双激光粉末床熔融过程中冷却速率的迭代学习控制
Iterative Learning Control of the Cooling Rate in a Dual-Laser Powder Bed Fusion Process
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中文总结 AI 辅助
针对双激光LPBF工艺，提出基于优化的迭代学习控制器，结合仿真与实验校正参数误差，将设备成本降低超一个数量级。
中文摘要 AI 辅助
激光粉末床熔融（LPBF）增材制造工艺中熔池的热历史决定了凝固微观结构以及最终3D打印部件的机械性能。双激光系统通过重新加热熔池后方的材料，为控制冷却曲线提供了额外的自由度，但由于该工艺的复杂物理特性，校准工艺参数颇具挑战性。我们提出了一种基于优化的迭代学习控制器，通过明智地结合仿真与实验来确定最优的功率、速度和偏移设置：模型提供搜索方向，而从真实设备实验获得的反馈则校正参数误差，从而在模型不准确的情况下也能实现收敛。该方法在仿真中得到了验证，使用高保真热模型作为设备替代模型，并在设备与模型之间故意设置了吸收系数、潜热处理和粉末床有效导热率的不匹配。结果表明，该控制器将设备成本降低了超过一个数量级，并在不同随机种子下达到了一组紧密的低成本解，而仅依赖模型的前馈优化虽然在替代模型上看似收敛，却停滞在显著更高的设备成本上。
英文摘要
The thermal history of the melt pool in laser powder bed fusion (LPBF) additive manufacturing processes governs the solidification microstructure and the mechanical properties of the resulting 3D-printed parts. Dual-laser systems offer additional degrees of freedom to control the cooling profile by reheating material behind the melt pool, but calibrating process parameters is challenging due to the complex physics of the process. We present an optimization-based iterative learning controller that determines optimal power, velocity, and offset settings by judiciously combining simulations and experiments: the model supplies search directions while feedback obtained from experiments on the real plant corrects for parameter errors, enabling convergence despite model inaccuracies. The approach is validated in simulation using a high-fidelity thermal model as a plant surrogate, with deliberate mismatches in absorption coefficient, latent heat treatment, and powder-bed effective conductivity between plant and model. Results show that the controller drives the plant cost down by over an order of magnitude and reaches a tight band of low-cost solutions across seeds, while model-only feedforward optimization stalls at a substantially higher plant cost despite appearing to converge on the surrogate.
发表机构
- University of British Columbia（不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
