# Predicting Defects in Laser Powder Bed Fusion using in-situ Thermal Imaging Data and Machine Learning

arXiv: https://arxiv.org/abs/2112.11212 · HTML: https://mlsanity.com/paper/2112.11212/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 16 Dec 2021.

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

- [A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing](https://mlsanity.com/paper/2101.05605/) (2021)
- [Impact of Surface and Pore Characteristics on Fatigue Life of Laser Powder Bed Fusion Ti-6Al-4V Alloy Described by Neural Network Models](https://mlsanity.com/paper/2109.09655/) (2021)

## Built on by (most cited first, 6 of 6)

- [AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing](https://mlsanity.com/paper/2406.00031/) (2024)
- [Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics](https://mlsanity.com/paper/2307.12304/) (2023)
- [Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing](https://mlsanity.com/paper/2408.05307/) (2024)
- [Reference Dataset and Benchmark for Reconstructing Laser Parameters from On-axis Video in Powder Bed Fusion of Bulk Stainless Steel](https://mlsanity.com/paper/2409.12475/) (2024)
- [Predicting Stress and Damage in Carbon Fiber-Reinforced Composites Deformation Process using Composite U-Net Surrogate Model](https://mlsanity.com/paper/2504.14143/) (2025)
- [Scalable AI Framework for Defect Detection in Metal Additive Manufacturing](https://mlsanity.com/paper/2411.00960/) (2024)

## Abstract (arXiv)

Variation in the local thermal history during the laser powder bed fusion (LPBF) process in additive manufacturing (AM) can cause microporosity defects. in-situ sensing has been proposed to monitor the AM process to minimize defects, but the success requires establishing a quantitative relationship between the sensing data and the porosity, which is especially challenging for a large number of variables and computationally costly. In this work, we develop machine learning (ML) models that can use in-situ thermographic data to predict the microporosity of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold (/tau) and the maximum radiance (T_{max}). These features are computed, stored for each voxel in the built material, are used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for random forests. Feature importance analysis based on the ML models shows that T_{max}is more important to the voxel state than /tau. The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it.