# Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion

arXiv: https://arxiv.org/abs/2311.16168 · HTML: https://mlsanity.com/paper/2311.16168/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 16 of its 59 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow](https://mlsanity.com/paper/2109.03697/) (2021)
- [A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction](https://mlsanity.com/paper/2211.14680/) (2022)
- [Spectral Regularization for Combating Mode Collapse in GANs](https://mlsanity.com/paper/1908.10999/) (2019)
- [Lossless Image Compression through Super-Resolution](https://mlsanity.com/paper/2004.02872/) (2020)
- [MGGAN: Solving Mode Collapse using Manifold Guided Training](https://mlsanity.com/paper/1804.04391/) (2018)
- [MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning](https://mlsanity.com/paper/2201.11662/) (2022)
- [Graph Convolutional Neural Networks for Body Force Prediction](https://mlsanity.com/paper/2012.02232/) (2020)
- [Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.03355/) (2021)
- [Surrogate Modeling of Melt Pool Thermal Field using Deep Learning](https://mlsanity.com/paper/2207.12259/) (2022)
- [Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression](https://mlsanity.com/paper/1807.00270/) (2018)

## Built on by (most cited first, 11 of 11)

- [Large Language Model Agent as a Mechanical Designer](https://mlsanity.com/paper/2404.17525/) (2024)
- [LLM-3D Print: Large Language Models To Monitor and Control 3D Printing](https://mlsanity.com/paper/2408.14307/) (2024)
- [Generative Learning for Forecasting the Dynamics of Complex Systems](https://mlsanity.com/paper/2402.17157/) (2024)
- [Airfoil Diffusion: Denoising Diffusion Model For Conditional Airfoil Generation](https://mlsanity.com/paper/2408.15898/) (2024)
- [ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning](https://mlsanity.com/paper/2404.16882/) (2024)
- [Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers](https://mlsanity.com/paper/2404.17699/) (2024)
- [Point-wise Diffusion Models for Physical Systems with Shape Variations: Application to Spatio-temporal and Large-scale system](https://mlsanity.com/paper/2508.01230/) (2025)
- [Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion](https://mlsanity.com/paper/2405.07823/) (2024)
- [Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring](https://mlsanity.com/paper/2409.13171/) (2024)
- [Coupled Diffusion-Encoder Models for Reconstruction of Flow Fields](https://mlsanity.com/paper/2601.07946/) (2026)
- [Bayesian Interpolating Neural Network (B-INN): a scalable and reliable Bayesian model for large-scale physical systems](https://mlsanity.com/paper/2601.22860/) (2026)

## Abstract (arXiv)

Defects in laser powder bed fusion (L-PBF) parts often result from the meso-scale dynamics of the molten alloy near the laser, known as the melt pool. For instance, the melt pool can directly contribute to the formation of undesirable porosity, residual stress, and surface roughness in the final part. Experimental in-situ monitoring of the three-dimensional melt pool physical fields is challenging, due to the short length and time scales involved in the process. Multi-physics simulation methods can describe the three-dimensional dynamics of the melt pool, but are computationally expensive at the mesh refinement required for accurate predictions of complex effects, such as the formation of keyhole porosity. Therefore, in this work, we develop a generative deep learning model based on the probabilistic diffusion framework to map low-fidelity, coarse-grained simulation information to the high-fidelity counterpart. By doing so, we bypass the computational expense of conducting multiple high-fidelity simulations for analysis by instead upscaling lightweight coarse mesh simulations. Specifically, we implement a 2-D diffusion model to spatially upscale cross-sections of the coarsely simulated melt pool to their high-fidelity equivalent. We demonstrate the preservation of key metrics of the melting process between the ground truth simulation data and the diffusion model output, such as the temperature field, the melt pool dimensions and the variability of the keyhole vapor cavity. Specifically, we predict the melt pool depth within 3 μ m based on low-fidelity input data 4× coarser than the high-fidelity simulations, reducing analysis time by two orders of magnitude.