|
|
|
| Optimized shearing strategy for heavy plate based on contour recognition |
| Jian-zhao Cao1, Yu-xia Wang1, Shao-wen Huang2, Chang-tao Wang3 |
| 1 School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang 110168, Liaoning, China 2 Shandong Iron and Steel Co., Ltd., Jinan 271105, Shandong, China 3 School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, Liaoning, China |
|
|
|
|
Abstract The shearing line is the key to improve the quality and efficiency of heavy plates. A model of contour recognition and intelligent shearing strategy for the heavy plate was proposed. Firstly, multi-array binocular vision linear cameras were used to complete the image acquisition. Secondly, the total length of the steel plate after cooling was predicted by back propagation neural network algorithm according to the contour data. Finally, using the scanning line and a new camber description method, the shearing strategy including head/tail irregular shape length and rough dividing strategy was calculated. The practical application shows that the model and strategy can effectively solve the problems existing in the shearing process and can effectively improve the yield of steel plates. The maximum error of detection width, length, camber, and the length of the irregular deformation area at the head/tail of the plate are all less than 5 mm. The correlation coefficient of the length prediction model based on the back propagation neural network is very high. The reverse ratio result of edge cutting failure using the proposed rough dividing strategy is 1/401 = 0.2%, which is 2% higher than that by human.
|
|
|
|
|
|
| Cite this article: |
|
Jian-zhao Cao,Yu-xia Wang,Shao-wen Huang, et al. Optimized shearing strategy for heavy plate based on contour recognition[J]. Journal of Iron and Steel Research International, 2023, 30(09): 1821-1833.
|
|
|
|
| [1] |
Yan-qi Fu, Qing Zhao, Man-qian Lv, Zhen-shan Cui. Modified artificial neural network model with an explicit expression to describe flow behavior and processing maps of Ti2AlNb-based superalloy[J]. JOURNAL OF IRON AND STEEL RESEARCH,INTERNATIONAL, 2021, 28(11): 1451-1462. |
| [2] |
Zhi-yu GAO,,Tao PAN,Zhuo WANG,Guo-quan LIU,,5,Hang SU. Hot Deformation Behavior of a Novel Ni-Cr-Mo-B Ultra-heavy Plate Steel by Hot Compression Test[J]. Chinese Journal of Iron and Steel, 2015, 22(9): 818-826. |
| [3] |
Jin-hua RUAN,Li-wen ZHANG,Sen-dong GU,Jian-lin ZHANG,Wen-bin HE,Shao-hui CHEN. Broadside Compensation Rolling Model for Plan View Pattern Control in Wide and Heavy Plate Rolling Process[J]. Chinese Journal of Iron and Steel, 2014, 21(5): 503-508. |
| [4] |
ZHANG Hui-ning,,XU An-jun,,CUI Jian,HE Dong-feng,,TIAN Nai-yuan,. Establishment of Neural Network Prediction Model for Terminative Temperature Based on Grey Theory in Hot Metal Pretreatment[J]. Chinese Journal of Iron and Steel, 2012, 19(6): 25-29. |
|
|
|
|
2023, Vol. 30 
