Weld pool boundary detection based on U-Net algorithm and weld seam tracking in plasma arc welding Article Swipe
Jidong Lu
,
Satoshi Yamane
,
Weixi Wang
,
Ning Li
,
Siqi Wang
,
Yuxiong Xia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5254986/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5254986/v1
Plasma arc welding (PAW)can achieve high-quality welding joints in high-strength manufacturing fields such as aviation and automotive and improve production efficiency. The plasma keyhole arc welding(K-PAW) can be used for butt welding in thick plates since the plasma arc is more concentrated than Metal Active Gas (MAG) welding. The weld pool can effectively reflect the welding melt state. To make robot automatic welding, it is important to observe the weld pool in real-time. The K-PAW will be stable to ensure the welding quality if the weld line can be taught accurately. However, the electrodes of the plasma welding torch cannot be observed from the outside. Teaching the weld line to torch in real-time from human sight will be difficult. In this study, the Complementary Metal-Oxide-Semiconductor (CMOS) camera fixed on the welding torch with an interference filter was utilized to observe the weld pool. Estimate the weld line position in real-time by image processing techniques based on deep learning. The control of seam tracking was performed to avoid misalignment using the weld line position. Specifically, the authors employed the U-Net autoencoder, a deep learning technology to create a training model and then used the training model to detect the boundary area from the weld pool image observed by a camera in the welding process. The model demonstrates sufficient prediction which the accuracy reached 99.5% for the training data and 96.5% on the test data recognition. Moreover, the authors discussed a control method that utilized weld line position estimated from the boundary area to verify the effectiveness of this prediction model from 3mm within the deviation of 1 mm in controlling process. It is important to improve the quality of KAW welding in this study. Combining vision sensing technologies and deep learning methods will provide new technologies to enable greater welding precision and improve welding quality. It could also accelerate the development of welding technology in the intelligent manufacturing field.
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- Type
- preprint
- Language
- en
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- https://doi.org/10.21203/rs.3.rs-5254986/v1
- OA Status
- gold
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- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403849470Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-5254986/v1Digital Object Identifier
- Title
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Weld pool boundary detection based on U-Net algorithm and weld seam tracking in plasma arc weldingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-10-29Full publication date if available
- Authors
-
Jidong Lu, Satoshi Yamane, Weixi Wang, Ning Li, Siqi Wang, Yuxiong XiaList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-5254986/v1Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.21203/rs.3.rs-5254986/v1Direct OA link when available
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Welding, Arc (geometry), Tracking (education), Net (polyhedron), Boundary (topology), Algorithm, Plasma arc welding, Arc welding, Computer science, Mathematics, Materials science, Geometry, Metallurgy, Mathematical analysis, Pedagogy, PsychologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.welding | 3, 7, 32, 56, 82, 98, 131, 212, 280, 299, 303, 312 |
| abstract_inverted_index.(PAW)can | 4 |
| abstract_inverted_index.Estimate | 144 |
| abstract_inverted_index.However, | 92 |
| abstract_inverted_index.Teaching | 106 |
| abstract_inverted_index.accuracy | 221 |
| abstract_inverted_index.aviation | 15 |
| abstract_inverted_index.boundary | 199, 250 |
| abstract_inverted_index.employed | 177 |
| abstract_inverted_index.learning | 183, 290 |
| abstract_inverted_index.observed | 102, 206 |
| abstract_inverted_index.outside. | 105 |
| abstract_inverted_index.position | 148, 246 |
| abstract_inverted_index.process. | 213, 270 |
| abstract_inverted_index.quality. | 304 |
| abstract_inverted_index.tracking | 163 |
| abstract_inverted_index.training | 188, 194, 226 |
| abstract_inverted_index.utilized | 138, 243 |
| abstract_inverted_index.welding, | 63 |
| abstract_inverted_index.welding. | 48 |
| abstract_inverted_index.Combining | 284 |
| abstract_inverted_index.Moreover, | 235 |
| abstract_inverted_index.automatic | 62 |
| abstract_inverted_index.deviation | 264 |
| abstract_inverted_index.discussed | 238 |
| abstract_inverted_index.estimated | 247 |
| abstract_inverted_index.important | 66, 273 |
| abstract_inverted_index.learning. | 158 |
| abstract_inverted_index.performed | 165 |
| abstract_inverted_index.position. | 173 |
| abstract_inverted_index.precision | 300 |
| abstract_inverted_index.real-time | 113, 150 |
| abstract_inverted_index.accelerate | 308 |
| abstract_inverted_index.automotive | 17 |
| abstract_inverted_index.difficult. | 119 |
| abstract_inverted_index.electrodes | 94 |
| abstract_inverted_index.prediction | 218, 258 |
| abstract_inverted_index.processing | 153 |
| abstract_inverted_index.production | 20 |
| abstract_inverted_index.real-time. | 73 |
| abstract_inverted_index.sufficient | 217 |
| abstract_inverted_index.techniques | 154 |
| abstract_inverted_index.technology | 184, 313 |
| abstract_inverted_index.accurately. | 91 |
| abstract_inverted_index.controlling | 269 |
| abstract_inverted_index.development | 310 |
| abstract_inverted_index.effectively | 53 |
| abstract_inverted_index.efficiency. | 21 |
| abstract_inverted_index.intelligent | 316 |
| abstract_inverted_index.autoencoder, | 180 |
| abstract_inverted_index.concentrated | 42 |
| abstract_inverted_index.demonstrates | 216 |
| abstract_inverted_index.high-quality | 6 |
| abstract_inverted_index.interference | 135 |
| abstract_inverted_index.misalignment | 168 |
| abstract_inverted_index.recognition. | 234 |
| abstract_inverted_index.technologies | 287, 295 |
| abstract_inverted_index.Complementary | 124 |
| abstract_inverted_index.Specifically, | 174 |
| abstract_inverted_index.effectiveness | 255 |
| abstract_inverted_index.high-strength | 10 |
| abstract_inverted_index.manufacturing | 11, 317 |
| abstract_inverted_index.welding(K-PAW) | 26 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| abstract_inverted_index.Metal-Oxide-Semiconductor | 125 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.24988824 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |