Summary of Deep Learning For Melt Pool Depth Contour Prediction From Surface Thermal Images Via Vision Transformers, by Francis Ogoke et al.
Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers
by Francis Ogoke, Peter Myung-Won Pak, Alexander Myers, Guadalupe Quirarte, Jack Beuth, Jonathan Malen, Amir Barati Farimani
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper introduces a machine learning framework for monitoring Laser Powder Bed Fusion (L-PBF) melt pools using thermal images, which can help predict and prevent defects. A hybrid CNN-Transformer architecture is used to correlate thermal image sequences with melt pool cross-section contours, allowing the model to capture spatial and temporal information. The framework improves upon existing analytical models by modeling the curvature of subsurface melt pool structures in high energy density regimes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In L-PBF, insufficient overlap between melt pools can cause defects and reduce mechanical performance. To address this issue, researchers developed a machine learning framework that uses thermal images to predict melt pool shape. The model combines convolutional neural networks (CNNs) and transformers to analyze thermal image sequences and create detailed maps of melt pool cross-sections. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Transformer
