First Contact: Data-driven Friction-Stir Process Control

TL;DR

This paper introduces a data-driven control method using Neural Lumped Parameter Differential Equations to precisely manage tool temperatures during Friction Stir Processing, enhancing process consistency and efficiency.

Contribution

It presents a novel integration of neural differential equations with classical heat transfer models for real-time FSP control, validated through experiments.

Findings

Accurate prediction of tool temperatures using neural models.

Effective open-loop setpoint control for FSP.

Enhanced process stability and repeatability.

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.