First Contact: Data-driven Friction-Stir Process Control
This addresses process control in manufacturing for more efficient FSP operations, but appears incremental as it integrates existing methods.
The study tackled the problem of controlling tool temperatures during Friction Stir Processing by using Neural Lumped Parameter Differential Equations for open-loop setpoint control, resulting in rapid attainment of desired temperatures and consistent thermomechanical states.
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.