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Optimized shearing strategy for heavy plate based on contour recognition

Journal of Iron and Steel Research(International) 2023年第9期30卷 1821-1833页

作者： Jian-zhao Cao Yu-xia Wang Shao-wen Huang Chang-tao Wang School of Computer Science and Engineering Shenyang Jianzhu UniversityShenyang 110168LiaoningChina Shandong Iron and Steel Co. Ltd.Jinan 271105ShandongChina School of Electrical and Control Engineering Shenyang Jianzhu UniversityShenyang 110168LiaoningChina

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.

关键词： Heavy plate Contour detection Intelligent shearing strategy length prediction Neural network model

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