In Laser Powder Bed Fusion (LPBF), the applied laser energy produces high thermal gradients that lead to unacceptable final part distortion. Accurate distortion prediction is essential for optimizing the 3D printing process and manufacturing a part that meets geometric accuracy requirements. This study introduces data-driven parameterized reduced-order models (ROMs) to predict distortion in LPBF across various machine process settings. We propose a ROM framework that combines Proper Orthogonal Decomposition (POD) with Gaussian Process Regression (GPR) and compare its performance against a deep-learning based parameterized graph convolutional autoencoder (GCA). The POD-GPR model demonstrates high accuracy, predicting distortions within $\pm0.001mm$, and delivers a computational speed-up of approximately 1800x.

翻译：在激光粉末床熔融（LPBF）工艺中，施加的激光能量会产生高热梯度，导致最终零件出现不可接受的变形。精确的变形预测对于优化3D打印工艺及制造满足几何精度要求的零件至关重要。本研究引入数据驱动的参数化降阶模型（ROMs）来预测不同机器工艺参数下LPBF过程中的变形。我们提出了一种将本征正交分解（POD）与高斯过程回归（GPR）相结合的ROM框架，并将其性能与基于深度学习的参数化图卷积自编码器（GCA）进行了对比。POD-GPR模型展现出高精度，其变形预测误差在$\pm0.001mm$以内，并实现了约1800倍的计算加速。