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.
Accurate and rapid recognition of flatness defects is important to produce high‐quality cold rolling strips. First, this study collects the flatness defect images and creates the first image dataset of flatness defect (YSU_CFC_1) according to its characteristics. Then, a flatness defect recognition model of the cold rolling strip with a new stacked generative adversarial network is proposed; it is trained through a recurrent strategy. The generators in this recognition model can generate more real and various fake image samples of flatness defects according to categories. The classifier is used to classify flatness defect images. The recognition results of this model are compared with recognition models established by four convolution neural networks. Results show that the accuracy of this model is the highest, and the accuracy reaches 98.625%, the recognition accuracy of single type defects is ≥95%, and the average recognition time of a single defect image is 24.13 ms, which meets the engineering requirements. Finally, the classification and recognition mechanism of this model is interpretably analyzed, the causes of the recognition error are explored, and the direction of further optimization is provided. The results of this research provide new methods and ideas for the sophisticated detection of flatness defects.
Surface defect automatic detection has great significance for copper strip production. The traditional machine vision for surface defect automatic detection of copper strip needs artificial feature design, which has a long cycle, and poor ability of versatility and robustness. However, deep learning can effectively solve these problems. Therefore, based on the deep convolution neural network and the transfer learning strategy, an intelligent recognition model of surface defects of copper strip is established in this paper. Firstly, the defects were classified in accordance with the mechanism and morphology, and the surface defect dataset of copper strip was established by comprehensively adopting image acquisition and image augmentation. Then, a two-class discrimination model was established to achieve the accurate discrimination of perfect and defect images. On this basis, four CNN models were adopted for the recognition of defect images. Among these models, the EfficientNet model through transfer learning strategy had the best comprehensive performance with a recognition accuracy rate of 93.05%. Finally, the interpretability and deficiency of the model were analysed by the class activation map and confusion matrix, which point toward the direction of further optimization for future research.
During the process of hot rolling, seam defects often appear on the edges of the interstitial‐free (IF) steel, which seriously affects product quality and yield. To this end, the macro‐ and micromorphology of edge seam defects are analyzed, their specific characteristics are summarized, and the defect cast slab rolling experiment is conducted. The experiment shows that the pore, crack, and slag inclusion are not the causes of the seam defects. Then, through a combination of theoretical analysis, actual parameters’ detection, and experiments, the formation mechanisms of the seam defects are evidently side folding of the intermediate slab and flipping of the corner metal. The mechanisms of the two types of folds are also described in detail. Finally, in view of the mechanisms, the advantages and disadvantages of control measures are analyzed, and the concave anvil of the slab sizing press is selected at last. To determine the effectiveness of the scheme, a pass experiment is conducted on a slab sizing press at the production site. Results show that the steel coils without slab sizing press generally have seam defects, whereas the coils with a concave anvil slab sizing press do not. The defects are almost totally eliminated.
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