Explore the vast range of titles available.

Precise selective cooling control of work roll can significantly improve the cold rolled strip flatness in steel manufacturing industry. To improve the control accuracy of the coolant output of selective work roll cooling control system, a machine learning (ML) algorithm with differential evolution-gray wolf algorithm optimization support vector machine regression (DE-GWO-SVR) model has been proposed for the first time in this study. This model combines the differential evolution (DE) with grey wolf optimization algorithm (GWO) to improve the optimization performance of the algorithm. Then, the SVR model parameters are optimized with differential evolutionary gray wolf hybrid algorithm (DE-GWO) to improve the regression accuracy. Finally, the influences of data normalization methods and the selection of SVR kernel functions were systematically investigated. Compared with the test results of other regression models, the evaluation index R2 based on the DE-GWO-SVR model is greater and the RMSE, MAE, and MAPE are smaller. The DE-GWO-SVR model performs the best, with a higher regression accuracy than the other regression models. Besides, it has been successfully applied to a 1450 mm five-stand industrial cold rolling mill. The model has higher control accuracy for the thermal crown of the work roll and better control effect for the flatness deviation of the strip steel. This study provides a novel strategy with a help of ML algorithm to effectively improve the flatness quality of cold rolled strips by optimizing the selective cooling control of work roll, which exhibits a great practical application potential in steel manufacturing.

Quarter-wave flatness defect often appears in the DP980 cold rolling process, seriously affecting product quality. To find the causes of this kind of flatness defect, a multi-pass finite element model (FEM) based on a 2130 UCM mill was established, and the accuracy of the model was verified by three methods. The simulation results of the S3 and S4 stands show that the deviation between the simulated and theoretical profiles can be reduced when the work roll bending (WRB) force approaches the positive limiting value; as the WRB force decreases, the reduction in the quarter position of the strip increases significantly. The S5 stand is simulated under different rolling force conditions, and when near the real rolling force, the strip plastic deformation exists only in the central area effect, which is unable to solve the abnormal thickness in quarter position. The control effect of unit forward tension, entry profile, and bending force of the S3 stand was analyzed systematically. The results show that the actual setting value will cause a quarter wave, and improving the WRB force is useful to reduce but cannot eliminate the flatness defect, which is verified by experiments. The work roll compensation curve is designed to eliminate the flatness defect, and the experiment results show that the work roll compensation curve has an obvious control effect on the quarter wave.

During the hot rolling process, the work rolls suffer severe wear, resulting in a relatively short lifespan. Severe roll wear can adversely affect the strip shape while introducing roll wear into the crown calculation model can enhance the model accuracy. Therefore, it is crucial to quantify roll wear during the rolling process. Roll wear is a nonlinear time series and the accuracy of the existing work roll wear mechanistic models is not high. In this paper, a novel prediction model for work roll wear based on TCN-LSTM-Attention is developed. TCN utilizes convolutional structures of local and global information to extract data features, while LSTM focuses on capturing long-term and more complex sequence patterns, effectively handling nonlinear characteristics in data. With the incorporation of attention mechanisms, the model becomes more adept at effectively capturing relationships among different segments within the input sequence, which significantly improves predictive performance and reduces the risk of overfitting. Firstly, outlier cleaning and feature selection are performed using Boruta to construct the data set. Then, the predicted results of the proposed model are compared with the existing time series prediction models. The results indicate that the TCN-LSTM-Attention has the highest prediction accuracy, with an R 2 of 0.989 and an RMSE of 0.0082 μm. Finally, the predicted results of work roll wear are combined with the mechanism to correct the strip crown pre-calculation model, which significantly improves the calculation accuracy.

Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls gradually degrades, which significantly impacts the quality of the final product. This paper presents an improved degradation model based on the Wiener process for predicting the remaining useful life (RUL) of hot rolling work rolls, addressing the critical need for accurate and reliable RUL estimation to optimize maintenance strategies and ensure operational efficiency in industrial settings. The proposed model integrates pulsed eddy current testing with VMD-Hilbert feature extraction and incorporates a Gaussian kernel into the standard Wiener process to effectively capture complex degradation paths. A Bayesian framework is employed for parameter estimation, enhancing the model’s adaptability in real-time prediction scenarios. The experimental results validate the superiority of the proposed method, demonstrating reductions in RMSE by approximately 85.47% and 41.20% compared to the exponential Wiener process and the RVM model based on a Gaussian kernel, respectively, along with improvements in the coefficient of determination (CD) by 121% and 19.76%. Additionally, the model achieves reductions in MAE by 85.66% and 42.61%, confirming its enhanced predictive accuracy and robustness. Compared to other algorithms from the related literature, the proposed model consistently delivers higher prediction accuracy, with most RUL predictions falling within the 20% confidence interval. These findings highlight the model’s potential as a reliable tool for real-time RUL prediction in industrial applications.

Skin-pass cold rolling is a crucial step in sheet metal production, modifying the sheet surface topography, ensuring thickness uniformity, and enhancing tribological performance. A key factor in this process is the surface texturing of work rolls, which, when transferred to the rolled sheet, directly affects lubrication distribution and formability in subsequent stamping operations. Properly textured sheets promote lubricant retention, reducing friction and wear, while roll wear can compromise texture transfer, leading to defects in the final product. This review presents a holistic view of surface texturing from the roll topography to the final product. First, it explores different texturing methods for work rolls, analyzing their efficiency, durability, and impact on texture transfer. Then, alternative texturing techniques and coatings are discussed as strategies to mitigate roll wear. By assessing the relationship between roll texturing and sheet drawability, this study provides insights to improve industrial processes, enhance product quality, and promote more sustainable manufacturing solutions.

In the routine roll shifting mode, work rolls of the continuous variable crown (CVC) hot strip mill are always in repeated shifting positions, which affects the uniform wear of work rolls. As an available solution to the above problem, a new random shifting mode for CVC work rolls has been developed in this paper. According to the relationship between shifting position and bending force, the new CVC shifting mode shifts work rolls in a random pattern within the limits by randomly changing the bending force, so that the roll shifting is dispersed and the strip shape remains good. The Light Gradient Boosting Machine (LightGBM) algorithm is applied to build the prediction models of CVC shifting to accurately express the relationship between shifting position and bending force. Random search and Bayesian optimization are used to optimize the LightGBM models, respectively. By comparison, LightGBM with Bayesian optimization is recommended to predict roll shifting, which is more accurate and efficient than using random search. The new CVC shifting mode has been implemented by an off-line application in the 1780 mm hot rolling line. The results reveal that the proposed CVC shifting mode can well disperse roll shifting positions and accurately control strip shape.

To investigate the edge drop control characteristics of the roll contour on the taper segment of a taper-work roll, a three-dimensional roll stack-strip-tension coupling model was established with the finite element method, and the simulation model was verified using experimental data. The effects of the taper-work roll shifting (T-WRS) for different contours on the edge drop, center crown, flatness, and nonuniformity of the rolling pressure field unevenness were studied. The results indicated that the thickness difference caused by the combination of the roll contour and the work roll shifting (WRS) effectively compensated for the edge drop caused by the elastic deformation of the roll was the main way for T-WRS to reduce edge drop. Increasing the height of the roll contour inserted into the strip could significantly improve the edge drop control ability. However, it would also lead to increasing the trend of M-mode waviness defects and increase the peak of rolling pressure and the unevenness of the contact pressure between the rolls. Therefore, a novel approach for the roll contour evaluation was proposed by constructing the multi-objective function of the taper-work roll shifting. With edge drop control as the primary goal, the optimal range and suitable range of the WRS for different roll contours were calculated for the first stand of the tandem cold mill.

In the process of hot rolling silicon steel, roll wear directly affect its shape. Accurate prediction of roll wear is an important condition for rolling qualified silicon steel strips. The traditional roll wear prediction model is established by the slicing method. The wear of F5–F7 work rolls used for finishing rolling silicon steel on a 2250 mm production line in a steel mill was predicted by this model. It was found that there was deviation between the predicted results and the actual wear, and the prediction accuracy of the model was insufficient. Therefore, the wear of the surfaces of the rolls used for rolling silicon steel on this production line was studied. Based on the analysis of the work roll wear’s form and the rolling parameters that affect the roll wear, the traditional roll wear prediction model was optimized by the genetic algorithm. Finally, the optimized model was verified, and the prediction accuracy of the wear prediction model improved. The accurate prediction results provide a basis for the formulation of a shape control strategy when rolling silicon steel on this production line.

The WRC scheme of misalignment of cylindrical work rolls in quarto stands when they are supported on rolls, the barrel of which is made in the form of a single-cavity hyperboloid, has been considered. The implementation of the scheme together with the HPD solves the key problem of reducing the longitudinal and transverse thickness variations of the rolled strips.

One of the risks in asymmetric sheet rolling is roll slippage relative to the strip, which may cause significant process disruptions. This paper presents analytical methods for assessing and preventing roll slippage, which rely on deformation zone analysis and on the control of the process parameters during the asymmetric rolling of steel strips. An analytical expression is derived that defines the onset condition for slippage as a function of the geometric and load parameters of the deformation zone. A condition for eliminating slippage by adjusting interstand tensions is also formulated. The proposed methods were validated on a continuous cold-rolling mill with asymmetric work rolls.