Hot Rolled Steel Strip Research Articles

Prediction of Mechanical Properties of Hot-Rolled Strip Steel Based on XGBoost and Metallurgical Mechanism

Prediction of Mechanical Properties of Hot-Rolled Strip Steel Based on XGBoost and Metallurgical Mechanism

Multi-scale sensing and multi-dimensional feature enhancement for surface defect detection of hot-rolled steel strip

ABSTRACT Surface defect detection of hot-rolled steel strip is a crucial process in its production. Aiming at the problem of poor detection accuracy caused by complex shape of defect features and imbalance between object scale and feature scale, this paper proposes a multi-scale sensing and multi-dimensional feature enhancement algorithm. Firstly, a multi-branch residual aggregation module (MRAM) is proposed, which uses the interactive features of different receptive fields between layers to capture global and local texture features, improving the feature extraction capability for defects of different scales. Then, the multi-dimensional attention enhancement head (MAEH) is proposed, which integrates feature information from different prediction branches and realises feature information enhancement in scale, space and channel dimensions for the aggregated information to improve the accuracy and robustness of steel surface defect detection. The results show that the proposed algorithm achieves 82.7% mAP and 89 FPS on the NEU-DET dataset, and 73.3% mAP and 94 FPS on the GC10-DET dataset. Compared with state-of-the-art algorithms, the proposed algorithm has the highest mAP, strong generalisation ability, and good real-time performance, which proves its effectiveness in detecting hot-rolled steel strip surface defects.

Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning

Establishing a low-cost, high-precision predictive model for mechanical properties is crucial for enhancing the properties of steel. Due to the complexity of the steel production process, traditional mechanistic models struggle to efficiently and accurately describe the relationships among alloy elements, processes, and properties. In response to this, the paper introduces a high-precision framework for predicting and optimizing mechanical properties through feature engineering and machine learning models. Firstly, an effective dimension reduction of features is achieved through a weighted fusion heterogeneous feature selection strategy, thereby identifying the optimal model input features. Subsequently, an improved seagull optimization algorithm is utilized to optimize the hyperparameters of Extreme Gradient Boosting, further enhancing the predictive accuracy of the model. Moreover, based on the Shapley Additive Explanation method, a quantitative analysis of the model's predictive outcomes is conducted, elucidating the impacts of alloy elements and processes on mechanical properties, consistent with established principles of physical metallurgy. The proposed framework not only enables precise prediction of mechanical properties in steel but also provides theoretical guidance and technical support for process optimization design and the development of new steel grades.

Industry Image Classification Based on Stochastic Configuration Networks and Multi-Scale Feature Analysis.

For industry image data, this paper proposes an image classification method based on stochastic configuration networks and multi-scale feature extraction. The multi-scale features are extracted from images of different scales using deep 2DSCN, and the hidden features of multiple layers are also connected together to obtain more informational features. The integrated features are fed into SCNs to learn a classifier which improves the recognition rate for different categories. In the experiments, a handwritten digit database and an industry hot-rolled steel strip database are used, and the comparison results demonstrate the proposed method can effectively improve the classification accuracy.

Surface defect detection of strip steel based on GT-CutMix augmentation algorithm and improved DSSD model

Abstract Nowadays, defect detection technology based on deep learning continuously increases the surface quality requirements of hot-rolled strip steel. However, due to limitations in industrial production, defect datasets often suffer from insufficient training samples and imbalanced categories. This paper proposes effective solutions, namely the GT-CutMix offline data augmentation algorithm and lightweight small sample defect detection models. The GT-CutMix augmentation algorithm significantly improves defect utilization by accurately sampling defect locations and integrating them into the original data set. We design the S-DSSD defect detection model by constructing a lightweight SI-MobileNet to replace the ResNet101 backbone of the Deconvolutional Single Shot Detector (DSSD) network. This can reduce the resource parameters and consumption. At the same time, it can speed up training and inference. To further improve the detection accuracy, we integrate the Pyramid Split Attention (PSA) attention mechanism into the prediction module of DSSD and construct the SA-DSSD model. Under the GT-CutMix augmentation algorithm, the mAP of S-DSSD and SA-DSSD models on X-SDD dataset are 76.83% and 78.63%, respectively. Meanwhile, the corresponding detection speeds are 45 FPS and 40 FPS, respectively. In addition, on the NEU-DET cross-dataset experiment, the mAP of the SA-DSSD model reaches 74.88%. Our methods are highly effective and generalized for small sample defect detection, which can provide selective solutions for specific needs such as high speed and precision in different industrial production scenarios.

YOLOv8n-GAM: an improved surface defect detection network for hot-rolled strip steel

Production defects caused by irresistible factors such as process design problems or differences in steel properties in strip production affect the economic benefits of the enterprise and threaten production safety. Traditional defect detection methods are difficult to achieve real-time and high-precision detection, so developing surface defect detection methods based on deep learning is of great significance for strip production. In order to effectively improve the accuracy of the deep learning model in detecting surface defects on hot-rolled strip, in this work we propose a real-time detection model for surface defects on strip steel based on the YOLOv8n model. Firstly, the newly convolutional layer Con5v is designed to replace the original convolutional layer in the neck, and an attention mechanism is added in front of each Con5v to improve the algorithm’s ability to extract small target information. Secondly, an additional set of upsampled feature extraction units is added to the neck in order to enhance the spatial information of the feature map. Subsequently, a set of feature fusion units is incorporated and the convolutional layers in it are improved to provide better feature maps. Thirdly, the number of decoupling detection heads is increased to receive more high-quality features. The final experimental results show that YOLOv8n-GAM (YOLOv8 Nano Model with Global Attention Mechanism) achieves 81.4mAP and 82.0FPS on the NEU-DET dataset and 71.2mAP and 55.0FPS on the GC10-DET dataset, which are 5.7% and 6.9% higher than those of YOLOv8n, respectively. The model proposed in this paper achieves a comprehensive performance improvement in strip steel.

Stacking ensemble learning fused with whale optimisation algorithm based method for crown prediction of hot-rolled strip

Precision of strip shape is one of the important indexes to measure the quality of strip steel in hot rolling. Accurate prediction of strip shape can realise the timely adjustment of the production system, which is the important foundation for ensuring the quality and stable production of hot-rolled strip steel products. Given the poor accuracy of the traditional mechanism model, this paper proposes a stacking ensemble learning for crown prediction that consist of the random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), categorical boosting (CatBoost) and linear regression, whose hyperparameters are optimised by whale optimisation algorithm (WOA) Firstly, the key features are selected by analysing the feature importance and the Pearson correlation coefficient between rolling parameters. Then, the hyperparameters of the stacking ensemble learning are optimised by four meta-heuristic algorithms during modeling process, among which WOA has the best optimisation effect. Compared to RF, XGBoost, LightGBM, CatBoost and the stacking, proposed method has the highest prediction accuracy that the hit rate reaches 98.58% in the range of 5.0 μm of crown deviation. In addition, the effect of strip width, bending force, rolling force and roll shifting on crown are analysed based on Shapley additive explanations (SHAP).

Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block

To improve the precision of defect categorization and localization in images, this paper proposes an approach for detecting surface defects in hot-rolled steel strips. The approach uses an improved YOLOv5 network model to overcome the issues of inadequate feature extraction capacity and suboptimal feature integration when identifying surface defects on steel strips. The proposed method achieves higher detection accuracy and localization precision, making it more competitive and applicable in real production. Firstly, the multi-scale feature fusion (MSF) strategy is utilized to fuse shallow and deep features effectively and enrich detailed information relevant to target defects. Secondly, the CSPLayer Res2Attention block (CRA block) residual module is introduced to reduce the loss of defect information during hierarchical transmission, thereby enhancing the extraction of fine-grained features and improving the perception of details and global features. Finally, the experimental results indicate that the mAP on the NEU-DET and GC10-DET datasets approaches 78.5% and 67.3%, respectively, which is 4.9% and 2.1% higher than that of the baseline. Meanwhile, it has higher precision and more precise localization capabilities than other methods. Furthermore, it also achieves 59.2% mAP on the APDDD dataset, indicating its potential for growth in further domains.

Steel strip surface defect detection based on multiscale feature sensing and adaptive feature fusion

A surface defect detection method for hot-rolled steel strips was proposed to address the challenges of detecting small target defects, significant differences in morphology, and unclear defect characteristics. This method is based on multiscale feature perception and adaptive feature fusion. First, based on the spatial distribution characteristics of the steel strip image, redundant background interference is removed using automatic gamma correction and Otsu thresholding. Second, based on the characteristics of surface defects in steel strips, this paper proposes TDB-YOLO (YOLO with a small target detection layer), a Bidirectional Feature Pyramid Network (BiFPN), and Double Cross Stage Partial (CSP) Bottleneck with three convolutions (DC3). To detect small object defects, a small target detection layer with a smaller receptive field focuses on fine-grained features, reducing the model’s probability of missed detection. In terms of feature extraction, DC3 enhances the interaction of feature information from different spatial scales, enabling the model to effectively handle features of varying scales. In terms of feature fusion, the BiFPN is used to adaptively fuse deep-level and shallow-level feature information, enhancing the semantic richness of the feature information. Ultimately, the proposed model in this paper achieved an accuracy of 90.3% and a recall rate of 88.0% for surface defects in steel strips. The mean average precision was 90.4%, and the frames per second was 33. The detection performance of this model outperformed those of other detection models, demonstrating its ability to effectively meet the real-time detection requirements of surface defects in industrial scenarios on steel strips.

CNN-based hot-rolled steel strip surface defects classification: a comparative study between different pre-trained CNN models

CNN-based hot-rolled steel strip surface defects classification: a comparative study between different pre-trained CNN models

Evaluation of the type and amplitude of the flatness defect of a hot-rolled steel strip produced on a continuous mill

Evaluation of the type and amplitude of the flatness defect of a hot-rolled steel strip produced on a continuous mill

Online Partition-Cooling System of Hot-Rolled Electrical Steel for Thermal Roll Profile and Its Industrial Application

The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. However, it is always overlooked due to its dynamic uncertainty. To solve this problem, it is necessary to achieve online cooling-status control for the local thermal expansion of rolls. Based on the existing structure of a mill, a pair of special partition-cooling beams with an intelligent cooling system was designed. For high efficiency and practicality, a new online predictive model was established for the dynamic temperature field of the hot-rolling process. An equivalent treatment was applied to the boundary condition corresponding to the practical cooling water flow. In addition, by establishing the corresponding target distribution curve for the partitioned water flow cooling, online water-flow-partitioning control of the thermal roll profile was achieved. In the practical application process, a large number of onsite results exhibited that the predicted error was within 5% compared to the experimental results. The temperature difference between the upper and lower rolls was within 5 °C, and the temperature difference on both sides of the rolls was controlled within 0.7 °C. The hit rate of convexity (C40) increased by 33%. It was demonstrated that the partition-cooling processes of hot rolling are effective for the local shape and special convexity. They are able to serve as a better control method in the hot-rolling process.

Swin-Transformer -YOLOv5 for lightweight hot-rolled steel strips surface defect detection algorithm.

An essential industrial application is the examination of surface flaws in hot-rolled steel strips. While automatic visual inspection tools must meet strict real-time performance criteria for inspecting hot-rolled steel strips, their capabilities are constrained by the accuracy and processing speed of the algorithm used to identify defects. To solve the problems of poor detection accuracy, low detection efficiency, and unsuitability of low computing power platforms of the hot-rolled strip surface defect detection algorithm The Swin-Transformer-YOLOv5 model based on the improved one-stage detector is proposed. By employing GhostNet, the model's lightweight design, and guaranteed detection accuracy are both achieved. The C3 module introduces Swin-Transformer to address the issues of cluttered backdrops of defect photos and easily confused defect categories. With the addition of the CoordAttention module, the model's capacity to extract defective features is improved, and its performance keeps getting better. The issue of huge differences in different scales and poor detection of small flaws is resolved by employing BiFPN for feature fusion, and the detector's capacity to adapt to targets of different scales is improved. The experimental results demonstrate that the improved Swin-Transformer-Yolov5 model significantly outperforms the industry-standard target detection algorithms, and the model's mAP value still improves by 8.39% over the original model while reducing the number of parameters, GFLOPs, and weight by 36.6%, 40.0%, and 34.7%, respectively. The model is better suited for use on low-arithmetic platforms as a result.

Prediction model for properties of hot-rolled steel strip: An ICME approach

The Integrated Computational Materials Engineering (ICME) approach is implemented to develop an integrated model for the processing of steel at a hot strip mill. The effect of temperature, deformation, and subsequent cooling rate on microstructure and its associated properties of the steel are mapped using analytical, numerical as well as machine learning approaches, all applied in tandem. The modeling approaches include a rigid visco-plastic approach for the flow formulation, finite volume method to calculate temperature distribution profile, artificial neural network (ANN) modeling for predicting CCT diagram, and Mecking–Kock model along with stress mixing law for the structure–property correlation. The composite model for mapping the final microstructure and microstructure-dependent properties of the steel, which depends on the composition, and various parameters of the hot strip mill is validated through a comparison of predicted values with the published results. The work shows that the hybridization of constitutive equations with the data-driven approach in ICME can successfully model a complex system like the hot strip mill.

Automatic recognition of surface defects of hot rolled strip steel based on deep parallel attention convolution neural network

In general, the existence of surface defects in hot rolled strip steel will seriously affect the quality and appearance of industrial products. In order to improve the classification efficiency and accuracy of surface defect recognition. Firstly, a lightweight and high-precision deep learning neural network, deep parallel attention convolutional neural network, is proposed in this paper. In this network, fusion attention module is combined with parallel multi-scale module to focus important feature information and capture context feature information without losing important feature information. Secondly, in order to verify the lightweight of the proposed model, the Parameters, FLOPs, Inference time and FPS of the deep parallel attention convolutional neural network are compared with the current popular classification networks. The experimental results show that the deep parallel attention convolutional neural network can be applied to the actual steel defect detection. The experimental results show that the recognition accuracy, precision and area under the curve of the deep parallel attention convolutional neural network model reach 99.57%, 99.6% and 99.8%, respectively. This method significantly improves the average accuracy of classification and is an effective tool for detecting steel defects.

Construction of a multi-class discrimination matrix and systematic selection of areal texture parameters for quantitative surface and defect classification

The surface of every manufactured component has a topography resulting from its fabrication route, which influences its final functionality (optical performance, wear resistance, aesthetic quality, etc.). While the quantitative characterization of the surface is essential for detecting process-induced variations or defects and predicting functional performance, the plethora of surface descriptors developed by the image processing and surface metrology communities makes the selection of optimal surface descriptors Edisonian in nature and highly dependent on experiential knowledge. This work proposes a systematic approach for selecting surface parameters that best distinguish between different surfaces as grouped by visual or process-related differences. Using a form of univariate analysis rooted in signal detection theory, the predictive capability of a discriminability value, d′, is demonstrated in the classification of mutually exclusive surface states. A “discrimination matrix” that offers a robust feature selection algorithm for multiclassification challenges is also introduced. The generality of the approach is demonstrated on the Northeastern University dataset consisting of intensity images from six different surface classes commonly found in hot-rolled steel strip operations. Using the outlined approach, it was found that only four surface descriptors used in conjunction with a simple decision tree classifier achieved a 95% classification accuracy. Surface descriptors used in the study were limited to those described within the ISO 25178-2 standard, while machine learning approaches were limited to a decision tree classifier. The reasoning for both is to maintain as much algorithm output interpretability as possible; the advantages of such is discussed from the perspective of the larger goal of linking surface texture to manufacturing processes and surface functionality fundamental mechanisms.

Flow characteristics of cooling nozzle for high strength after hot rolling

To further improve the cooling strength and uniformity of hot rolled strip steel after rolling and optimize the structure and process parameters of high-strength cooling equipment, this paper combined the basic structural characteristics and characteristic parameters of high-strength cooling nozzles of hot rolled strip steel, using finite element analysis software. The effect of various pressure and structural arguments on the internal flow characteristics of high-strength cooling nozzles was studied. The results show that with the increasing of the system pressure, the flow rate at the manifold outlet also increases gradually, and the fluctuation of the jet velocity increases gradually, but the flow line trend of the internal velocity is unchanged. The change in the position of the main shunt hole can affect the change in flow rate at the nozzle outlet. However, when the pressure is greater than 0.3 MPa, the fluctuation range of the flow rate at the slot outlet tends to be larger. The relevant results provide some reference for the design of high-strength cooling equipment in hot rolling.

A novel vision-based defect detection method for hot-rolled steel strips via multi-branch network

A novel vision-based defect detection method for hot-rolled steel strips via multi-branch network

Surface defect classification of hot-rolled steel strip based on mixed attention mechanism

PurposeThis paper aims to propose a classification method for steel strip surface defects based on a mixed attention mechanism to achieve fast and accurate classification performance. The traditional method of classifying surface defects of hot-rolled steel strips has the problems of low recognition accuracy and low efficiency in the industrial complex production environment.Design/methodology/approachThe authors selected min–max scaling comparison method to filter the training results of multiple network models on the steel strip surface defect data set. Then, the best comprehensive performance model EfficientNet-B0 was refined. Based on this, the authors proposed two mixed attention addition methods, which include squeeze-excitation spatial mixed module and multilayer mixed attention mechanism (MMAM) module, respectively.FindingsWith these two methods, the authors achieved 96.72% and 97.70% recognition accuracy on the steel strip data set after data augmentation for adapting to the complex production environment, respectively. Using the transfer learning method, the EfficientNet-B0 based on MMAM obtained 100% recognition accuracy.Originality/valueThis study not only focuses on improving the recognition accuracy of the network model itself but also considers other performance indicators of the network, which are rarely considered by many researchers. The authors further improve the intelligent production technique and address this issue. Both methods proposed in this paper can be applied to embedded equipment, which can effectively improve steel strip factory production efficiency and reduce material and time loss.

Micro-Segregation induced strain inhomogeneity in >900 MPa UTS martensitic hot rolled advanced high strength steel

High strength martensitic strip steels used in the yellow goods industry require good ductility and high strength. This paper examines how local compositional inhomogeneity of alloying elements (Mn, Mo, and Cr), caused by interdendritic microsegregation during casting and subsequent deformation, affect local straining behaviour in a >900 MPa autotempered martensite hot rolled strip steel. The alloy rich and alloy depleted bands have been characterised using micro-XRF and SEM-EDX through thickness for a 10 mm thick strip. Tensile samples were selectively machined from areas with a range of microsegregation severity in the longitudinal and transverse orientations. Digital Image Correlation (DIC) during tensile testing was used to examine local straining behaviour and shear band formation. Differential straining of up to 2.5x between solute enriched (lower strain) and solute depleted (high strain) bands was found in the strongly microsegregated transverse samples due to the lower hardness of the solute depleted bands (reduced solid solution strengthening). The high local strain in solute depleted bands in the strongly segregated sample led to premature failure (at lower global applied strain) compared to the less segregated samples. No significant strain partitioning was observed for the longitudinal oriented samples regardless of segregation level.