Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing
This addresses the need for accurate distortion prediction to optimize 3D printing processes and meet geometric accuracy requirements, representing a domain-specific incremental improvement.
The study tackled the problem of predicting distortion in metal 3D printing (LPBF) by introducing data-driven parameterized reduced-order models, achieving high accuracy with predictions within ±0.001mm and a computational speed-up of about 1800x.
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.