About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
A Framework to Solve the Inverse “Process-Structure” Problem of Identifying Process Parameters to Produce a Target Microstructure |
Author(s) |
Dung-Yi Wu, Todd Hufnagel |
On-Site Speaker (Planned) |
Dung-Yi Wu |
Abstract Scope |
Although the “forward” problem of predicting what microstructure will result from a given processing schedule is solvable using deterministic models, the “inverse” problem of determining what processing is required to produce a desired microstructure is much more challenging, typically requiring multiple iterative cycles of experimentation and simulation". There is a need for a smarter way to navigate through a high-dimensional processing parameter space to find the combination of parameters to achieve a microstructure that satisfies multiple, quantitative descriptors.
In this work, we apply a Bayesian Optimization framework based on Gaussian Process Regression to efficiently explore parameter space in the context of heat treatment of aluminum alloys to maximize resistance to spall failure. We demonstrate the utility of our approach through a case study that controls the volume fraction of Al7Cu2Fe second-phase particles and aluminum grain size distribution in a commercial aluminum 7085 alloy. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Modeling and Simulation, Aluminum |