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It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

This study investigated the application of neural network techniques to predict the mechanical properties of low-carbon hot-rolled steel strips using industrial data. A feedforward neural network (FFNN) model was developed to predict the yield strength (YS), ultimate tensile strength (UTS), and elongation (%EL) based on the chemical composition and processing parameters. For the low-carbon hot-rolled steel strip (C: 0.02–0.06%, Mn: 0.17–0.38%), 435 datasets were utilized with 17 input parameters, including 15 composition elements, finish rolling temperature (FRT), and coil target temperature (CTT). The model was trained using 335 datasets and tested using 100 randomly selected datasets. The optimum network architecture consisted of two hidden layers with 34 neurons each, achieving a mean squared error of 0.014 after 200,000 iterations. The model predictions showed excellent agreement with the actual values, with mean percentage errors of 4.44%, 3.54%, and 4.84% for the YS, UTS, and %EL, respectively. The study further examined the influence of FRT and CTT on mechanical properties, demonstrating that FRT has more complex effects on mechanical properties than CTT. The model successfully predicted property variations with different processing parameters, thereby providing a valuable tool for alloy design and process optimization in steel manufacturing.

In a complex industrial environment, it is difficult to obtain hot rolled strip steel surface defect images. Moreover, there is a lack of effective identification methods. In response to this, this paper implements accurate classification of strip steel surface defects based on generative adversarial network and attention mechanism. Firstly, a novel WGAN model is proposed to generate new surface defect images from random noises. By expanding the number of samples from 1360 to 3773, the generated images can be further used for training classification algorithm. Secondly, a Multi-SE-ResNet34 model integrating attention mechanism is proposed to identify defects. The accuracy rate on the test set is 99.20%, which is 6.71%, 4.56%, 1.88%, 0.54% and 1.34% higher than AlexNet, VGG16, ShuffleNet v2 1×, ResNet34, and ResNet50, respectively. Finally, a visual comparison of the features extracted by different models using Grad-CAM reveals that the proposed model is more calibrated for feature extraction. Therefore, it can be concluded that the proposed methods provide a significant reference for data augmentation and classification of strip steel surface defects.

Strip steel surface defects occur frequently during the manufacturing process, and these defects cause hidden risks in the use of subsequent strip products. Therefore, it is crucial to classify the strip steel’s surface defects accurately and efficiently. Most classification models of strip steel surface defects are generally based on convolutional neural networks (CNNs). However, CNNs, with local receptive fields, do not have admirable global representation ability, resulting in poor classification performance. To this end, we proposed a hybrid network architecture (CNN-T), which merges CNN and Transformer encoder. The CNN-T network has both strong inductive biases (e.g., translation invariance, locality) and global modeling capability. Specifically, CNN first extracts low-level and local features from the images. The Transformer encoder then globally models these features, extracting abstract and high-level semantic information and finally sending them to the multilayer perceptron classifier for classification. Extensive experiments show that the classification performance of CNN-T outperforms pure Transformer networks and CNNs (e.g., GoogLeNet, MobileNet v2, ResNet18) on the NEU-CLS dataset (training ratio is 80%) with a 0.28–2.23% improvement in classification accuracy, with fewer parameters (0.45 M) and floating-point operations (0.12 G).

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%.

Concrete-filled steel tube (CFST) columns are widely used in civil engineering because of their excellent bearing capacity; however, the reinforcement of CFST columns lacks effective measures. To strengthen CFST columns quickly and effectively, two methods, namely, winding FRP (fiber reinforced polymer) or steel strips, were explored in this work. Two unconfined CFST columns, eight FRP-strengthened CFST columns and four welded steel strip-strengthened CFST columns were manufactured and tested. The failure modes and axial load–strain curves of all specimens under compression load were concluded and compared. The effects of the primary parameters, such as FRP layers (1, 2, 3 and 4 layers) and steel strip thickness (3.0 and 6.0 mm), on the bearing capacity and deformation capacity were also investigated. The ultimate load of CFST columns increased from 28.72 to 64.16% after being confined by FRP with one to four layers. The ultimate load of the welded steel strip-strengthened CFST column with 3.0 mm steel strips and 6.0 mm steel strips increased by 28.46% and 49.82%, respectively, compared with the unconfined CFST column. Thus, the increase in FRP layers and steel strip thickness can markedly improve the compressive behavior of the FRP/welded steel strip-strengthened CFST columns. The cost performance of the two different reinforcement methods also showed that the cost of the welded steel strip-strengthened CFST column is nearly 40% of that of the FRP-strengthened CFST column when the same strengthening effect was obtained, which indicated that the welded steel strip-strengthened CFST column is more cost-efficient than CFST columns confined by FRP. Finally, six existing models for the ultimate load of FRP-strengthened CFST columns were presented and evaluated. From the evaluation results, the Zhang et al.’s model, Lu et al.’s model and Hu et al.’s model for FRP-strengthened CFST columns were shown to provide the best applicability and accuracy. Based on the Mander et al.’s model, a model for the ultimate load of welded steel strip-strengthened CFST columns was proposed and evaluated. The proposed model can accurately predict the ultimate load of welded steel strip-strengthened CFST columns.

In order to improve the detection accuracy of the surface defect detection of industrial hot rolled strip steel, the advanced technology of deep learning is applied to the surface defect detection of strip steel. In this paper, we propose a framework for strip surface defect detection based on a convolutional neural network (CNN). In particular, we propose a novel multi-scale feature fusion module (ATPF) for integrating multi-scale features and adaptively assigning weights to each feature. This module can extract semantic information at different scales more fully. At the same time, based on this module, we build a deep learning network, CG-Net, that is suitable for strip surface defect detection. The test results showed that it achieved an average accuracy of 75.9 percent (mAP50) in 6.5 giga floating-point operation (GFLOPs) and 105 frames per second (FPS). The detection accuracy improved by 6.3% over the baseline YOLOv5s. Compared with YOLOv5s, the reference quantity and calculation amount were reduced by 67% and 59.5%, respectively. At the same time, we also verify that our model exhibits good generalization performance on the NEU-CLS dataset.

This study investigates the preparation and mechanical properties of ultra-thin stainless steel honeycomb structures. Using SUS304 ultra-thin stainless steel strips as the raw material, honeycomb cores with varying structural parameters were fabricated via laser spot welding. The mechanical properties of both the ultra-thin stainless steel strips and the resulting honeycomb structures were systematically evaluated through tensile and dynamic impact experiments. The results demonstrate that the ultra-thin stainless steel strips exhibit high strength due to the size effect. Specifically, the average elastic modulus, yield strength, and tensile strength of the thicker specimens were measured at 71.92 GPa, 836.98 MPa, and 1032.6 MPa, respectively, while those of the thinner specimens were 75.34 GPa, 600.32 MPa, and 885.21 MPa, respectively. In dynamic impact tests, the honeycomb structures underwent plastic deformation following elastic deformation. Notably, the maximum stress capacity of the honeycomb structures increased with rising strain rates. The maximum stress values observed for the three samples were 67.01 MPa, 68.14 MPa, and 69.68 MPa, respectively, while the corresponding values for the reference samples were 51.57 MPa, 55.27 MPa, and 57.94 MPa, respectively. Overall, the mechanical performance of the ultra-thin stainless steel honeycomb structures surpasses that of conventional 304 stainless steel and aluminum honeycomb structures, exhibiting superior strength parameters and properties. This research provides a theoretical foundation and experimental validation for the practical application of ultra-thin stainless steel honeycomb structures, particularly in fields requiring lightweight and high-strength materials.

The article presents some known methods for assessing the type and amplitude of flatness defects of steel strips and analyzes models of flatness defects in these strips after their rolling and heat treatment. These models can be conditionally divided into four groups. The first group of such models allows one to estimate the presence of flatness defect by the difference in the elongation coefficients across the width of the strip. The models of the second group are based on the possibility of determining the best shape of the cross section of the strip. The models of the third group relate the causes of the loss of strip flatness to the technology of cooling the steel strip after rolling. In the fourth group of models, the main cause of the loss of strip flatness is indicated to be the parameters of the working and support rolls.

As the demand for automotive materials grows more stringent in environmental considerations, it becomes imperative to conduct thorough environmental impact assessments of dual-phase automotive strip steel (DP steel). However, the absence of detailed and comparable studies has left the carbon footprint of DP steel and its sources largely unknown. This study addresses this gap by establishing a cradle-to-gate life cycle model for DP steel, encompassing on-site production, energy systems, and upstream processes. The analysis identifies and scrutinizes key factors influencing the carbon footprint, with a focus on upstream mining, transportation, and on-site production processes. The results indicate that the carbon footprint of DP steel is 2.721 kgCO 2 -eq/kgDP, with on-site processes contributing significantly at 88.1%. Sensitivity analysis is employed to assess the impact of changes in resource structure, on-site energy, CO 2 emission factors, and byproduct recovery on the carbon footprint. Proposals for mitigating carbon emissions in DP steel production include enhancing process gas recovery, transitioning to cleaner energy sources, and reducing the hot metal-to-steel ratio. These findings offer valuable insights for steering steel production towards environmentally sustainable practices.