Laser powder bed fusion (LPBF) is an additive manufacturing technique that has gained popularity thanks to its ability to produce geometrically complex, fully dense metal parts. However, these parts are prone to internal defects and geometric inaccuracies, stemming in part from variations in the melt pool. This paper proposes a novel vector-level feedforward control framework for regulating melt pool area in LPBF. By decoupling part-scale thermal behavior from small-scale melt pool physics, the controller provides a scale-agnostic prediction of melt pool area and efficient optimization over it. This is done by operating on two coupled lightweight models: a finite-difference thermal model that efficiently captures vector-level temperature fields and a reduced-order, analytical melt pool model. Each model is calibrated separately with minimal single-track and 2D experiments, and the framework is validated on a complex 3D geometry in both Inconel 718 and 316L stainless steel. Results showed that feedforward vector-level laser power scheduling reduced geometric inaccuracy in key dimensions by 62%, overall porosity by 16.5%, and photodiode variation by 6.8% on average. Overall, this modular, data-efficient approach demonstrates that proactively compensating for known thermal effects can significantly improve part quality while remaining computationally efficient and readily extensible to other materials and machines.

翻译：激光粉末床熔融（LPBF）是一种增材制造技术，因其能够生产几何形状复杂、完全致密的金属零件而广受欢迎。然而，这些零件容易产生内部缺陷和几何误差，部分原因在于熔池的变化。本文提出了一种新颖的矢量级前馈控制框架，用于调控LPBF中的熔池面积。通过将零件尺度的热行为与小尺度熔池物理过程解耦，该控制器提供了与尺度无关的熔池面积预测，并在此基础上进行高效优化。这是通过操作两个耦合的轻量化模型实现的：一个高效捕捉矢量级温度场的有限差分热模型，以及一个降阶解析熔池模型。每个模型均通过最小化的单道和二维实验分别校准，该框架在Inconel 718和316L不锈钢的复杂三维几何结构上得到了验证。结果表明，前馈矢量级激光功率调度使关键尺寸的几何误差平均减少了62%，总体孔隙率降低了16.5%，光电二极管信号波动平均减少了6.8%。总体而言，这种模块化、数据高效的方法表明，主动补偿已知的热效应可以显著提高零件质量，同时保持计算效率，并易于扩展到其他材料和设备。