论文标题
高斯过程（GP）基于选择性激光熔化过程的学习控制
Gaussian Process (GP)-based Learning Control of Selective Laser Melting Process
论文作者
论文摘要
选择性激光熔化（SLM）是有效金属添加剂制造的新兴过程之一。由于复杂的热量交换和材料相变，精确地对SLM动力学进行建模并设计了对SLM过程的强大控制是一项挑战。在本文中，我们首先介绍了基于数据驱动的高斯过程的SLM过程的动态模型，然后设计模型预测控制以调节熔体池尺寸。控制器设计中考虑了物理和过程约束。通过高保真有限元模拟对学习模型和控制设计进行测试和验证。与其他控制设计的比较结果证明了控制设计的功效。
Selective laser melting (SLM) is one of emerging processes for effective metal additive manufacturing. Due to complex heat exchange and material phase changes, it is challenging to accurately model the SLM dynamics and design robust control of SLM process. In this paper, we first present a data-driven Gaussian process based dynamic model for SLM process and then design a model predictive control to regulate the melt pool size. Physical and process constraints are considered in the controller design. The learning model and control design are tested and validated with high-fidelity finite element simulation. The comparison results with other control design demonstrate the efficacy of the control design.
