About this Abstract |
| Meeting |
11th International Symposium on Superalloy 718 and Derivatives 2026: Legacy, Innovations, and Future Directions
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| Symposium
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Superalloy 718 and Derivatives 2026: Legacy, Innovations, and Future Directions
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| Presentation Title |
Framework for Incorporation of Manufacturing Process Variability in Grain Size Predictions for Alloy 718 Forgings |
| Author(s) |
Cameron St. Clair, Kory Chang, Benjamin Tarrence, Jacob Albrecht, Kevin R. Severs |
| On-Site Speaker (Planned) |
Cameron St. Clair |
| Abstract Scope |
Accurate prediction of grain size in Alloy 718 forgings is critical for balancing property requirements to meet aerospace material specifications. A computationally efficient workflow has been developed for integrating manufacturing process variability into grain size predictions. Initially, grain size predictions are generated under nominal thermomechanical conditions using a Johnson-Mehl-Avrami-Kolmogorov (JMAK) model embedded within a finite element analysis (FEA) framework. To account for process variability, a space-filling Design of Experiments (DoE) was conducted using a Latin hypercube sampling strategy across key input parameters, including temperature, strain rate, and duration of various process steps. The resulting grain size outputs from these simulations were used to train a surrogate model that accurately replicates the FEA response with significantly reduced computational cost. Historical production data were then analyzed to construct statistically representative input distributions, capturing real-world manufacturing variability. These distributions served as the basis for a Monte Carlo simulation leveraging the surrogate model to estimate the probability of the process producing grain sizes within specification limits. This probabilistic approach enables a quantitative assessment of process robustness and provides actionable insights for process control and optimization. This work demonstrates how advanced modeling techniques can be coupled with historical data analytics to better predict and manage metallurgical outcomes under realistic production conditions, thereby ensuring the robustness of the forging process design. |
| Proceedings Inclusion? |
Definite: At-meeting proceedings |