A defect recognition model for cross-section profile of hot-rolled strip based on deep learning

Abstract

The cross-section profile is a key signal for evaluating hot-rolled strip quality, and ignoring its defects can easily lead to a final failure. The characteristics of complex curve, significant irregular fluctuation and imperfect sample data make it a challenge of recognizing cross-section defects, and current industrial judgment methods rely excessively on human decision making. A novel stacked denoising autoencoders (SDAE) model optimized with support vector machine (SVM) theory was proposed for the recognition of cross-section defects. Firstly, interpolation filtering and principal component analysis were employed to linearly reduce the data dimensionality of the profile curve. Secondly, the deep learning algorithm SDAE was used layer by layer for greedy unsupervised feature learning, and its final layer of back-propagation neural network was replaced by SVM for supervised learning of the final features, and the final model SDAE_SVM was obtained by further optimizing the entire network parameters via error back-propagation. Finally, the curve mirroring and combination stitching methods were used as data augmentation for the training set, which dealt with the problem of sample imbalance in the original data set, and the accuracy of cross-section defect prediction was further improved. The approach was applied in a 1780-mm hot rolling line of a steel mill to achieve the automatic diagnosis and classification of defects in cross-section profile of hot-rolled strip, which helps to reduce flatness quality concerns in downstream processes.