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Meeting MS&T26: Materials Science & Technology
Symposium Uncertainty Quantification in Ultra-High Temperature Materials Manufacturing
Presentation Title From Design to Melt: Rare Earth Retention in Ni-Based Superalloys
Author(s) Geeta Kumari, Hanna Hefley
On-Site Speaker (Planned) Geeta Kumari
Abstract Scope This study evaluates the retention of rare earth element (REE) additions in a nickel-based superalloy during vacuum induction melting (VIM). Computational modeling guided experimental design; however, results highlight the importance of melt processing conditions. Sixteen REEs were introduced and melted at ~1300 °C for ~1 hour under argon at ~0.5 and ~1.0 atm. At lower pressure, significant losses were observed, particularly for Y and Ce, with most elements exhibiting high volatility and reactivity with the Al₂O₃ crucible. Increasing inert gas pressure reduced losses for several elements and enabled recovery of some additions, though behavior varied across the REE series. Notably, deviations from predicted trends emphasized the role of partial pressure and interfacial reactions in governing stability. These findings demonstrate that REE retention is strongly influenced by gas atmosphere, crucible interactions, and melt history, providing practical guidance for scaling computationally designed alloys to VIM production.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Autonomous Materials Characterization Through Simulation to Experiment Analysis with Continual Deep Learning
Bayesian Design of Experiments for Calphad Modeling
Computational Tools for Predicting High-Temperature Materials Properties via DFT, MD, and Deep Learning
Data to Discovery: A Closed-Loop Ecosystem for Designing Compositionally Complex Alloys
End-to-End Machine Learning for Creep Modeling: Data Processing, Parameter Learning, and Uncertainty Analysis
From Design to Melt: Rare Earth Retention in Ni-Based Superalloys
From Dirty Processing to Enhanced Performance: Hidden Variables for Strength Consistency in UHTCs
Generalization of a Crystal Plasticity Model from Grade 91 to Grade 92 Steel: A Coupled High-Throughput Constitutive Model and Data-Driven Analysis Approach
MXene and Polymer Derived TiC–SiC Ceramics with Enhanced Electrical Conductivity and Tailored Thermal–Mechanical Performance for High-Temperature Applications
UHTM and the Materials R&D Landscape
Uncertainty-Guided Experimental Determination of Phase Diagrams
Uncertainty Quantification of In-Situ Densification of Polymer-Derived Ceramics
Uncertainty Quantification via Deep Kernel Learning on Synchrotron Diffraction Patterns

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