Predictive Control for Steel Rib Bending Based on Deep Learning
In the shipbuilding industry, the inefficiency of the successive approximation control method in CNC cold-bending machines has hindered productivity in steel bending manufacturing, particularly for rib profiles. This study proposes control methods for cold bending machines based on deep learning mod...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/41 |
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| _version_ | 1832588179983564800 |
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| author | Yijiang Xia Jinhui Luo Zhuolin Ou Xin Han Junlin Deng Ning Wu |
| author_facet | Yijiang Xia Jinhui Luo Zhuolin Ou Xin Han Junlin Deng Ning Wu |
| author_sort | Yijiang Xia |
| collection | DOAJ |
| description | In the shipbuilding industry, the inefficiency of the successive approximation control method in CNC cold-bending machines has hindered productivity in steel bending manufacturing, particularly for rib profiles. This study proposes control methods for cold bending machines based on deep learning models to address this challenge, including CNN and Transformer-CNN (T-CNN), to predict the elastic spring-back rate of cold-processed metal profiles and generate precise control pulses for achieving target bending angles. Experimental validation using real-world datasets collected from a shipyard’s CNC cold bending machine demonstrates that the T-CNN model significantly reduces the number of steps required for each bending operation, achieving a 75% reduction in production time and substantially enhancing processing efficiency. By leveraging the strengths of CNNs and Transformer architectures, the T-CNN model excels at handling long sequence data and capturing global dataset characteristics. Results show that the T-CNN model outperforms traditional control methods and standard CNNs in prediction accuracy, stability, and efficiency, making it a superior choice for cold bending control. |
| format | Article |
| id | doaj-art-14f92dc3e3a94d1c8d83513983e31581 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-14f92dc3e3a94d1c8d83513983e315812025-01-24T13:36:38ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011314110.3390/jmse13010041Predictive Control for Steel Rib Bending Based on Deep LearningYijiang Xia0Jinhui Luo1Zhuolin Ou2Xin Han3Junlin Deng4Ning Wu5School of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, ChinaSchool of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, ChinaSchool of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, ChinaSchool of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, ChinaSchool of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, ChinaSchool of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, ChinaIn the shipbuilding industry, the inefficiency of the successive approximation control method in CNC cold-bending machines has hindered productivity in steel bending manufacturing, particularly for rib profiles. This study proposes control methods for cold bending machines based on deep learning models to address this challenge, including CNN and Transformer-CNN (T-CNN), to predict the elastic spring-back rate of cold-processed metal profiles and generate precise control pulses for achieving target bending angles. Experimental validation using real-world datasets collected from a shipyard’s CNC cold bending machine demonstrates that the T-CNN model significantly reduces the number of steps required for each bending operation, achieving a 75% reduction in production time and substantially enhancing processing efficiency. By leveraging the strengths of CNNs and Transformer architectures, the T-CNN model excels at handling long sequence data and capturing global dataset characteristics. Results show that the T-CNN model outperforms traditional control methods and standard CNNs in prediction accuracy, stability, and efficiency, making it a superior choice for cold bending control.https://www.mdpi.com/2077-1312/13/1/41machine learningpredictionbendingCNC machinebulb flat steel |
| spellingShingle | Yijiang Xia Jinhui Luo Zhuolin Ou Xin Han Junlin Deng Ning Wu Predictive Control for Steel Rib Bending Based on Deep Learning Journal of Marine Science and Engineering machine learning prediction bending CNC machine bulb flat steel |
| title | Predictive Control for Steel Rib Bending Based on Deep Learning |
| title_full | Predictive Control for Steel Rib Bending Based on Deep Learning |
| title_fullStr | Predictive Control for Steel Rib Bending Based on Deep Learning |
| title_full_unstemmed | Predictive Control for Steel Rib Bending Based on Deep Learning |
| title_short | Predictive Control for Steel Rib Bending Based on Deep Learning |
| title_sort | predictive control for steel rib bending based on deep learning |
| topic | machine learning prediction bending CNC machine bulb flat steel |
| url | https://www.mdpi.com/2077-1312/13/1/41 |
| work_keys_str_mv | AT yijiangxia predictivecontrolforsteelribbendingbasedondeeplearning AT jinhuiluo predictivecontrolforsteelribbendingbasedondeeplearning AT zhuolinou predictivecontrolforsteelribbendingbasedondeeplearning AT xinhan predictivecontrolforsteelribbendingbasedondeeplearning AT junlindeng predictivecontrolforsteelribbendingbasedondeeplearning AT ningwu predictivecontrolforsteelribbendingbasedondeeplearning |