2024/12/05 by Indu Kant Deo, Youngsoo Choi, Deo, Indu Kant +7 · 1 citation
Engineering · #Additive Manufacturing and 3D Printing Technologies #Computational Engineering #FOS: Computer and information sciences #Finance #Industrial Vision Systems and Defect Detection #Machine Learning (cs.LG) #Manufacturing Process and Optimization #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2412.04577
openalex publication_date 2024/12/05 · openalex created_date 2024/12/10 · openalex updated_date 2026/08/01
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 ±0.001mm, and delivers a computational speed-up of approximately 1800x.