Summary of Data-driven, Parameterized Reduced-order Models For Predicting Distortion in Metal 3d Printing, by Indu Kant Deo et al.
Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing
by Indu Kant Deo, Youngsoo Choi, Saad A. Khairallah, Alexandre Reikher, Maria Strantza
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to predict distortion in Laser Powder Bed Fusion (LPBF) processes, which is crucial for optimizing the manufacturing process and achieving high geometric accuracy. The authors introduce data-driven parameterized reduced-order models (ROMs) that combine Proper Orthogonal Decomposition (POD) with Gaussian Process Regression (GPR). They compare the performance of their proposed ROM framework to a deep-learning based parameterized graph convolutional autoencoder (GCA). The results show that the POD-GPR model achieves high accuracy, predicting distortions within , and offers a significant computational speed-up of approximately 1800x. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper uses special math to predict how laser powder bed fusion processes might go wrong, so manufacturers can make better parts. The team tried out different approaches and found one that works really well, being super accurate and fast too! |
Keywords
» Artificial intelligence » Autoencoder » Deep learning » Regression
