Summary of Binocular Model: a Deep Learning Solution For Online Melt Pool Temperature Analysis Using Dual-wavelength Imaging Pyrometry, by Javid Akhavan et al.
Binocular Model: A deep learning solution for online melt pool temperature analysis using dual-wavelength Imaging Pyrometry
by Javid Akhavan, Chaitanya Krishna Vallabh, Xiayun Zhao, Souran Manoochehri
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 A crucial challenge in metal Additive Manufacturing (AM) is monitoring the Melt Pool (MP) temperature in real-time. Traditional methods are slow and require manual effort to produce actionable insights. To address this, we propose an AI-based solution that reduces manual data processing and improves efficiency. Our study utilizes a dataset of dual-wavelength process monitoring data and corresponding temperature maps. We introduce the Binocular model, a deep learning approach that analyzes MP temperature in Laser Powder Bed Fusion (L-PBF) using dual input observations. This model achieves high accuracy in temperature estimation (0.95 R-squared score) while processing up to 750 frames per second, approximately 1000 times faster than conventional methods. Our work addresses real-time MP temperature monitoring and contributes to the advancement of metal AM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine building things layer by layer using a special machine that melts metal powder together. This process is called Additive Manufacturing (AM). But, it’s hard to control this process in real-time because the temperature needs to be just right. If it’s not, the final product might not turn out well. To solve this problem, we created an AI-powered tool that can quickly and accurately measure the temperature of the melted metal. This tool is much faster than traditional methods, which makes it useful for controlling the process in real-time. Our tool helps make sure that the products built with this technology are high-quality and consistent. |
Keywords
» Artificial intelligence » Deep learning » Temperature
