Steel Strip Research Articles

Soft sensor modeling of steel pickling concentration based on IGEP algorithm

ABSTRACT Accurate measurement of acid concentration is paramount for ensuring the quality of strip steel pickling. Online measurement, a method that reduces operational complexity and lags effectively, is gradually replacing offline measurement of acid concentration. In this study, an indirect soft sensor model based on the improved gene expression programming (IGEP) algorithm has been constructed, leveraging easily measurable indexes from a large-scale dataset. The IGEP-based model predicted that the mean absolute errors for H+ and Fe2+ concentrations were 1.72 and 1.98 g/L, respectively. Additionally, the goodness of fit values for the H+ and Fe2+ prediction models were 0.945 and 0.933, respectively. Compared with the model based on support vector regression (SVR), which is suitable for small samples, it was demonstrated that the IGEP-based model achieved better predictive performance. Taken together, our study has designed a more effective and practical model for determining the acid concentration of strip steel pickling, providing a new ideal choice for the steel industry, which is of profound significance in the concentration control of pickling solution and the production of strip steel.

Correction to: Nanosecond multi‑passes laser surface texturing on AISI 301LN TRIP steel

Correction to: Nanosecond multi‑passes laser surface texturing on AISI 301LN TRIP steel

Optimisation study of roll profile for controlling high-order wave defects in an ultra-wide rolling mill

This paper investigates the issue of higher-order waviness in the rolling process of wide strip steel, focusing on the different higher-order roll shape compensation curves of Baotou steel 2250 mm hot strip mill. First, three typical higher-order waviness compensation curves are fitted and quantitatively evaluated. Second, a mathematical model of the unloaded roll gap is established to analyse the shape control characteristics of the entire roll gap. Finally, a finite element model of the roll system (work roll Φ700 × 2550 mm and support roll Φ1600 × 2250 mm) is constructed to analyse the effects of changes in the position of the shifting rolls on the cross-section of the strip steel at different widths (1400, 1600, 1800 and 2000 mm) using a controlled variable method. The study finds that the fourth-order roll shape demonstrates good performance in controlling higher-order waviness while maintaining the linear control characteristics of quadratic crown. Considering the magnitude of higher-order waviness in actual rolling processes, the compensation amount for the quartic crown is determined to be 0.05 mm. Based on this, the higher-order work roll shape is optimised, resulting in industrial applications where the flatness hit rate of 1.2–2.5 mm thick strip steel increased from 70.57% to 88.7% and the hit rate for 2.51–4 mm thick strip steel increased from 89.11% to 90.8%. Further optimisation of the roll crown setting from [−0.7 mm, 0.4 mm] to [−0.55 mm, 0.55 mm] and adjustment of the shifting roll position improved the middle waviness, ultimately increasing the flatness hit rate for 1.2–2.5 mm thick strip steel to 89.2% and for 2.51–4 mm thick strip steel to 92.1%, thus validating the effectiveness of the roll shape optimisation.

XAIP: An eXplainable AI‐Based Pipeline for Identifying Key Factors of Surface Defects in Strip Steel

The surface defect has a direct impact on the performance and quality of the final product, so it is crucial to identify the key factors causing the defect. To achieve accurate key factor identification, an eXplainable AI‐based Pipeline (XAIP) is proposed herein. The proposed XAIP combines data mode clustering and local model construction to meet practical application requirements. Meanwhile, after analyzing the characteristics of practical data, a strategy for defect rate calculation is designed to provide more fine‐grained defect‐related information to supervise model training. The final identification results are presented by a two‐stage explainable method, which is designed to reveal the information of data modes and key variables and can give engineers more specific defect‐related information. The experiment results based on two practical manufacturing datasets show the effectiveness of the proposed method.

A Strip Steel Surface Defect Salient Object Detection Based on Channel, Spatial and Self-Attention Mechanisms

Strip steel is extensively utilized in industries such as automotive manufacturing and aerospace due to its superior machinability, economic benefits, and adaptability. However, defects on the surface of steel strips, such as inclusions, patches, and scratches, significantly affect the performance and service life of the product. Therefore, the salient object detection of surface defects on strip steel is crucial to ensure the quality of the final product. Many factors, such as the low contrast of surface defects on strip steel, the diversity of defect types, complex texture structures, and irregular defect distribution, hinder existing detection technologies from accurately identifying and segmenting defect areas against complex backgrounds. To address the above problems, we propose a novel detector called S3D-SOD for the salient object detection of strip steel surface defects. For the encoding stage, a residual self-attention block is proposed to explore semantic information cues of high-level features to locate and guide low-level feature information. In addition, we apply a general residual channel and spatial attention to low-level features, enabling the model to adaptively focus on the key channels and spatial areas of feature maps with high resolutions, thereby enhancing the encoder features and accelerating the convergence of the model. For the decoding stage, a simple residual decoder block with an upsampling operation is proposed to realize the integration and interaction of feature information between different layers. Here, the simple residual decoder block is used for feature integration due to the following observation: backbone networks like ResNet and the Swin Transformer, after being pretrained on the large dataset ImageNet and then fine-tuned on a smaller dataset for strip steel surface defects, are capable of extracting feature maps that contain both general image features and the specific characteristics required for the salient object detection of strip steel surface defects. The experimental results on the SD-saliency-900 dataset show that S3D-SOD is better than advanced methods, and it has strong generalization ability and robustness.

Influence of Strain Rate on Barkhausen Noise in Trip Steel.

This paper deals with Barkhausen noise in Trip steel RAK 40/70+Z1000MBO subjected to uniaxial plastic straining under variable strain rates. Barkhausen noise is investigated especially with respect to microstructure alterations expressed in terms of phase composition and dislocation density. The effects of sample heating and the corresponding Taylor-Quinney coefficient are considered as well. Barkhausen noise of the tensile test is measured in situ as well as after unloading of the samples. In this way, the contribution of external and residual stresses on Barkhausen noise can be distinguished in the direction of tensile loading, as well as in the transversal direction. It was found that the in situ-measured Barkhausen noise grows in both directions as a result of tensile stresses and the realignment of domain walls. The post situ-measured Barkhausen noise drops down in the direction of tensile load due to the high opposition of dislocation density at the expense of the growing transversal direction due to the prevailing effect of the realignment of domain walls. The temperature of the sample remarkably grows along with the increasing strain rate which corresponds with the increasing Taylor-Quinney coefficient. However, this effect plays only a minor role, and the density of the lattice imperfection expressed especially in terms of dislocation density prevails.

Surface Defect Identification of Strip Steel Using ViT‐RepVGG

In the production of strip steel, surface defect identification is crucial for improving product quality and ensuring smooth subsequent processes. Existing technologies face challenges such as low detection efficiency and susceptibility to environmental noise. This article employs an automated deep learning method without requiring consideration of complex environmental changes and proposes an improved RepVGG (ViT‐RepVGG) model for surface defect identification. The model is based on the RepVGG architecture, and the study explores the impact of incorporating the self‐attention mechanism of ViT under various addition strategies on model performance. A comparison is made between the optimized model and classic network models, as well as recently published models, in terms of identification performance. The research also examines the performance variations of the model under different hyperparameter settings and its identification performance for six types of defects. The results indicate that adding the ViT module to stage 3 of the A1‐type RepVGG, with a learning rate, optimizer, and activation function set to 0.0001, Adam, and Gelu, respectively, yields the optimal ViT‐RepVGG model performance. These findings demonstrate the feasibility of enhancing classification performance by incorporating the self‐attention mechanism into neural networks, providing an effective foundation for the online identification of strip steel surface defects.

Effect of hot coil immersion cooling process on microstructure and properties of strip steel and oxide scales

ABSTRACT Coils obtained at the end of hot rolling of four different low carbon steels (DX54D, HX180YD, DX51D and HCT590X) were subjected to immersion in water for cooling (IC) as against the normal natural cooling (NC). IC produces significant improvements in the microstructure, scale formation, distribution of mechanical properties and surface quality. A model developed to simulate thermal history in the hot coil being cooled indicates that the 0.1°C/min core cooling rate in NC increased by over 7 times in IC, thereby decreasing the temper softening effects. Results of the tests performed exhibit a greater uniformity in the longitudinal properties and a 30–50% reduction in the band spread. The volume fraction of carbide on the DX54D steel is reduced by 20–30%, and the yield platform length is increased from 2.6 to 3.4%. The IC process was observed to significantly inhibit the formation of the banded structure in the HCT590X steel and the average yield strength of the coil increased by ∼30 MPa. The thickness of the oxide scale in all the steels decreased by 30–40% and the FeO content in the scale increased by 10–20%.

Order-difference of normalized square envelope spectrum and its applications in early chatter detection of roll grinder

Roll grinder is widely demanded in grinding the surface of rolls that will be used to mill steel strips for high-quality automobile outer panels. The grinding chatter generally leads to poor machining accuracy and massive economic losses. The significance of roll grinder vibration monitoring is to detect the early grinding chatter. However, in harsh working conditions, some chatter detection methods inevitably confuse the fault components and interference components in vibration signals and the chatter feature information cannot be extracted exactly. The dynamical model of roll grinder system reveals that the spectrums of vibration signals contain intrinsic frequency components and chatter frequency components under health and chatter conditions. Based on the analytical modelling, a new health index order-difference of normalized square envelope spectrum (ODNSES) is proposed to detect early chatter and evaluate the health degradation of the roll grinder. The normalized square envelope spectrum (SES) is used as the pretreatment of ODNSES to compare the frequency components in vibration signals under health and early chatter conditions. Furthermore, order-difference of normalized SES is proposed to suppress the noise interference and to enable ODNSES to characterize the grinding chatter process. The detailed simulation research is performed to verify three important properties of ODNSES. The experimental results show that ODNSES would not be interfered by background noise significantly and it is on average 29.12% more sensitivity to impulse amplitudes than classical health indexes. The testing results on actual roll grinder exhibit that ODNSES can detect the early chatter and quantify the abnormal vibration in the grinding process.

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

Optimization Study on Hardness of Cold Rolled Strip Steel Based on a Data-Driven Approach

As a key index to measure product quality and processability, the hardness of cold-rolled strip steel has an important influence on the production efficiency of iron and steel enterprises. However, due to the coupling between the process parameters at each stage of the continuous annealing process, and the setting of traditional process parameters depending on the experience of the operator, the lack of scientific and systematic optimization methods, which easily leads to the problem of high hardness fluctuation and low resource utilization. In this study, the data-driven method is used to conduct a preliminary correlation analysis to further clarify the optimal combination of process parameters for achieving the target hardness. The Pearson correlation coefficient is used to determine that carbon content, quenching furnace temperature, and strip speed significantly affect strip hardness. To further clarify the optimal combination of process parameters to achieve the target hardness, a Genetic Algorithm (GA) was tried in this study. The results show that the optimized process parameter combination can effectively improve the stability and consistency of strip hardness, and provide a scientific basis for the process parameter setting in the cold rolled strip production process, which is helpful for enterprises to improve product quality, reduce cost, and enhance market competitiveness.

Environmental impacts of mechanically stabilised earth walls

Mechanically stabilised earth (MSE) walls have gained widespread popularity in North America because of their cost-effectiveness and reliable performance. Although the use of carbon-intensive materials for MSE walls is significantly lower compared to conventional retaining walls like concrete gravity walls and cantilever walls, heavy earthwork and transportation activities are involved in the construction of MSE walls, which increase their carbon footprint. The intensity of construction and transportation activities need particular attention because these are heavily reliant on the combustion of fossil fuels. In this study, the environmental impacts of MSE walls, reinforced by two common types – steel strips and geogrid – are quantified using life cycle assessment (LCA). A parametric study is conducted to investigate the effects of (i) soil properties, (ii) material properties of reinforcement, (iii) applied load, (iv) design parameters, and (v) hauling distances of materials on the global warming impact of MSE walls. Based on the study, reference charts are developed for the purpose of quick estimation of global warming impact of MSE walls based on design dimensions, applied load, and site conditions.

Research of Asymmetric and Symmetric Cold Rolling Influence on Microstructure, Mechanical Properties and Stress–Strain State of Steel Strips

Research of Asymmetric and Symmetric Cold Rolling Influence on Microstructure, Mechanical Properties and Stress–Strain State of Steel Strips

Theoretical Study of Asymmetric Bending Force on Metal Deformation in Cold Rolling

A three-dimensional elastic–plastic finite element model of a six-roll cold rolling mill has been developed using the finite element software ABAQUS. The actual parameters of the rolling mill have been incorporated into the finite element model, with the working conditions applied as boundary constraints and load conditions. Subsequently, a non-symmetrical bending force is introduced to the finite element model. Through simulation calculations, this study analyzes the patterns of change in the transverse pressure of the rolling mill and roller pressure during non-symmetrical bending, as well as the variations in strip thickness, crown, edge drop, and flatness. Additionally, the regulating function of the bending force is examined. Each adjustment of 5 t in the asymmetric bending force results in an increase of approximately 0.01 mm in the thickness of the positive bending side of the strip while causing a decrease of about 0.01 mm in the thickness of the negative bending side. Therefore, the application of asymmetric bending forces proves to be effective in controlling the shape of lateral wave defects on the edges of steel strips.

Flatness jump phenomenon in cold-rolled steel strip with high temperature: Root cause and solution

The steel strip produced in the 20-high cold rolling mill has the characteristic of high temperature in a certain factory, and a rare strip flatness phenomenon occurs in the fourth pass, different from the common flatness defects, the flatness value of each position in the strip width shows an irregular and drastic fluctuation state along with time, and it is called the flatness jump phenomenon, which seriously affects the stability of the rolling process and the production efficiency. Aiming at this problem, a method combining the rolling experiment with the numerical simulation is used to study its generation mechanism and seek for effective control technique. Through the thermal imaging measurement and the analysis of the production data, the temperature fluctuation in the fourth pass is the main reason for the flatness jump, which is related to the nucleate boiling state when the emulsion impacts on the high-temperature strip surface in the cooling process. Based on the rolling control experiments of different cooling factors, the critical temperature range for the occurrence of flatness jump is determined, which is used for the improvement of cooling technique subsequently, and the range of emulsion exit flow is optimized to 500 L/min ∼ 2050 L/min. The rolling experiment is carried out in the industrial field, it is found that the production process is stable and the flatness jump does not appear, with the small value basically within the range of ± 10 IU, and the scheme is planned for long-term application after a period of optimization, which could ultimately enhance the stability of the rolling process and the performance of finished product. Further, the paradigm and information provided in this work would be beneficial for understanding the formation mechanism of the similar complicated flatness phenomenon in cold rolling and optimizing the industrial applications of cooling technology.

A Lightweight Strip Steel Surface Defect Detection Network Based on Improved YOLOv8

Strip steel surface defect detection has become a crucial step in ensuring the quality of strip steel production. To address the issues of low detection accuracy and long detection times in strip steel surface defect detection algorithms caused by varying defect sizes and blurred images during acquisition, this paper proposes a lightweight strip steel surface defect detection network, YOLO-SDS, based on an improved YOLOv8. Firstly, StarNet is utilized to replace the backbone network of YOLOv8, achieving lightweight optimization while maintaining accuracy. Secondly, a lightweight module DWR is introduced into the neck and combined with the C2f feature extraction module to enhance the model’s multi-scale feature extraction capability. Finally, an occlusion-aware attention mechanism SEAM is incorporated into the detection head, enabling the model to better capture and process features of occluded objects, thus improving performance in complex scenarios. Experimental results on the open-source NEU-DET dataset show that the improved model reduces parameters by 34.4% compared with the original YOLOv8 algorithm while increasing average detection accuracy by 1.5%. And it shows good generalization performance on the deepPCB dataset. Compared with other defect detection models, YOLO-SDS offers significant advantages in terms of parameter count and detection speed. Additionally, ablation experiments validate the effectiveness of each module.

YOLO-LFPD: A Lightweight Method for Strip Surface Defect Detection.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction, slow detection speed, and insufficient datasets, YOLOv5 is improved on the basis of YOLOv5, and the YOLO-LFPD (lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, a GA genetic algorithm with OTA loss function, further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

Optimization of small coal pillar width and control measures in gob-side entry excavation of thick coal seams

This study introduces an innovative optimized bolting support system specifically tailored for gob-side entry excavation in thick coal seams at a coal mine in southwestern Shandong, China. Employing theoretical analysis, numerical simulation, and field measurements, the research focuses on examining the failure characteristics of surrounding rock during gob-side entry excavation. The key innovation lies in the development of a 5-meter optimal coal pillar width, ensuring balanced stress distribution and structural integrity. Additionally, a lag time of at least 46 days between gob-side entry excavation and the upper working face retreat is recommended to mitigate roof subsidence and surrounding rock deformation. The optimized bolting support system, featuring increased bolt pretension, utilization of high-strength steel strips, and reinforcement of weak points, effectively reduces deformation of the roadway surrounding rock, meeting support requirements for normal production. This novel approach successfully addresses the support challenges in thick coal seam gob-side entry excavation, enhancing mining safety and resource recovery rates.

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.

Analysis of fatigue damage of hot rolling work rolls coupled with wear effect

During the hot rolling process, the work roll experiences cyclic contact stress due to the deformation of the strip steel, as well as thermal stress from periodic heating and cooling. The surface failure of the work roll results from the combined effects of wear and fatigue damage. This study proposes a comprehensive approach to analyzing coupled wear and fatigue damage using a method based on the Manson-Coffin equation, modified slope formula, and the Miner criterion. Finite element models were developed to simulate simplified heat transfer and thermal coupling, taking into account the actual rolling parameters and high-temperature material properties of the work roll. The evolution of the physical field of the work roll during the rolling process was analyzed. The analysis of these models revealed that the maximum strain on the work roll occurred at the rolling exit and was predominantly influenced by temperature. The effects of rolling speed and temperature on work roll strain varied, with fatigue damage being significantly reduced when wear was considered compared to scenarios where wear was not accounted for, under the same number of cycles.