A multi-task spatiotemporal deep neural network for predicting penetration depth and morphology in laser welding
Quick Answer
This paper introduces a multi-task deep learning model for laser welding that predicts penetration state, depth, and weld seam morphology with high accuracy.
Quick Take
The model achieves 99.35% accuracy for penetration state prediction and a 1.79 mm error for penetration depth, enhancing in-situ quality control in laser welding systems.
Key Points
- The model integrates spatiotemporal features from weld pool images and welding parameters.
- Validation shows 99.35% accuracy in predicting penetration state.
- Penetration depth prediction error is only 1.79 mm.
- Weld cross-section reconstruction accuracy is 95.65%.
- A robust dataset construction method enhances model generalization.
Paper Resources
📖 Reader Mode
~2 min readAbstract:In laser penetration welding, the assessment of penetration state and weld seam morphology plays a crucial role in determining the weld quality. This paper presents a comprehensive introduction of the innovative muti-task deep learning model that has the capability to predict penetration state, depth, and weld seam morphology with high accuracy. The monitoring platform relies on weld pool images captured during the laser welding process using a complementary metal-oxide-semiconductor camera. The proposed model integrates spatiotemporal features extracted from top weld pool images along with welding parameters, establishing a deep learning framework based on convolutional neural networks and state space models for more efficient extraction and processing of spatial-temporal information. Furthermore, a reliable method for constructing the dataset is proposed to enhance both robustness and generalization capability of the developed model. Validation results on the test set demonstrate that prediction accuracy for penetration state can reach 99.35%, while prediction error for penetration depth is 1.79 millimeter, and accuracy of reconstructing the weld cross-section is 95.65%. This study provides new insights and methodologies for in-situ quality control strategies in laser penetration welding systems.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.26260 [cs.CV] |
| (or arXiv:2606.26260v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26260 arXiv-issued DOI via DataCite (pending registration) |
|
| Related DOI: | https://doi.org/10.1016/j.engappai.2025.113641
DOI(s) linking to related resources |
Submission history
From: Sen Li [view email]
[v1]
Wed, 24 Jun 2026 18:02:53 UTC (4,356 KB)
— Originally published at arxiv.org
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