An improved Vision Transformer-based method for classifying surface defects in hot-rolled strip steel

A new Vision Transformer(ViT) model is proposed for the classification of surface defects in hot rolled strip, optimizing the poor learning ability of the original Vision Transformer model on smaller datasets. Firstly, each module of ViT and its characteristics are analyzed; Secondly, inspired by the deep learning model VGGNet, the multilayer fully connected layer in VGGNet is introduced into the ViT model to increase its learning capability; Finally, by performing on the X-SDD hot-rolled steel strip surface defect dataset. The effect of the improved algorithm is verified by comparison experiments on the X-SDD hot-rolled strip steel surface defect dataset. The test results show that the improved algorithm achieves better results than the original model in terms of accuracy, recall, F1 score, etc. Among them, the accuracy of the improved algorithm on the test set is 5.64% higher than ViT-Base and 2.64% higher than ViT-Huge; the accuracy is 4.68% and 1.36% higher than both of them, respectively.

Effect of Carbon Content on Microstructure, Properties and Texture of Ultra-Thin Hot Rolled Strip Produced by Endless Roll Technology

In order to make a comprehensive comparison between ultra-thin hot rolled low carbon steel (LC) and extra low carbon steel (ELC) produced by endless roll technology and explain the differences, a detailed investigation into the microstructural characterization, characteristics of cementite and precipitates, mechanical properties, internal friction peaks, texture characterization by an X-ray powder diffractometer and electron backscatter diffraction, and formability including earing behavior, hole expanding ratio and V-shaped bending properties was carried out with different carbon content for 1.0 mm thickness ultra-thin hot rolled strip produced in endless strip production line. The experimental results indicate that the microstructure of both is composed of multi-layer areas with different grain sizes and thicknesses, the strength and elongation of LC are higher than that of ELC, but the content of solid solution carbon atoms and r of ELC are higher than that of LC, at the same time, the formability of ultra-thin strip ELC is better than that of LC mainly related to the content of {hkl} <110> and {111} <112> of ELC was higher than those of LC. The mechanical and formability properties of ultra-thin hot rolled strip by endless roll technology can meet the requirements of replacement cold rolled strip by hot rolled strip.

A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel

Hot-rolled strip steel is widely used in automotive manufacturing, chemical and home appliance industries, and its surface quality has a great impact on the quality of the final product. In the manufacturing process of strip steel, due to the rolling process and many other reasons, the surface of hot rolled strip steel will inevitably produce slag, scratches and other surface defects. These defects not only affect the quality of the product, but may even lead to broken strips in the subsequent process, seriously affecting the continuation of production. Therefore, it is important to study the surface defects of strip steel and identify the types of defects in strip steel. In this paper, a scheme based on ResNet50 with the addition of FcaNet and Convolutional Block Attention Module (CBAM) is proposed for strip defect classification and validated on the X-SDD strip defect dataset. Our solution achieves a classification accuracy of 94.11%, higher than more than a dozen other compared deep learning models. Moreover, to adress the problem of low accuracy of the algorithm in classifying individual defects, we use ensemble learning to optimize. By integrating the original solution with VGG16 and SqueezeNet, the recognition rate of oxide scale of plate system defects improved by 21.05 percentage points, and the overall defect classification accuracy improved to 94.85%.