Detection of reinforcement of multi-bead and multi-layer weld in additive manufacturing based on on-line visual information of weld pool
In the multi-bead and multi-layer arc additive manufacturing process, the information of cladding reinforcement reflects the welding quality to a certain extent, so it is of great significance to monitor the reinforcement of cladding layers in real time. In this paper, a point cloud density search m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423003368 |
| _version_ | 1797858740397932544 |
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| author | Jun Lu Yang Zhao Xiaoyu Chen Jing Han Zhuang Zhao |
| author_facet | Jun Lu Yang Zhao Xiaoyu Chen Jing Han Zhuang Zhao |
| author_sort | Jun Lu |
| collection | DOAJ |
| description | In the multi-bead and multi-layer arc additive manufacturing process, the information of cladding reinforcement reflects the welding quality to a certain extent, so it is of great significance to monitor the reinforcement of cladding layers in real time. In this paper, a point cloud density search method is used to segment the point cloud of a single weld bead in the multi bead weld seam, and then the reinforcement of each cladding bead of multi-bead and multi-layer weld is extracted separately when the bottom plate is deformed due to high temperature, and a residual-based prediction model is constructed for quantitative forecasting of the transient reinforcement before solidification of block cladding layer in real time. The following work is completed to prove the accuracy and effectiveness of the proposed model, two different strategies are used to predict the reinforcement of multi-bead and multi-layer welds. Through the experiment, we can see the mean forecast error of the reinforcement of multi-bead and multi-layer welds is less than 0.3 mm, while the time for the model to dealing with the molten pool image is 18 ms, and the optimal strategy can make the average error better than 0.15 mm, which proves that the model constructed in this paper has great generalization performance and realizes the real-time and high-precision prediction of cladding reinforcement in the case of small deformation. The study of this paper supplies a necessary basis for the online monitoring and control of morphological defects in the process of weld processing. |
| first_indexed | 2024-04-09T21:18:16Z |
| format | Article |
| id | doaj.art-40a4b0f8f69b4569bc6900b1ba9fb73f |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-04-09T21:18:16Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-40a4b0f8f69b4569bc6900b1ba9fb73f2023-03-28T06:48:11ZengElsevierJournal of Materials Research and Technology2238-78542023-03-012346784690Detection of reinforcement of multi-bead and multi-layer weld in additive manufacturing based on on-line visual information of weld poolJun Lu0Yang Zhao1Xiaoyu Chen2Jing Han3Zhuang Zhao4Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaCorresponding author.; Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaIn the multi-bead and multi-layer arc additive manufacturing process, the information of cladding reinforcement reflects the welding quality to a certain extent, so it is of great significance to monitor the reinforcement of cladding layers in real time. In this paper, a point cloud density search method is used to segment the point cloud of a single weld bead in the multi bead weld seam, and then the reinforcement of each cladding bead of multi-bead and multi-layer weld is extracted separately when the bottom plate is deformed due to high temperature, and a residual-based prediction model is constructed for quantitative forecasting of the transient reinforcement before solidification of block cladding layer in real time. The following work is completed to prove the accuracy and effectiveness of the proposed model, two different strategies are used to predict the reinforcement of multi-bead and multi-layer welds. Through the experiment, we can see the mean forecast error of the reinforcement of multi-bead and multi-layer welds is less than 0.3 mm, while the time for the model to dealing with the molten pool image is 18 ms, and the optimal strategy can make the average error better than 0.15 mm, which proves that the model constructed in this paper has great generalization performance and realizes the real-time and high-precision prediction of cladding reinforcement in the case of small deformation. The study of this paper supplies a necessary basis for the online monitoring and control of morphological defects in the process of weld processing.http://www.sciencedirect.com/science/article/pii/S2238785423003368Quantitative predictionWeld reinforcementArc welding additive manufacturingOn-line visual information of weld poolDeep Residual Network |
| spellingShingle | Jun Lu Yang Zhao Xiaoyu Chen Jing Han Zhuang Zhao Detection of reinforcement of multi-bead and multi-layer weld in additive manufacturing based on on-line visual information of weld pool Journal of Materials Research and Technology Quantitative prediction Weld reinforcement Arc welding additive manufacturing On-line visual information of weld pool Deep Residual Network |
| title | Detection of reinforcement of multi-bead and multi-layer weld in additive manufacturing based on on-line visual information of weld pool |
| title_full | Detection of reinforcement of multi-bead and multi-layer weld in additive manufacturing based on on-line visual information of weld pool |
| title_fullStr | Detection of reinforcement of multi-bead and multi-layer weld in additive manufacturing based on on-line visual information of weld pool |
| title_full_unstemmed | Detection of reinforcement of multi-bead and multi-layer weld in additive manufacturing based on on-line visual information of weld pool |
| title_short | Detection of reinforcement of multi-bead and multi-layer weld in additive manufacturing based on on-line visual information of weld pool |
| title_sort | detection of reinforcement of multi bead and multi layer weld in additive manufacturing based on on line visual information of weld pool |
| topic | Quantitative prediction Weld reinforcement Arc welding additive manufacturing On-line visual information of weld pool Deep Residual Network |
| url | http://www.sciencedirect.com/science/article/pii/S2238785423003368 |
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