigue behavior in additively manufactured (AM) steels. Automated, high-throughput metrology is essential to understand the relationship between surface treatments, defects, and their impact on fatigue. In this study, we collected a library of optical microscopy (OM) and scanning electron microscopy (SEM) images and trained Vision Transformers (ViTs) to detect and characterize defects in Direct Metal Laser Sintering (DMLS)-printed 316L stainless steels. To evaluate the dynamic behavior of 3D-printed steels, we used Moore fatigue test and corroborated our model with experimental data. It was observed that surface defects are key parameters controlling the fatigue response by acting as stress concentrators. The results establish a quantitative link between microstructural attributes and macro-scale properties in AM metals, demonstrating that our ML-assisted automated characterization technique can enhance predictive modeling of the mechanical performance of AM metals and optimize quality control processes. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Characterization, Machine Learning |