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As an emerging technology in the production of electrical steel, edge‐drop control (EDC) caters to the increasing requirements for the overall shape quality of strips. Herein this review, the development of EDC theory, method, and control system used in the cold‐rolling process is summarized. Also, in this review, research on EDC theory is found to be based on the 3D plastic deformation model of the strip, considering the lateral metal flow. Further, an analytical model and a numerical calculation method are employed to establish an edge‐drop calculation model with high accuracy and speed. Next, taper‐work‐roll shifting (T‐WRS), a conventional EDC technology, is found to have been greatly improved in actual production. However, T‐WRS technology involves coupling problems in terms of edge drop and crown and flatness control. Finally, the closed‐loop EDC is found to depend on the online WRS of multiple stands. Particularly, the genetic law of strip thickness in the continuous rolling process and the edge‐drop regulation characteristics of each stand in the continuous rolling mill are significant aspects; thus, a reasonable roll‐shifting control strategy must be formulated. Based on the reviewed research, the development prospect of EDC is characterized. [ABSTRACT FROM AUTHOR]

Chatter is a major problem that restricts production efficiency and product quality in the cold rolling process at high speed. Although many research studies have focused on relating rolling parameters to mill stability, information about the roll-stack in the axial direction and the strip in the width direction have not yet been considered due to the limitations of modeling methods. This paper proposes a new finite element model for the Universal Crown Control mill (UCM mill) with an improved Timoshenko beam used to investigate the influence of flatness control actuators on the mill stability. A dynamic rolling force calculation formula coupled with a mixed lubrication model is obtained. Then, the structure–process coupling model is established to simulate the rolling process at both steady and unsteady states. The results show that the work roll bending (WRB) has the greatest impact on the critical speed, followed by the intermediate roll shifting (IRS) and the intermediate roll bending (IRB). During vibration, the distribution of the friction stress varies rapidly, and a negative forward slip appears, which exacerbates vibration. Furthermore, the analysis of the energy dissipation in the rolling interface illuminates the vibration mechanism. Therefore, this study contributes to mechanism improvement and chatter suppression in the cold rolling process. [ABSTRACT FROM AUTHOR]

The control precision of thickness and tension is a crucial indicator for evaluating a tandem cold rolling control system. However, the control mode for field application cannot meet the actual quality requirements. Therefore, a distributed model predictive control (DMPC) strategy combined with neighborhood optimization is proposed to decrease the strip thickness deviation and tension change in this paper. First, a cold rolling model describing the relationship among the process parameters is established for the multi-stand cold rolling system. Then, according to the neighborhood optimization theory, the state evolution equation of the output system on each stand is derived. Furthermore, through proper consideration of the input and state information during optimization, optimal control variables are obtained using the proposed performance index to improve the system performance. A series of simulations were carried out with actual rolling data to analyze and validate the capability of the designed control system. The statistical data show that as roll speed disturbance occurs, the thickness and tension deviations can be controlled within respective ranges of 6 × 10 − 5 mm and 0.012 kN with the DMPC control strategy. In addition, each scan cycle calculation only takes 0.0085 s in such a strategy. Compared with the conventional control method, the thickness and tension DMPC control system provides excellent performance and can effectively enhance the strip product quality. • A state space model of thickness and tension is established with considering all the stands of cold rolling process. • A novel distributed model predictive control strategy based on Neighborhood Optimization is designed for the system. • Compared with other control strategies, the proposed DMPC strategy has excellent control performance. [ABSTRACT FROM AUTHOR]

The accuracy of flatness actuator efficiency (FAE) is a prerequisite to achieve high precision automatic flatness control. This study proposes an accurate acquisition approach of FAE based on the combination of simulation modeling and data‐driven modeling for flatness control of cold‐rolled strip. First, a 3D rolling feature space model is constructed according to the change interval of rolling process parameters based on rolling conditions. A 3D finite element simulation model is subsequently established to determine the prior value of actuator efficiency at each node in the feature space model. Then, to improve the accuracy of prior value, an online optimization model is developed by means of data‐driven mechanism. The sorting algorithm and the central limit theorem are introduced to filter the measured process data and calculate the variable weighting. The processed data are imported into the model to improve the fitting model. Finally, a linear output prediction model based on the trend extrapolation method is proposed to realize the accurate acquisition of FAE under any rolling conditions. The data analysis of the production process shows that the model is adaptive and accurate, which can realize high precision flatness control. [ABSTRACT FROM AUTHOR]

Mill chatter in tandem cold rolling mill is a major rejection to the quality and production of the strips. In most mill vibration models, either the roll mass is usually limited to vibrate in vertical direction and vertical-horizontal directions, or the multiple rolls system is simplified to a single mass system. However, the torsional chatter is also a typical type of mill chatter, and the presence of intermediate roll and backup roll will affect the overall vibration of the mill structure system. In this paper, a newly cold rolling mill vibration model coupled with the dynamic rolling processing model and nonlinear vibration model is proposed with the consideration of dynamic coupling and nonlinear characteristics of the rolling process, multiroll equilibrium, and roll movement in both vertical-horizontal-torsional directions. By using Hopf bifurcation theorem and Routh–Hurwitz determinant, the existence of the Hopf bifurcation point of the mill vibration system and bifurcation characteristics are analyzed. At last, the influence of different rolling conditions on the stability of the coupled mill system is investigated, and these results can also be used to design an optimum rolling schedule and determine the appearance of mill chatter under certain rolling conditions. [ABSTRACT FROM AUTHOR]

The flatness control system of cold-rolled strip is characterized by nonlinearity, strong coupling, and multivariable features. Theoretical models of flatness derived from physical mechanisms of the rolling process are intricate. This complexity makes it challenging to establish an accurate control model. Reinforcement learning (RL) is a machine learning method that uses an intelligent agent to learn from data interacting with its environment. In this study, considering the large quantity of historical data generated by the strip steel control system, we used the RL method to analyze the data offline in order to train a data-driven controller. We combined it with ensemble learning to propose a novel method for flatness control based on ensemble deep reinforcement learning (EDRL). Initially, we constructed a state space model for the cold-rolled flatness utilizing historical production data. This model served as the environment for offline RL training. We present an EDRL-based flatness control method that incorporates the principles of ensemble learning. Ensemble learning amalgamates multiple individual models to achieve superior performances. In the online simulation of rolling production, the EDRL-controlled flatness value was less than 5 (I-Unit) IU from the target flatness value, exceeding the performance of the proportional–integral (PI) controller. The proposed method surpassed the conventional approach in terms of both robustness and its capability to regulate flatness. • An EDRL control strategy is proposed for the cold rolling flatness control problem. • The integration strategy effectively improves the convergence speed of flatness. • The proposed control strategy outperforms state-of-the-art PI controllers. • The proposed integrated strategy exhibits robust stability in industrial scenarios. [ABSTRACT FROM AUTHOR]

• A dynamic velocity field for speed variation conditions is proposed. • A hybrid model combining data-driven and theoretical models is established. • MI improves the accuracy of prediction and reduces the computing time. • LR adjustment enhances the stability of initial network and the convergence speed. • Some parameters of uncertain physical relationship with rolling force are studied. The variations in thickness at the head and tail significantly impact the product quality in tandem cold rolling. Improving rolling force calculation accuracy under speed-up and speed-down conditions is essential to enhance thickness control. This paper proposes a sequential coupled model combining data-driven and theoretical models to predict rolling force under speed variation conditions. To enhance accuracy and overcome the limitations of traditional velocity field, a linear cross-section dynamic velocity field taking into account the effects of roll gap changes on the deformation zone is established. Moreover, a long short-term memory (LSTM) network is constructed to capture sequential information from production data and compensate for changes in friction state caused by speed variation and roughness attenuation in the theoretical model. 12 coils, totaling 12,000 samples, are selected from the process data acquisition system (PDA) and utilized as the dataset after normalization. Mutual information (MI) theory is employed for feature selection to improve the prediction accuracy and reduce the computing consumption. Learning rate (LR) adjustment is applied to enhance the stability of initial network and the convergence speed. Compared to evaluated models, the proposed model has relative error within ±2.5 %, R2 of it is 0.9935 and the prediction time is only around 2.5 ms. The results suggest that the proposed model demonstrates the most favorable overall performance. Additionally, some key rolling parameters whose physical relationship with rolling force remains uncertain are investigated. [Display omitted] [ABSTRACT FROM AUTHOR]