The edge of a hot rolling strip corresponds to the area where surface defects often occur. The morphologies of several common edge defects are similar to one another, thereby leading to easy error detection. To improve the detection accuracy of edge defects, the authors of this paper first classified the common edge defects and then made a dataset of edge defect images on this basis. Subsequently, edge defect recognition models were established on the basis of LeNet-5, AlexNet, and VggNet-16 by using a convolutional neural network as the core. Through multiple groups of training and recognition experiments, the model’s accuracy and recognition time of a single defect image were analyzed and compared with recognition models with different learning rates and sample batches. The experimental results showed that the recognition model based on the AlexNet had a maximum accuracy of 93.5%, and the average recognition time of a single defect image was 0.0035 s, which could meet the industry requirement. The research results in this paper provide a new method and thought for the fine detection of edge defects in hot rolling strips and have practical significance for improving the surface quality of hot rolling strips.

In order to evaluate the transverse thickness difference of cold-rolled strips according to the information of hot-rolled strips and scientifically guide the setting of the indicators of the hot-rolled silicon strip, the influence model about the relation of the transverse thickness difference of cold-rolled strip to the profile indicators of hot-rolled strip is established in this paper based on simulation results. The transverse thickness difference of cold-rolled strip predicted based on the influence model have strong correspondences to the measured data. Based on the influence model and the statistical analysis of the measured data, the control criterion of the profile indicators of hot-rolled silicon steel according to the requirements for the transverse thickness difference of cold-rolled strip are finally recommended. The simulation results show that the transverse thickness difference of cold-rolled strip is quadratic nonlinearly related to the wedge and crown of hot-rolled strip. The influence model and statistical data analysis indicate that reducing the edge-drop of hot-rolled strip is beneficial to restrain the transverse thickness difference of cold-rolled strip.