Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913599485640704 |
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| author | Deo, Indu Kant Choi, Youngsoo Khairallah, Saad A. Reikher, Alexandre Strantza, Maria |
| author_facet | Deo, Indu Kant Choi, Youngsoo Khairallah, Saad A. Reikher, Alexandre Strantza, Maria |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04577 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing Deo, Indu Kant Choi, Youngsoo Khairallah, Saad A. Reikher, Alexandre Strantza, Maria Machine Learning Computational Engineering, Finance, and Science 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. |
| title | Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing |
| topic | Machine Learning Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2412.04577 |