Optimized shearing strategy for heavy plate based on contour recognition

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.