About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing: Materials Design and Alloy Development VII – Design With Multi-Modal and Field Data by Integrating Uncertainty
|
| Presentation Title |
High-Throughput Time-Temperature-Hardness/Transformation Dataset Generation for Laser Powder Bed Fusion Alloy 718 |
| Author(s) |
William Frieden Templeton, Jacob McCauley, Sneha Prabha Narra |
| On-Site Speaker (Planned) |
Sneha Prabha Narra |
| Abstract Scope |
We use a Gleeble thermal simulator to perform high-throughput heat treatments on laser powder bed fusion Alloy 718, systematically varying temperature and dwell time. A thermal model, calibrated with welded thermocouple measurements, determines the steady-state temperature distribution along each sample and augments the in-situ temperature measurements. Ex-situ characterization focuses on hardness data from the entire sample surface, combined with scanning electron microscopy of select regions. The resulting thermal simulations and experimental data are used to construct a time-temperature-hardness dataset for Alloy 718, which is then converted to a time-temperature-transformation (TTT) dataset under defined assumptions for strength contributors. Isothermal transformations are modeled using the Johnson–Mehl–Avrami–Kolmogorov (JMAK) equation, where we compare uncertainties and systematic differences between model fitting methods. This workflow enables efficient data collection for varying time and temperature conditions and highlights opportunities to further improve sample design and data analysis for robust process-property modeling. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Copper / Nickel / Cobalt, ICME |