用神经微分方程实现搅拌摩擦加工的温度闭环控制。
First Contact: Data-driven Friction-Stir Process Control
- 结合数据驱动与热传导模型，预测工具温度变化
- 实验证明可快速达到目标温度，保持工艺一致性
- 适合智能制造与精密加工领域研究者参考
本研究验证了神经集总参数微分方程在搅拌摩擦加工（FSP）下压阶段开环设定点控制中的应用。该方法将数据驱动框架与经典热传导技术相结合，用于预测工具温度，进而指导控制策略。通过训练好的神经集总参数微分方程模型，将理论预测转化为实际设定点控制，实现工具温度的快速稳定，并确保FSP过程中热力状态的一致性。研究涵盖了控制方法的设计、实现及实验验证，为高效、自适应的FSP操作奠定了基础。
原文摘要 · Abstract (English)
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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