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

arXiv: https://arxiv.org/abs/2412.04577 · HTML: https://mlsanity.com/paper/2412.04577/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Virtual Foundry Graphnet for Metal Sintering Deformation Prediction](https://mlsanity.com/paper/2404.11753/) (2024)

## Built on by (most cited first, 1 of 1)

- [Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition](https://mlsanity.com/paper/2504.11433/) (2025)

## Abstract (arXiv)

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.