{"ID":2898935,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03177","arxiv_id":"2507.03177","title":"First Contact: Data-driven Friction-Stir Process Control","abstract":"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.","short_abstract":"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 u...","url_abs":"https://arxiv.org/abs/2507.03177","url_pdf":"https://arxiv.org/pdf/2507.03177v1","authors":"[\"James Koch\",\"Ethan King\",\"WoongJo Choi\",\"Megan Ebers\",\"David Garcia\",\"Ken Ross\",\"Keerti Kappagantula\"]","published":"2025-07-03T21:09:46Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.LG\"]","methods":"[]","has_code":false}