About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Data-enhanced Hybrid Machine Learning Model for Solid-state Friction Surfacing Process |
Author(s) |
Benjamin Klusemann, Frederic E. Bock, Zina Kallien, Norbert Huber |
On-Site Speaker (Planned) |
Benjamin Klusemann |
Abstract Scope |
Friction surfacing is a solid-state layer deposition process to additively manufacture structures of metallic materials. Since materials remain in solid state, deteriorations of microstructural characteristics and defect formations typical for fusion-based processes are circumvented. Thermal process histories can be determined with solid heat-transfer finite element models to identify and utilize relationships between process parameters and properties. Such models are computationally efficient and convenient; however, require final deposit geometries a priori, which makes experimental measurements prior to simulations almost inevitable. The aim of the proposed data-enhanced hybrid machine learning approach is to reduce the amount of those experiments to a minimum via exploiting links between process parameters, substrate and backing materials as well as maximum process temperatures and resulting deposit geometries. Initial experimental data is used to validate numerical models which are data-mined to feed a machine learning framework that can ultimately perform predictions for different process parameter and material combinations. |
Proceedings Inclusion? |
Planned: |
Keywords |
Aluminum, Machine Learning, Modeling and Simulation |