AI 中文总结
针对LPBF中温度稳定难题，提出双环数据驱动控制策略，整合层内线性输出反馈与层间前馈控制，经策略梯度优化增益。仿真与实验表明该策略有效，能降低跟踪误差与输入约束违规，还发现新动力学，是模拟到现实策略优化在LPBF的成功应用。
AI 中文摘要
激光粉末床熔融（LPBF）是一种金属增材制造工艺，温度稳定对于避免诸如变形和开裂等缺陷至关重要。现有控制方法需要手动调整，在打印复杂几何形状时增加了零件失败的风险。本文介绍了一种双环、数据驱动的控制策略来稳定表面温度，确保在存在干扰的情况下具有鲁棒性和接近最优的性能。该方法整合了：（i）通过策略梯度优化增益的层内线性输出反馈控制；（ii）结合温度轨迹优化和迭代学习控制的层间前馈控制。仿真结果表明，即使在显著的模型失配和测量噪声下，多尺度控制器也能有效稳定温度。实验结果表明，该方法的简化硬件受限版本与原位数据驱动方法的当前最优性能相匹配，相对于贝叶斯优化调整的基线，平均跟踪误差降低了3.4%，平均输入约束违规降低了47.5%。实验还展示了一类新的高频激励动力学，可减少矢量头部肿胀，为增材制造领域开辟了新的研究途径。这项工作标志着模拟到现实策略优化在LPBF工艺中的首批成功应用之一。
英文摘要
Laser powder bed fusion (LPBF) is a metal additive manufacturing process where temperature stabilization is of vital importance to avoid defects such as distortion and cracking. Existing control methods require manual tuning, increasing the risk of part failure when printing complex geometries. This paper introduces a dual-loop, data-driven control strategy to stabilize the surface temperature, ensuring robustness and near-optimal performance in the presence of disturbances. The proposed method integrates (i) an in-layer linear output feedback control with gains optimized through policy gradient, and (ii) a layer-to-layer feedforward control combining temperature trajectory optimization and iterative learning control. Simulation results show that the multi-scale controller effectively stabilizes the temperature even under significant model mismatch and measurement noise. Experimental results demonstrate that a simplified, hardware-constrained version of this method matches the state-of-the-art performance of in-situ data-driven methods, reducing mean tracking error by 3.4% and mean input-constraint violation by 47.5% relative to a Bayesian Optimization-tuned baseline. For this physical LPBF validation, the controller is tuned entirely offline using uncontrolled print data from a single calibration layer. Our experiments also demonstrate a new class of high-frequency excitation dynamics that result in reduced vector head swelling, opening up new avenues of research in the additive manufacturing community. This work marks one of the first successful applications of sim-to-real policy optimization in LPBF processes.
