用数据驱动的简化模型，高速精准预测金属3D打印变形。
Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing
- 结合主成分分析与高斯过程，构建参数化降阶模型
- 预测误差在±0.001mm内，计算速度提升1800倍
- 适合需要快速优化打印参数的工业场景
在激光粉末床熔融（LPBF）过程中，激光能量引发高热梯度，导致零件最终变形不可接受。准确预测变形对优化3D打印工艺、实现几何精度至关重要。本研究提出一种数据驱动的参数化降阶模型（ROM），用于预测不同设备工艺设置下的变形。所提出的框架结合了本征正交分解（POD）与高斯过程回归（GPR），并对比了基于深度学习的参数化图卷积自编码器（GCA）。POD-GPR模型表现出高精度，预测误差控制在±0.001mm以内，并实现约1800倍的计算加速。
原文摘要 · Abstract (English)
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.
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