First Contact: Data-driven Friction-Stir Process Control
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911038505484288 |
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| author | Koch, James King, Ethan Choi, WoongJo Ebers, Megan Garcia, David Ross, Ken Kappagantula, Keerti |
| author_facet | Koch, James King, Ethan Choi, WoongJo Ebers, Megan Garcia, David Ross, Ken Kappagantula, Keerti |
| contents | This study validates the use of Neural Lumped Parameter Differential Equations for open-loop setpoint control of the plunge sequence in Friction Stir Processing (FSP). The approach integrates a data-driven framework with classical heat transfer techniques to predict tool temperatures, informing control strategies. By utilizing a trained Neural Lumped Parameter Differential Equation model, we translate theoretical predictions into practical set-point control, facilitating rapid attainment of desired tool temperatures and ensuring consistent thermomechanical states during FSP. This study covers the design, implementation, and experimental validation of our control approach, establishing a foundation for efficient, adaptive FSP operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | First Contact: Data-driven Friction-Stir Process Control Koch, James King, Ethan Choi, WoongJo Ebers, Megan Garcia, David Ross, Ken Kappagantula, Keerti Systems and Control Machine Learning This study validates the use of Neural Lumped Parameter Differential Equations for open-loop setpoint control of the plunge sequence in Friction Stir Processing (FSP). The approach integrates a data-driven framework with classical heat transfer techniques to predict tool temperatures, informing control strategies. By utilizing a trained Neural Lumped Parameter Differential Equation model, we translate theoretical predictions into practical set-point control, facilitating rapid attainment of desired tool temperatures and ensuring consistent thermomechanical states during FSP. This study covers the design, implementation, and experimental validation of our control approach, establishing a foundation for efficient, adaptive FSP operations. |
| title | First Contact: Data-driven Friction-Stir Process Control |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2507.03177 |