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.
Cite
@article{arxiv.2507.03177,
title = {First Contact: Data-driven Friction-Stir Process Control},
author = {James Koch and Ethan King and WoongJo Choi and Megan Ebers and David Garcia and Ken Ross and Keerti Kappagantula},
journal= {arXiv preprint arXiv:2507.03177},
year = {2025}
}