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Camber in hot-rolled plates significantly impacts product quality and rolling process stability, making accurate camber prediction crucial. However, it is challenging to measure asymmetric factors impacting camber in real production, hindering the ability of current models to predict and analyze the overall camber of rolled plates. This study proposes a method that combines asymmetric fluctuations of measurable variables with deep learning to predict camber. We developed a data analysis platform for plate processing and constructed a camber dataset using machine vision technology. During the modeling phase, a hot-rolled plate camber prediction model, BiLSTM-AM-Seq2Seq, was proposed, integrating a bidirectional long short-term memory network, an attention mechanism, and a sequence-to-sequence model. Additionally, an improved scheduled sampling method was also introduced to steer model training. Model performance was evaluated with real-world production data, demonstrating superior accuracy and stability. Specifically, the model achieved a mean absolute error of 12.29 mm and a root mean square error of 17.32 mm. Consequently, this model meets the demands of practical production and addresses the need for overall camber prediction of hot rolled plates. • Data-driven prediction model for overall camber shape of rolled plates is proposed. • Modeling using fluctuations of observable variables to address the absence of key asymmetric variables in real production. • Data analysis platform was developed and real data were used to demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

In the field of hot rolling big data, the presence of different steel types, specifications, and data heterogeneity poses significant challenges to the accuracy and stability of using single machine learning regression technology for prediction. Therefore, this study proposes a hot‐rolled strip crown prediction method that combines data clustering and fusion modeling. First, this article introduces a relevant mechanism for designing cluster strategies. The optimal clustering strategy is determined through comparative experiments using rolling process parameters, strip size, and main material components as the clustering features. Subsequently, the K‐Means++ algorithm is used to effectively cluster the training and testing datasets based on this strategy, generating multiple clusters for both datasets. Finally, this study establishes seven different training models to match the most suitable regression prediction model for each cluster, and matching between each cluster and the model is determined through rigorous testing. The evaluation of the fusion model shows an R2 value of 0.829 and a root mean square error value of 3.974. The experimental results show that the proposed method outperforms traditional methods in solving the challenges of multiclass classification and data heterogeneity, providing strong data support for the intelligent control of the hot‐rolled strip crown in the future. [ABSTRACT FROM AUTHOR]

The accurate prediction of strip crown is the precondition of the shape preset model in hot strip rolling. In this study, a new hybrid strip crown forecasting model is proposed in combination with extreme learning machine (ELM) and the industrial data. The production data of 1780 mm hot strip rolling are collected by the on-site data acquisition system to form dataset. Principal component analysis (PCA) is used to reduce the dimension of the input data for modeling samples. To improve the prediction accuracy of ELM, an improved PSO based on S-curve decreasing inertia weight (SDWPSO) is proposed to optimize the initial weights and biases of ELM. Finally, the optimal ELM model and simple production dataset are used to establish a strip crown prediction model of hot strip rolling named PCA-SDWPSO-ELM. The comprehensive performance of the proposed hybrid PCA-SDWPSO-ELM prediction model is evaluated by MAE, MAPE and RMSE. The superiority of the proposed model is also proved by comparing the prediction results with those of the other three comparison models. The research shows that the hybrid PCA-SDWPSO-ELM method can solve the problem of nonlinear and strong coupling in traditional engineering. It is suitable for parameter prediction and optimization in the iron and steel manufacturing industry, especially in the process of shape control in hot strip rolling. [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]

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. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1007/s00170-021-07508-2 [ABSTRACT FROM AUTHOR]

With the increasing demands on strip product quality, conventional control methods cannot break the cold rolling production bottleneck The development of advanced control algorithms provides theoretical support for the improvement of strip accuracy and product stability of the tandem cold rolling process. Considering the complex characteristics of the system, a distributed model predictive control (DMPC) strategy based on the Nash equilibrium is proposed in this study. A state space model including the rolling process with constraints is established. To improve the calculation speed, a Laguerre function is integrated in the control strategy, so as to reduce the parameter dimension. The DMPC online optimal control strategy based on the Nash equilibrium is proposed to solve the problem between the constrained multivariable complex system and online rolling optimization. Its performance and effectiveness are demonstrated through a series of experiments based on rolling data from industry. • A state space model of thickness and tension during rolling process is established. • A DMPC strategy based on Nash Equilibrium is designed for the rolling system. • The DMPC strategy of the whole rolling system shows excellent control performance. [ABSTRACT FROM AUTHOR]