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Meeting MS&T22: Materials Science & Technology
Symposium Uncertainty Quantification in Data-Driven Materials and Process Design
Presentation Title Anisotropic Creep Modeling and Uncertainty Quantification of an Electron Beam Melted AM Ni-Based Superalloy
Author(s) Patxi Fernandez-Zelaia, Yousub Lee, Sebastien Dryepondt, Michael Kirka
On-Site Speaker (Planned) Patxi Fernandez-Zelaia
Abstract Scope Electron beam melting powder bed fusion is a promising technology for fabricating high temperature nickel based superalloys. The microstructure exhibits a strong build direction fiber texture which is believed to drive anisotropic creep behavior. In this work we present a crystal plasticity model, which incorporates non-Schmid effects, developed for additively manufactured IN738LC. The probabilistic calibration is performed to quantify model parameter uncertainty. A sequential design approach is utilized to infer the probability density of these constitutive parameters. The established model is utilized to study the behavior of equiaxed grain clusters sometimes observed within in the nominally columnar structure. Uncertainty is propagated from the constitutive model parameters to the full-field response. These features are a source of localized strain accumulation which agrees with experimentally obtained micrographs.
Keywords Ceramics,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Feature-rich Approach to the Characterization of High Temperature, Sulfate-induced Corrosion of Advanced Alloys
Active Learning for Density Functional Theory Simulations with DeepHyper
Anisotropic Creep Modeling and Uncertainty Quantification of an Electron Beam Melted AM Ni-Based Superalloy
Bayesian Calibrated Yield Strength Model for High-entropy Alloys
Bayesian Estimation and Active Learning of Data-driven Interatomic Potentials for Propagation of Uncertainty through Molecular Dynamics
Data-driven Modeling and Control for Temperature-controlled Shear Assisted Processing and Extrusion (ShAPE) using Koopman Operators
Data-driven Structure-property Mapping in Small Data Regime: Towards Increasing Generalizability
Efficient Phase Diagram Determination via Sequential Learning
Enabling the Fourth Paradigm of Multiscale ICME Models through Versatile Gaussian Process and Bayesian Optimization
Learning from Multi-source Scarce Data via Latent Map Gaussian Processes
Machine Learning of Phase Diagrams
Neural Network Surrogate Predictions with Uncertainties for Materials Science
Quantifying Uncertainty in Atomistic Exploration
Solving Stochastic Inverse Problems for Property–structure Linkages Using Data-consistent Inversion and Machine Learning
Thermodynamic Modeling with Uncertainty Quantification and its Implications for Intermetallic Catalysts Design: Application to Pd-Zn-Based Gamma-Brass Phase
Uncertainty Quantification of a High-throughput Local Plasticity Test: Profilometry-based Indentation Plastometry of Al 7075 T6 Alloy
Uncertainty Quantification of Constitutive Models in Crystal Plasticity Finite Element Method
Using Scalable Multi-Objective Bayesian Optimization to Develop Aluminum Scandium Nitride Molecular Dynamics Force Fields

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