Aiming at the challenge of integrated control of key indicators such as shape, properties, and surface quality in the production of high-quality sheet and strip, the achievements of China in the integrated control technology of shape-property-surface for high-quality sheet and strip were introduced. The strip shape control technologies of multi-modal information fusion detection and multi-stand and multi-process coordination were briefly described from the aspects of straightness detection of hot rolled strip, intelligent prediction system of roll and strip shape state, comprehensive control model of roll bending and axial shifting, and strip shape feedforward control of cold and hot rolling process. The control technologies of hot rolling process and mechanical properties based on large model were briefly described from the aspects of microstructure parameters and mechanical properties control model of hot rollingprocess and pre-control of microstructure properties of hot continuous strip rolling. The intelligent simulation and control technology of high-quality strip surface characteristics was briefly described from the aspects of unsupervised classification detection algorithm for surface defects and comprehensive control of surface defects and characteristics. On this basis, the field applications of these technical achievements were described, and the development of comprehensive control of high-quality sheent and strip was prospected.
During the rolling process of medium and heavy plates, various types of plane shape defects may occur, which severely affect product quality and yield, thereby constraining performance and production line efficiency. As a critical technical aspect for ensuring the dimensional quality of final products, planar shape control has long been a key focus in the field of steel rolling. The development of planar shape control technologies for medium and heavy plates are systematically reviewed, covering the evolution from fundamental theories and experimental studies to engineering applications, with concise analysis, comparison, and commentary. Furthermore, recent advances in intelligent equipment and data-driven control technologies are summarized, and future research directions are discussed in the context of the steel industry's ongoing shift toward intelligent manufacturing. The aim is to provide theoretical support and practical insights for the continued optimization and upgrading of planar shape control technologies.
Front-end warping and bending of slab during hot roughing rolling is a common asymmetric shape defect. Traditional control methods typically adopt an integral control strategy for the slab head, which may result in excessive warping, leading to slab collisions with production line equipment and disruptions in rolling rhythm. To address this issue, a segmented control method for slab front-end warping and bending was developed and validated through finite element simulations. The proposed method consists of two stages,i.e. the sled coefficient action stage and the upper/lower roll speed reversal stage. Using machine vision technology, the amount of warping or bending between passes is detected, and the corresponding adjustment value is calculated through the segmented control strategy in combination with the front-end warping and bending monitoring and control system. The adjustment command is then automatically applied before the next rolling pass, thereby realizing automatic control of slab front-end warping and bending. Application results demonstrate that the segmented control method reduces the slab head warping height by 77.04% compared with integral control, significantly mitigating front-end warping and bending defects and enhancing the stability of product quality.
The strip crown plays a crucial role in determining the quality of products in strip hot rolling. Hence, achieving precisely hot-rolled strip crown diagnosis is important to improve the control capability of hot rolling. Since the hot rolling process features nonlinearity, heredity, and strong coupling, the diagnosis of strip crown is an imbalanced problem with complex decision boundaries. Existing crown prediction models tend to learn more information from the majority class, but ignore the data of the strip with unqualified crown. To overcome this limitation, a hot-rolled strip crown diagnosis model based on the fusion of hybrid resampling and cost-sensitive is proposed, and the cost-sensitive factor is obtained by artificial hummingbird algorithm. Some advanced machine learning models are selected as comparison models. The experimental results demonstrate that the proposed model outperforms all comparison models with the AUC of 0.889 and defect recall of 0.870. Moreover, the testing time of the proposed model is only 0.0059 s. After the madel was applied to online diagnosis,the detection rate of defect crown strips increased from 82% to 88%,and the crown compliance rate of strips improved from 59% to 71%.
In the production process of medium and heavy plates, the edge parts of plates often present irregular shapes. These irregular areas are generally unsuitable for further processing or application and need to be accurately cut off. To improve shearing efficiency and accuracy, an intelligent cut-to-length technology for segmented shearing of medium and heavy plates, which combines machine vision and PLC control, has been proposed.Firstly, the camera is accurately calibrated using Zhang Zhengyou's calibration method, and the spatial positional relationship between the camera and the shearing machine is determined. Then, the RGB color values of the image are extracted and further converted to the HSV color space, so that the image features are effectively extracted, and thus the endpoints of the plate head are accurately identified. Moreover, the distance between the endpoints in the world coordinate system is calculated by means of pixel coordinate conversion.When it is detected that the width of the plate head is stable and the number of endpoints is two, the system automatically determines that it is the regular position of the plate head. Subsequently, the system calculates the distance from the edge point of the length-measuring laser line to the regular position, so as to determine the target shearing position. The target position is sent to the PLC control system, and the roller table is controlled by the PLC to complete the shearing action.This intelligent fixed-length technology not only improves the accuracy of plate end-point recognition,but also significantly enhances the shearing accuracy and product quality, with the fixed-length accuracy controlled within ±5 mm.
In the plate production process, accurate contour measurement is not only the key cornerstone for optimizing the rolling process and adjusting equipment parameters, but also a decisive factor in ensuring product quality accurately meets customer needs. Considering that the current advanced plate surface defect detection system can efficiently produce detailed image data, this paper attempt to explore a new path to integrate surface defect detection and plate contour measurement, striving to achieve the high-precision goal of contour measurement at a lower cost. By fully utilizing the high-resolution plate images output by the surface defect detection system, a series of data processing steps are implemented, including image distortion correction, precise adjustment of attitude and tangential angles, and thickness and jitter compensation algorithms based on binocular vision technology, in order to accurately restore the actual size information of the plate. During the process, a dual-position detection and calibration mechanism for the upper and lower surfaces of plate is also introduced, effectively eliminating measurement errors caused by lateral displacement during plate movement, ensuring the authenticity and accuracy of contour data. The generated contour data can not only be used for traditional length and width measurements, but also further cover advanced analytical dimensions such as rectangle evaluation and sickle bending detection, providing comprehensive and high-precision data monitoring and feedback for plate production.
The rolling force prediction model is the core of the cold rolling set control system. For a long time, the traditional rolling force theoretical model has exhibited low prediction accuracy and a strong dependence on empirical parameters due to the complex influencing factors in the cold rolling process, such as multivariable interactions, strong coupling, nonlinearity, and time-dependence. These limitations have hindered its ability to meet the production requirements of high-precision cold-rolled ultra-thin-gauge strips. The setting of the rolling force primarily depends on the calculation accuracy of deformation resistance and the friction coefficient. By analyzing the classical Bland-Ford-Hill cold rolling force theoretical model, inverse calculation formulas for deformation resistance and the friction coefficient were established, thereby obtaining their true values. Subsequently, a least squares support vector machine (LSSVM) model optimized by the differential evolution (DE) algorithm (DE-LSSVM) was constructed. By inputting the true values of deformation resistance and the friction coefficient into the DE-LSSVM for training, corrections were made to these parameters, thus optimizing the rolling force theoretical prediction model. Experimental results demonstrate that, compared with the traditional rolling force theoretical model, the deviation of the rolling force prediction model based on optimization of deformation resistance and friction coefficient is controlled within 5%.
In order to improve the setting calculation accuracy of the cold rolling force model, reduce the length of the thickness deviation at the head of the strip, and improve the yield of the strip, basic chimp optimization algorithm is improved. And then, it is used to dynamically adjust for the deformation resistance and friction factor that affect calculation of rolling force, so as to find the best rolling force calculation model. The simulation experiment results show that by the optimized model, the deviation between the set rolling force of each stand and actual value is not more than 2.12%, and its standard deviation is reduced from 10.4% before optimization to 1.2%. Practice production proved that the proportion of the thickness deviation length of the strip head less than 20 m after rolling force optimization has increased from 35.84% before optimization to 60.2%. It is proved that the method can significantly improve the prediction accuracy of the rolling force model and improve the yield of the strip.
The pressure sensor in F4 stand of finishing mill of a pickling tandem cold mill unit is installed at the hydraulic valve station. There is a long pipeline between the valve station and the pressing oil cylinder, and there is pressure loss in the hydraulic pipeline, which leads to pressure measurement deviation and affects the accuracy of finishing rolling force calculation. The accuracy of rolling force calculation directly affects the accuracy of strip shape and thickness control. In response to the above issues, a pipeline pressure loss model is established to calculate the pipeline pressure loss, and the correctness of the pressure loss model is verified through simulation and experiments. Then, a rolling force compensation model considering pressure loss is established to analyze the deviation between thepressure at the valve station and the pressure in the hydraulic cylinder. The deviation rolling force is compensated by the rolling force compensation model, and the accuracy of the compensated rolling force calculation is improved compared to before compensation. The average deviation of rolling force calculation has decreased from 3% before compensation to 0.8%, the caculation accuracy of finishing rolling force is improved.
Combining the calculation of resistance to deformation of rolled piece with the based finite element calculation of elastic deformation of roll system, the model of shape control in rollingsilicon steel thin-gauge strip with Sendzimir 20-high mill was established.By calculating the deformation resistance of the rolled piece based on the preset shape and iteratively calculating the elastic deformation between the roll system under the rolling force of the rolling mill, the influence laws of the support roll gap shape adjustment (ASU) and the taper change of the No.1 intermediate cone roll on the shape of the rolled piece were determined.The adjustment of ASU in the middle of the support roll mainly affects the area of 0.6 m at the center of the width of the rolled piece, and has almost no effect on the area of 0-0.2 m at the edge. The adjustment of ASU at both ends of the support roll has an impact on the edges of the rolled piece, and also on the middle part of the rolled piece. Increasing the taper of the intermediate roll has a significant effect on improving the strip crown at a distance of 0-0.15 m from the edge of the rolled piece. By optimizing the calculation of the ASU of the roll system and the axial shifting amount of the intermediate roll, the average difference of transverse of the same silicon steel thin-gauge strip rolled by Sendzmir 20-high is reduced by more than 25%,and the shape control of the 0.25 mm thickness strip reaches below 8 IU, effectively improving the shape control capability of the Sendzimir 20-high mill for silicon steel thin-gauge strip.
During strip cold rolling, tension can alter the deformation resistance of the strip, thereby inducing fluctuations in thickness of strip; whereas variations in strip reduction, in turn, trigger changes in strip tension. Under the mutual interaction between tension and thickness, tension-thickness coupled fluctuations arise. Such fluctuations adversely affect both the thickness accuracy of the product and the stable operation of the mill. Focusing on the frequent occurrence of tension-thickness coupled fluctuations during thin-gauge strip rolling on a Sendzimir 20-high reversing cold rolling mill, this study investigates the structural principles of thickness control, tension control, and the inertial compensation function of Automatic Gauge Control (AGC). As demonstrated in this study, sudden fluctuations in the inlet strip linear speed are identified as the root cause of tension-induced elastic oscillations.The interaction between these elastic oscillations and AGC roll gap adjustments gives rise to tension-thickness coupled fluctuations; consequently, the inertial torque compensation adjustment of AGC must remain fully effective throughout the entire rolling process.To address this requirement, the calculation procedure for AGC inertial compensation torque is deduced, and the factors contributing to the failure of AGC inertial torque compensation are analyzed. By optimizing the torque dead-zone parameters of the uncoiling drive and enhancing the tension setting during the tail rolling stage of thin-gauge strip, the effectiveness of AGC inertial torque compensation is maintained throughout the rolling process, and tension-thickness coupled fluctuations are eliminated. Furthermore, a variable-speed integral control algorithm is employed to suppress tension-induced elastic oscillations, thereby improving the control precision of the tension closed-loop system. After system optimization, tension-thickness coupled fluctuations during thin-gauge strip rolling are notably mitigated, achieving a steady-state tension control precision of ±3% and a thickness control precision of ±2 μm.
Aiming to address the dual-challenges of complex pattern interference and real-time demand in color-coated sheet surface defect detection, this study proposes an unsupervised color-coated sheet defect detection method based on cross-layer attention fuzzy. By constructing a dynamic fuzzy adjustment mechanism, the fuzzy parameters are adaptively optimized by combining the local gradient information of the image, and the edge features of the defects are retained while the background noise is suppressed. A cross-layer attentional feature enhancement network is designed, which fuses multi-scale features and adaptively weights them, and significantly improves the sensitivity to tiny defects and general defects. And an adversarial training framework is introduced, which realizes the alignment of unsupervised feature distributions, and reduces the dependence on the labeled data. The experimental results show that the method achieves 98.21% accuracy in color-coated sheet surface defect detection, the false alarm rate is reduced to 1.2%, the 4 K image processing latency is 32 ms, the recall rate of micro-defects (less than 0.2 mm) reaches 96.12%, and the recall rate of general defects (0.2 mm and above) reaches 98.32%, and the robustness reaches 95.67% under the complex texture scenario.In the drawing, spotting, abrasive defect detection tasks, the comprehensive performance is significantly better than the comparison models, in which the accuracy of drawing defect detection reaches 96.55%, speckle defect reaches 95.89%, and abrasive defect reaches 97.21%. The method solves the bottleneck of traditional algorithms in texture-defect decoupling and real-time balance through the synergistic effect of dynamic fuzzy and cross-layer attention mechanisms, and provides a feasible solution for high-precision real-time detection in industrial scenarios.
The effective surface defects detection of strips is of great importance for ensuring product quality. However, due to low contrast and small target size, existing detection methods often face challenges in achieving sufficient detection accuracy. Therefore, a strip surface defect detection algorithm based on semantic enhancement and local attention mechanism (Scale Aware and Local Attention Detection, SALADet) is proposed. Firstly, a semantic interaction enhancement module is embedded in the backbone network to extract and enhance high-level semantic information in deep feature maps, improving the network's ability to distinguish between background and defects. Secondly, a local attention pyramid is introduced in the neck structure of the network to enhance the feature extraction of small targets, thereby improving the detection accuracy of small-scale objects. To further enhance detection performance, SALADet algorithm employs a decoupled detection head, effectively alleviating the conflict between classification and regression tasks, thus improving overall detection accuracy. Experimental results on the NEU-DET dataset show that the mean average precision of SALADet algorithm reaches 79.4%, representing improvements of 4.7%, 14.1%, 4.5%, 4.6%, and 6.1% over Faster R-CNN, SSD, YOLOX, YOLOv8 and CenterNet algorithms, respectively. Additionally, SALADet algorithm achieves an inference speed of 84.7 frames per second, demonstrating excellent real-time performance and practicality.
During the hot rolling process, the precise positioning of steel billets is of paramount importance, as it enables automated billet inspection, which in turn enhances both production efficiency and quality control. However, the actual production process is arduous due to the harsh conditions of the billet production environment, which presents challenges for plate inspection. To address the problems mentioned above, a robust Yolov8 algorithm for billet detection is proposed. A hybrid attention mechanism network is proposed to solve the problem of shallow feature loss in pyramid networks, enhancing the model's ability to learn local features and improving detection accuracy while maintaining model lightweightness. The implementation method involves the introduction of attention modules into the network with the objective of enhancing the preservation of detailed image feature information, thereby improving the overall detection accuracy of the targets. Subsequently, the NPANet feature fusion structure is designed to enhance the network's ability to fuse multi-scale features of images. This is achieved by refining the convolution modules to make the network model lighter. Finally, the loss function is improved to enhance the algorithm's regression performance and reduce the error in stable box generation. The experimental results demonstrate that the improved NDS-yolov8 model, in comparison to the initial network structure, exhibits a reduction in the weight file size from 6.2 MB to 4.6 MB, a decrease infloating-point performance from 8.1 GFLOPS to 6.4 GFLOPS, and an increase in the mean average precision (PmA@[0.5:0.95]by 0.5% at different IoU values. Compared to actual values in real-world scenarios, the NDS-yolov8 network model demonstrates significantly reduced error margins relative to the original Yolov8 network model. It achieves more accurate estimation of the billet's real-time position, thereby effectively enhancing the performance of billet detection and localization.
Due to harsh production environment and numerous coupling factors of control system, the process of heating furnace is always a weak link in the automation degree of production line, which affects the intelligent and digital process of production line. With the continuous update and development of big data technology, it has gradually entered the field of metallurgical industry, aiming at using data mining technology to establish data-driven mathematical models, so as to break through the bottleneck of traditional models. This paper introduces the construction of intelligent control system of heating furnace combustion based on big data platform in a certain scene. Big data algorithm is adopted to carry out intelligent optimization control for heating furnace combustion process, improve the control precision of heating furnace. The intelligent furnace temperature control boasts an online rate not less than 93% and discharge slab temperature control accuracy (±12 ℃) of 93.2%, while reducing fuel consumption of 3.5%,achieving energy savings and improve product quality.
In strip pickling lines, conventional fuzzy PID temperature control suffer from sensor noise, mismatched fuzzy rules, and limited adaptability. To overcome these drawbacks, a hybrid strategy for optimizing fuzzy PID that integrates local outlier factor (LOF), Kalman filter (KF) and whale optimization algorithm (WOA) is proposed. Firstly, LOF combined with a moving-average filter detects and corrects abnormal sensor readings, reducing their influence on the control loop. Secondly, KF fuses the cleaned data to suppress noise and disturbances. Finally, WOA optimises the fuzzy PID parameters on-line, minimizing manual tuning effort and improving accuracy. Simulation results show that, compared with conventional PID and standard fuzzy PID, the proposed scheme shortens settling time by 30.2 % and 17.3 %, and cuts overshoot by 2.56 % and 1.88 %, while markedly enhancing accuracy, robustness and disturbance rejection. The overall effect of temperature control during the pickling process of strip is optimized. These improvements contribute to more sustainable pickling operations, higher productivity and lower costs, and provide a transferable reference for PID enhancement in other industrial processes.
The grade determination of scrap steel is a critical step for achieving efficient recycling of steel resources. To address the limitations of existing methods, such as insufficient detection accuracy and low efficiency, this paper proposes an intelligent scrap steel determination algorithm based on improved DeepLabv3+ convolutional neural network. The algorithm incorporates a coordinate attention block hybrid attention mechanism after the atrous spatial pyramid pooling (ASPP) module and replaces some dilated convolutions in the ASPP with deep strip-shaped dilated convolutions. A comprehensive dataset of scrap steel pile images under various real-world conditions is constructed, including different material types, perspectives, and time periods, to train the intelligent determination model. The improved algorithm significantly enhances detection accuracy. In control groups using ResNet as the backbone network, the mIoU increased by approximately 2.54%, while with Xception as the backbone, the mIoU improved by about 4.42%, effectively boosting the semantic segmentation precision for scrap steel. A conversion model based on thickness and distance factors was established to transform the pixel proportion occupied by various types of scrap steel in images into actual weight proportions. A fully connected network was employed to align the algorithm's output with manual worker annotations. Extensive experiments demonstrate that the classification accuracy of the model in this paper reaches 93.75%, significantly outperforming existing methods and meeting practical production requirements.
Aiming at the problems of significant differences in cross-domain data distribution, scarcity of labeled data, and neglect of subdomain boundary information by traditional domain adaptive methods in industrial bearing fault diagnosis, this study proposes an unsupervised bearing fault diagnosis method based on categorical disparity-adversarial adaptive networks. The method innovatively integrates the subdomain boundary refinement alignment mechanism, and significantly improves the cross-domain feature consistency by combining the hybrid architecture of one-dimensional convolutional neural network and gated recurrent unit to collaboratively model the local time-frequency features and long-range temporal dependence. The adversarial adaptive feature generator-discriminator network is designed, and the dynamic game mechanism is introduced to optimize the training process, and the L2 paradigm constraints are utilized to force the potential spatial geometric consistency, effectively suppressing noise interference and realizing efficient generation of domain-invariant features. A multimodal fault classification framework is constructed and an attention-weighted nonlinear fusion strategy is adopted to dynamically integrate the changes in the time-frequency characteristics of vibration signals, which improves the classification accuracy of complex fault modes. The experimental validation on the CWRU bearing dataset shows that the model in this paper performs well in the experimental groups of C1-C6, C7-C12 and C13-C18 containing different rotational speeds (1 797、1 772、1 750 r/min) and fault degrees (0.177 8、0.355 6、0.533 4 mm), with the average recognition accuracies reaching 91.52%, 94.65% and 91.40%, which is significantly better than the comparison models of REB-ADDA, MsDCNs, SDA, and ISAMCN. In the hyper-parameter configuration with a learning rate of 0.001 and a batch size of 64, the average recognition accuracy of the C1-C6 group is as high as 98.7%, which is an improvement of 6.2% over the optimal baseline model, and the highest precision rate of 98.5%, recall rate of 98.2%, F1-score of 98.3% and other indicators are outstanding. The t-SNE visualization results clearly show that the boundaries of different fault clusters are distinct, and the separation of the inner ring and rolling body fault features is significant, which effectively proves that the model's feature discriminative ability and interpretability, and provides a solution with high precision and robustness for the intelligent operation and maintenance of industrial bearings.
Aiming at the problem of fatigue failure of rolls caused by alternating contact stress of Sendzimir mill, the contact stress distribution and fatigue failure mechanism of rolls were explored. Based on the influence function method, the contact pressure calculation model between rolls was established. The crack initiation life model was established by using the local stress and strain method of damage mechanics. The crack propagation life model was established by using the crack growth rate method of fracture mechanics. The real-time production data was integrated to develop the roll fatigue life prediction model system. According to the internal fatigue distribution of the roll, the grinding amount can be reasonably formulated, the over-limit fatigue layer can be removed, the safety of the roll can be guaranteed, and the rolling kilometers can be effectively increased. It is of great significance to realize precise grinding roll, precise preparation roll and precise use roll.
Due to factors such as the long-term operation of industrial conveyor belts, accidents of belt breakage in bulk material upward conveyor belts often occur. The belt breakage monitoring and protection system is mainly suitable for monitoring the catching of sliding broken belts when a broken belt accident occurs to prevent it from causing significant economic losses and personal injury accidents. This article analyzes the advantages and disadvantages of existing representative broken belt monitoring and protection technologies, analyzes the key requirements and pain points that need to be focused on in broken belt monitoring and protection technology, and has designed a new solution. The application of the new belt breakage protection system can improve the safety factor, operational stability and efficiency of the belt conveyor area. After the application of the new protection system and digital transformation, the belt conveyor system can easily connect with the production management system in the factory to provide data support. The belt conveyor system can be upgraded and transformed according to the intelligent integration platform of the belt conveyor to improve overall production efficiency.
In order to solve the problem of excessive manual intervention in the process of continuous hot-dip galvanizing roll coating, the chemical roll coating machine of hot-dip galvanizing unit was taken as the research object. Firstly, aiming at the complex characteristics of the roll coating treatment in the industrial production process, the automatic control and enhancement scheme based on the existing equipment capacity of the roll coating machine was formed. Secondly, the control model based on multi-factor coupling of industrial production was constructed, and the dual flexible control module of pressure and position was developed to meet the requirements of high-precision adjustment of roll coating process. Finally, the core database of the automatic control system was established, the system link program and transmission control parameters were optimized. At the same time, this technology was applied to a hot-dip galvanizing roll coating machine, achieved an automation rate of over 90% per month and reduced manual operation and lowered the scrap rate. This technology ensured precise adjustability and uniformity of coating weight of the strip. It met the requirements of downstream customers for the surface roll coating quality of hot-dip galvanized products, improved the automatic control level of the roll coating machine, and created considerable economic and social benefits.
Aiming at the problem of excessive manual intervention of the existing cold rolling finishing unit, the control system and core database of the existing finishing unit were analyzed, and the technical scheme of automatic control and upgrading of finishing unit was formed. Secondly, the alternating control strategy of constant elongation and constant rolling force mode was determined, the automatic function of logic control and execution control module were improved, and the core database of the control system was expanded and refined. Finally, the process adjustment program of constant elongation and constant rolling force was optimized. The feed-forward preset parameters and the flatness feedback closed-loop control and adjustment mechanism of the finishing unit were established, and the automatic control function of the plate shape of the finishing unit was completed. This research is applied to the industrial production, and the automatic control function of the finishing unit based on the online data is realized. The automation rate of the unit increases to more than 90%, the manual operation and the scrap rate are reduced.
The roll gap calculation model of L2 system is the foundation for controlling the thickness of hot rolled medium and heavy plates. Especially when employing the feedback pressure AGC control, the accuracy of roll gap calculation model of L2 system directly affects the thickness of the head and the whole plate, consequently affecting the yield of medium and heavy plates. A 2 680 mm semi-continuous roughing rolling mill utilizes the pressure feedback AGC control system, but faces the problem of low thickness accuracy in rolling stainless steel medium and heavy plates. To address this problem, a multi-factor regression analysis was conducted using process data from the intermediate billet rolling process and actual thickness data measured by the thickness gauge. Sixteen indicators were selected for analysis to determine the correlation and path analysis of the thickness of the intermediate billet in cold state. The analysis results indicate a collinear effect of various factors on the thickness of the billet in cold state. The main influencing factors identified in the process of rolling medium and heavy plates are the roll gap, number of rolled pieces per unit of the roll, and slab discharge temperature. A multivariate fitting regression model was developed based on these key influencing factors to correct the roll gap of the R7 stand during the rolling process. Extensive production practices have demonstrated that the application of the fitting regression model can significantly enhance the setting accuracy of the R7 stand roll gap. This improvement has led to an increase in the overall thickness accuracy rate of medium and heavy plates from 84.47% to 98.92%.
