Jinhui Luo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jinhui Luo has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Junlin Deng
College of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, China
1 shared publication - Ning Wu
Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, China
1 shared publication - Xin Han
School of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, China
1 shared publication - Yijiang Xia
School of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, China
1 shared publication - Zhuolin Ou
School of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011, China
1 shared publication
Latest Publications
- Open Access
- 30 December 2024
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 13 (1) , 41
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 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.
- 30 December 2024
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 13 (1) , 41
Open AccessPredictive 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 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.