Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing

• URL: http://arxiv.org/abs/2412.04577v1
• Date: Thu, 05 Dec 2024 19:47:25 GMT
• Title: Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing
• Authors: Indu Kant Deo, Youngsoo Choi, Saad A. Khairallah, Alexandre Reikher, Maria Strantza,
• Abstract summary: This study introduces data-driven parameterized reduced-order models (ROMs) to predict distortion in Laser Powder Bed Fusion (LPBF) 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.
• Score: 39.58317527488534
• License:
• Abstract: 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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