AI 中文总结
研究激光热处理中峰值表面温度控制问题，核心方法是开发基于多步深度算子网络的替代模型控制管道，贡献在于验证该管道，探究其分布外极限，展示开环与闭环精度差异及数据设计效果。
AI 中文摘要
基于激光的热处理，如激光粉末床熔合，需要严格调节峰值表面温度：移动热源重新进入先前加热的材料时会积累热量，使温度超出工艺窗口并导致缺陷。高保真热模型能捕捉这种物理现象，但对于在线优化来说太慢，这促使人们开发快速、可微且通用的替代模型。我们开发并验证了一个完整的基于替代模型的控制管道，用于调节304不锈钢基板上移动激光的最大表面温度。我们还通过探究其分布外极限来确定在控制回路中可以信任替代模型的条件。替代模型的一个关键组件是为移动源定制的多步深度算子网络：其分支子网对未来的功率和轨迹（位置和速度）序列进行编码，而其主干对当前的峰值温度和未来激光位置处的温度进行编码，从而实现一次性五步预测。举例来说，我们将这个替代模型用作后退时域模型预测控制器中的平滑（代数整流）非线性程序，该控制器在CasADi/IPOPT中求解。替代模型的前向传递比等效的有限差分步骤快一千多倍。我们表明，总体开环精度对于控制准备来说是必要的但不是充分的：两个离线误差几乎相同的替代模型在闭环中的行为却大不相同。一种受控的双集合数据设计将91K的路径角预测不足故障降低到了1.4K，并且13K的校准单边约束裕度使得在所有测试路径上都没有违反真实上限。
英文摘要
Laser-based thermal processing, such as laser powder bed fusion, requires tight regulation of the peak surface temperature: heat accumulates where the moving source re-enters previously heated material, driving the temperature out of its process window and causing defects. High-fidelity thermal models capture this physics but are too slow for online optimization, which motivates fast, differentiable, and generalizable surrogates. We develop and validate a complete surrogate-based control pipeline that regulates the maximum surface temperature of a moving laser on a 304-stainless-steel substrate. We also determine conditions under which our surrogate can be trusted inside the control loop by probing its out-of-distribution limits. A key component of our surrogate is a multi-step deep operator network bespoke for moving sources: its branch subnetwork encodes the future power and trajectory (position and velocity) sequence, while its trunk encodes the current peak temperature and the temperature at the future laser locations, yielding a one-shot five-step prediction. By way of illustration, we use this surrogate as a smooth (algebraic-rectifier) nonlinear program inside a receding-horizon model predictive controller solved in CasADi/IPOPT. The surrogate forward pass is over thousand times faster than the equivalent finite-difference steps. We show that aggregate open-loop accuracy is necessary but not sufficient for control-readiness: two surrogates with near-identical offline error behave drastically differently in closed loop. A controlled two-ensemble data design reduces a 91 K path-corner underprediction failure to 1.4 K, and a calibrated one-sided constraint margin of 13 K yields zero violations of the true upper bound on all tested paths.
Comments32 pages, 20 figrues
