A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks
📰 ArXiv cs.AI
Learn to predict laser welding penetration using physics-informed neural networks and self-supervised learning for improved weld quality
Action Steps
- Build a physics-informed neural network using SimPhysNet algorithm
- Train the model using self-supervised learning with a limited number of labelled images
- Configure the model to predict welding penetration state
- Test the model using experimental data
- Apply the model to real-world laser welding processes to improve weld quality
Who Needs to Know This
Researchers and engineers in the field of laser welding can benefit from this model to improve weld quality and reduce defects. This can be applied in industries such as automotive, aerospace, and manufacturing
Key Insight
💡 Physics-informed neural networks can accurately predict laser welding penetration using self-supervised learning with limited labelled data
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🔍 Predict laser welding penetration with SimPhysNet, a novel physics-informed neural network algorithm 🚀
Key Takeaways
Learn to predict laser welding penetration using physics-informed neural networks and self-supervised learning for improved weld quality
Full Article
Title: A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks
Abstract:
arXiv:2606.26059v1 Announce Type: cross Abstract: The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. Accurate prediction of the penetration state is therefore essential for ensuring weld quality. To this end, this paper introduces SimPhysNet, a novel algorithm that achieves high classification accuracy in laser welding penetration prediction using only a limited number of labelled images. This app
Abstract:
arXiv:2606.26059v1 Announce Type: cross Abstract: The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. Accurate prediction of the penetration state is therefore essential for ensuring weld quality. To this end, this paper introduces SimPhysNet, a novel algorithm that achieves high classification accuracy in laser welding penetration prediction using only a limited number of labelled images. This app
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