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Meeting MS&T22: Materials Science & Technology
Symposium Uncertainty Quantification in Data-Driven Materials and Process Design
Presentation Title Data-driven Modeling and Control for Temperature-controlled Shear Assisted Processing and Extrusion (ShAPE) using Koopman Operators
Author(s) Woongjo Choi, James V. Koch, Ethan King, Colby Wight, Zhao Chen, Erin Barker, Eric Smith, Jenna Pope, Keerti Kappagantula
On-Site Speaker (Planned) Woongjo Choi
Abstract Scope Advanced manufacturing processes often require repetitive trial-and-error attempts to develop a desirable product due to the nascent physics models available to predict relevant microstructural details and properties. Empirical studies are necessary when the processing exhibits nonlinearity and the complexity of the process is prohibitive for analytical modeling and control. However, generating additional data is resource-intensive and time-consuming. In this study, an active learning strategy for data-driven modeling and control using a Koopman operator is implemented for AA7075 tubes synthesized via Shear Assisted Processing and Extrusion (ShAPETM). The complex nonlinear relationships between ShAPE controlled variables (spindle speed and extrusion rate) and resulting extruded temperature are modeled by linear dynamics in a ‘lifted’ space using a Koopman operator approach, then linear control theory is leveraged to construct a model-based controller. The results for an active learning controller are compared with the results from a conventional PID controller.

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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