About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
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Symposium
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Uncertainty Quantification in Data-Driven Materials and Process Design
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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. |