Penetration State Recognition during Laser Welding Process Control Based on Two-Stage Temporal Convolutional Networks Article Swipe
Zhihui Liu
,
Shuai Ji
,
Chunhui Ma
,
Chengrui Zhang
,
Hongjuan Yu
,
Yisheng Yin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ma17184441
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ma17184441
Vision-based laser penetration control has become an important research area in the field of welding quality control. Due to the complexity and large number of parameters in the monitoring model, control of the welding process based on deep learning and the reliance on long-term information for penetration identification are challenges. In this study, a penetration recognition method based on a two-stage temporal convolutional network is proposed to realize the online process control of laser welding. In this paper, a coaxial vision welding monitoring system is built. A lightweight segmentation model, based on channel pruning, is proposed to extract the key features of the molten pool and the keyhole from the clear molten pool keyhole image. Using these molten pool and keyhole features, a temporal convolutional network based on attention mechanism is established. The recognition method can effectively predict the laser welding penetration state, which depends on long-term information. In addition, the penetration identification experiment and closed-loop control experiment of unequal thickness plates are designed. The proposed method in this study has an accuracy of 98.96% and an average inference speed of 20.4 ms. The experimental results demonstrate that the proposed method exhibits significant performance in recognizing the penetration state from long sequences of welding image signals, adjusting welding power, and stabilizing welding quality.
Related Topics
Concepts
Welding
Weld pool
Keyhole
Laser beam welding
Computer science
Artificial intelligence
Convolutional neural network
Segmentation
Computer vision
Laser
Materials science
Gas tungsten arc welding
Arc welding
Optics
Composite material
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ma17184441
- OA Status
- gold
- Cited By
- 5
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402413986
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402413986Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ma17184441Digital Object Identifier
- Title
-
Penetration State Recognition during Laser Welding Process Control Based on Two-Stage Temporal Convolutional NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-10Full publication date if available
- Authors
-
Zhihui Liu, Shuai Ji, Chunhui Ma, Chengrui Zhang, Hongjuan Yu, Yisheng YinList of authors in order
- Landing page
-
https://doi.org/10.3390/ma17184441Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/ma17184441Direct OA link when available
- Concepts
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Welding, Weld pool, Keyhole, Laser beam welding, Computer science, Artificial intelligence, Convolutional neural network, Segmentation, Computer vision, Laser, Materials science, Gas tungsten arc welding, Arc welding, Optics, Composite material, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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