Do We Need a New Foundation to Use Deep Learning to Monitor Weld Penetration?
Author:
Affiliation:
1. Department of Mathematics, University of Kentucky, Lexington, KY, USA
2. Department of Electrical and Computer Engineering and Institute for Sustainable Manufacturing, University of Kentucky, Lexington, KY, USA
Funder
National Science Foundation
Institute for Sustainable Manufacturing
Department of Electrical and Computer Engineering
Department of Mathematics at the University of Kentucky
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering
Link
https://ieeexplore.ieee.org/ielam/7083369/10102643/10107697-aam.pdf
Reference25 articles.
1. Deep-learning-based real-time monitoring of full-penetration laser keyhole welding by using the synchronized coaxial observation method
2. Burn-through prediction and weld depth estimation by deep learning model monitoring the molten pool in gas metal arc welding with gap fluctuation
3. Adaptive Intelligent Welding Manufacturing
4. How to Accurately Monitor the Weld Penetration From Dynamic Weld Pool Serial Images Using CNN-LSTM Deep Learning Model?
5. Detecting dynamic development of weld pool using machine learning from innovative composite images for adaptive welding
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2. Modeling imaged welding process dynamic behaviors using Generative Adversarial Network (GAN) for a new foundation to monitor weld penetration using deep learning;Journal of Manufacturing Processes;2024-08
3. Penetration prediction of narrow-gap laser welding based on coaxial high dynamic range observation and machine learning;Journal of Manufacturing Processes;2024-01
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