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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]

Accurate calculation of the heat source in the roll gap during the cold rolling process is the basis for a well-designed coolant system, which is particularly important for reducing roll wear and improving the flatness quality of strip steel. Although many established numerical models have high precision when calculating the work roll temperature, complex modeling processes and large calculation times result in the inability to predict the work roll temperature quickly for the application of automatic control systems. Therefore, the authors developed a new analytical heat source model to calculate the axial temperature distribution of the work roll in the cold rolling process. First, according to the deformation characteristics of strip cold rolling, the deformation zone is divided into a plastic zone and two elastic zones, and the length of each deformation zone is calculated considering the effect of tension. Then, the friction heat generated in the elastic zone is calculated. Second, new exponential velocity and corresponding strain-rate fields satisfying kinematically admissible conditions are proposed to calculate the deformation heat and friction heat generated in the plastic zone. Finally, the work roll temperature prediction model is established by contemplating the heat source of the roll gap, emulsion heat transfer, air cooling, and contact heat transfer with the intermediate roll. By repeatedly optimizing the weighted coefficient d of intermediate principal shear stress on the yield criterion, the maximum error between the calculated results and the actual measured cold roll temperature data was reduced to 3.1%. The effects of the reduction ratio, rolling speed, and resistance to deformation on the deformation heat and friction heat are discussed, and the variation of temperature field of the work roll with time is analyzed quantitatively. [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]

• A validated nonlinear dynamic chatter model of universal crown mill is developed. • Effect of rolling processing parameters on stand stability is given for single stand. • An improved prediction criterion for the critical rolling speed of mill chatter is proposed. • Optimal design of friction and reduction for stands No.4 and No.5 are investigated. Tandem cold rolling mills are comprehensive dynamic systems coupling with structure and rolling technology. A frequent problem is mill chatter, often occurring when rolling hard, thin materials at high speed and affecting the quality of the strip. In this paper, a dynamic increment model for chatter in a Universal Crown Control mill (UCM mill) is proposed with the considering of strip hardening, elastic deformation of the work roll, non-linear friction, transfer delay of the strip, and dynamic coupling effect of tension between adjacent stands. Mathematically, this model is based on the Taylor series expansion of the Bland-Ford-Hill rolling force equation. The comparative results of both simulation and experimental data show that this chatter model has a high accuracy. A novel, time-varying stability criterion is proposed to evaluate mill stability. Further study is provided on the optimal friction coefficient and reduction ratio required to increase the critical rolling speed and vibration stability in a tandem cold rolling mill. This mathematical criterion is validated by comparing the actual rolling speed with the calculated critical rolling speed. The present work provides a more accurate prediction of mill chatter and a basis for optimal design of rolling conditions. [ABSTRACT FROM AUTHOR]

The friction characteristics of rolling interface play a considerable role in rolling stability. In this work, vertical vibration speed is introduced into the Kármán equation. An unsteady lubrication model considering entraining and squeezing effect is proposed. Additionally, a dynamic chatter model is developed by coupling the improved rolling force and unsteady lubrication model. The verification results at the micro- and macro-scales indicate that this chatter model has high accuracy. The effects of main process parameters, such as reduction, back tension and viscosity, on the mill stability were analyzed. Moreover, the changes in parameters and tribological characteristics due to unstable rolling process were investigated. The work concludes that the rapid change of lubrication state and friction stress result in vibration intensification. Image 1 • Developed rolling force model considering vertical vibration speed is proposed. • Friction model that reflects lubrication state during unsteady rolling is obtained. • Natural frequency of the system slightly decreases with rolling speed increasing. • Lubrication state changes tremendously with time during unsteady rolling. • The decrease of average friction power leads to vibration more easily. [ABSTRACT FROM AUTHOR]