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arXiv · 2507.03177

First Contact: Data-driven Friction-Stir Process Control

James Koch·Ethan King·WoongJo Choi·Megan Ebers·David Garcia·Ken Ross·Keerti Kappagantula
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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.

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James Koch, Ethan King, WoongJo Choi, Megan Ebers, David Garcia, Ken Ross, Keerti Kappagantula. 2025-07-03. First Contact: Data-driven Friction-Stir Process Control. https://arxiv.org/abs/2507.03177

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