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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]

During the slab sizing press (SP) process, the pressing force corresponds to the slab profile, which guides the production schedule design and the final profile control. To accomplish the prediction of pressing force for SP, an improved ensemble method based on chaotic Harris hawks optimizer (CHHO) and stacking is proposed. A mechanistic knowledge is introduced for feature selection that enhances the rationality of input features. Subsequently, 11 machine learning models are compared and 5 of them are selected as candidate learners for the stacking method. Based on the candidates learners, 8 stacking strategies are constructed, which the stacked model with extratree regressor, gradient boosted decision trees, and kernel ridge regression (KRR) as base‐learners and KRR as meta‐learner performs the best. The R2, MAE, mean square error, mean squared log error, and mean absolute percentage error for the test dataset are 0.9912, 0.0856, 0.0167, 0.0005, and 2.00%, respectively, and 95% of the prediction errors are less than 0.15 MN. Then, sensitivity analysis and predictive analysis based on Shapley Additive Explanations are performed to demonstrate the good alignment of the proposed model with physical reality. Furthermore, to cope with the complexity and uncertainty of the production process, the proposed CHHO‐stacking model and the kernel density estimation method are integrated to model the prediction interval. [ABSTRACT FROM AUTHOR]

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]

The efficiency of the flatness actuator is the cornerstone of the strip steel flatness control system and is pivotal in maintaining the quality of the strip shape and microstructure properties in commercial strip steel. However, the fluctuation in the thermal crown of the work rolls during the cold rolling process significantly impacts strip flatness quality. Obtaining real-time updates on the thermal crown alterations of the work rolls presents notable difficulties. Hence, the actual efficiency of the flatness actuator measured in the cold rolling environment fails to consider the effects of the work rolls' thermal crown, undermining the control precision of strip shape. Exploring the mechanism through which the thermal crown of the work rolls influences strip shape is therefore crucial. This would allow for determining the efficiency of the flatness actuator, accounting for the work rolls' thermal crown. In this research, an innovative approach is utilized, where FEM is used to create a highly accurate model. This model encapsulates the temperature-stress-strain field. This model effectively elucidates the time-varying influence of the hot roll shape on the strip shape, enabling the derivation of a dynamic strip flatness actuator efficiency curve for work roll bending and other actuators in the nonstationary rolling process. In addition, it addresses common challenges encountered in strip shape control during tandem cold rolling, such as the roll change stage and acceleration/deceleration stage.> [ABSTRACT FROM AUTHOR]

To obtain the transverse temperature distribution of running strips under C‐type edge induction heaters during the hot‐rolling process, a coupled electromagnetic–thermal finite‐element model is established using COMSOL, in which the electromagnetic and temperature distributions regularity with various process parameters are systematically obtained. It is shown in the results that the temperature increase is directly proportional to the frequency and current and inversely proportional to the thickness. Meanwhile, the edge temperature drop can be rapidly compensated for using an edge heater, but it also leads to the appearance of a "cat‐ear" high‐temperature zone at the edge; thus, the transverse temperature uniformity cannot be guaranteed. To improve the temperature uniformity of the running strip, a magnetic shield plate is designed to weaken the effect of the cat ear, and the effects of the size and installation position of the shielding plate on the temperature distribution are analyzed. The standard deviation and uniformity of the temperature are used to determine the uniformity of the transverse temperature, and the optimal parameter combination mode of the magnetic shielding amounts is obtained, which can provide a reference for optimizing the heating process at the edge of the strip under moving conditions. [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]

An accurate description of the metal flow behavior during wedge zone deformation is key to the stability of the endless strip production rolling process. To predict the flatness distribution based on the law of volume invariance, a flatness calculation model of the wedge zone during the flying gauge change (FGC) process was established by considering the metal transverse flow. Furthermore, the effect of different rolling parameters on the flow of metal in the wedge zone was investigated using a combination of this model with a 3D finite element model. In addition, the influences of the reduction rate, roll crown, and friction coefficient on the flatness and metal lateral flow were analyzed and discussed. Finally, the numerical results provided by the finite element model were compared with the computed results of the flatness distribution to illustrate the effectiveness and stability of the proposed model. [ABSTRACT FROM AUTHOR]

In the hot strip process, the prediction of strip shape is a key factor to improve the quality of products. However, strip‐shape prediction models based on traditional machine learning algorithms with relatively simple network structures often rely on data preprocessing and feature selection. Herein, five strip‐shape prediction models are proposed by combining sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), extreme learning machine (ELM), and deep multilayer extreme learning machine (DELM). The prediction experiments show that the SSA‐DELM model has highest accuracy, and the determination coefficient (R2) of crown prediction and flatness prediction of SSA‐DELM model is 0.971 and 0.979. 100% of the samples have a prediction error of less than ±7 μm and ±7 IU. Furthermore, to solve the problem of concept drift affecting prediction accuracy in industrial processes, online optimization method is proposed. Especially, online sequential deep ELM model (OS‐DELM) optimized by SSA and guided by double window drift detection is proposed to fill the gap. Compared with other online optimization methods, the update time with this method can be reduced by up to 83.05% averagely, which is more suitable for online strip‐shape prediction in hot rolling process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The strips with good flatness after finish rolling will have flatness defects such as edge wave after run-out table cooling. These defects are caused by the residual stress due to inhomogeneous thermal and microstructural behaviors during the cooling stage. The traditional residual stress control methods rely too much on experience, resulting in poor control effect and poor universality. Therefore, this study proposed two residual stress control methods based on model calculation: water flow central crown and center wave compensation control. The former is mainly based on controlling the uniformity of strip transverse temperature and microstructure. The latter is mainly controlled by mutual compensation of internal stresses. In this paper, the mathematical models of water flow central crown cooling and critical center wave compensation stress were established, respectively. Meanwhile, a post-buckling model of hot-rolled strip was established to calculate the aim flatness value at exit of finishing mill under critical compensation stress. Furthermore, a fully coupled finite element model based on temperature, phase transitions, and stress was developed and it was verified using experimental data. Finally, the above two residual stress control methods were realized in the finite element model. The results showed that the two methods can reduce the residual stress and improve the flatness, and the application of center wave compensation control is more effective. [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]

A dog‐bone profile is developed by inhomogeneous deformation of a slab, whose parameters can be used to describe the metal flow rule during the width reduction process by a sizing press. The accuracy of a dog‐bone profile is essential to ensure the high width precision of the roughing process. However, the existing equipment cannot satisfy the requirements of online measurement. To solve this problem, herein, a prediction model for dog‐bone parameters based on the finite‐element simulation and a neural network is presented. The finite‐element simulation can reproduce the field production process, revealing variations in the dog‐bone profile parameters. In addition, based on the simulation data, a neural network model is constructed to predict the dog‐profile parameters. By adjusting the hyperparameters, the prediction accuracy of the constructed deep neural network model with two hidden layers is improved. For the dog‐bone profile parameters, the mean squared error is less than 3 mm, the mean absolute percentage error is less than 2%, the maximum absolute percentage error is less than 9%, and the root mean squared error is less than 4 mm. Thus, an accurate prediction of the dog‐bone profile parameters is achieved, which can guide subsequent high‐precision width control. [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]

Variable gauge rolling is a new process to obtain a plate for which the thickness changes continuously by continuously and dynamically adjusting the roll gap upward and downward in the rolling process. This technology is an effective method for producing lightweight, low-cost, and economical plates. However, variable gauge rolling is an unsteady process, and the changes in the force and deformation parameters are complex. In this research, based on the minimum energy theory of the variational principle and considering the characteristics of the roll movement and workpiece deformation comprehensively, the internal plastic deformation, friction, shear and tension powers, and the minimum result of the total power functional in upward and downward rolling are obtained with the first integral and then with a variation of adopting the specific plastic power and strain rate vector inner product. The analytical results of the deformation and force parameters are also established using the variational method. Then the precision of this model is certified using the measured values in a medium plate hot rolling plant and the experimental data for Tailor Rolled Blank rolling. Good agreement is found. Additionally, the variation rule of bite angle, neutral angle, and location neutral points are shown, and the change mechanism of the friction parameter on the stress state effect coefficient is given in variable gauge rolling. This research proposes a new mathematical model for rolling process control that provides a scientific basis and technical support for obtaining an accurate section shape in variable gauge rolling production. [ABSTRACT FROM AUTHOR]

Vertical rolling is an important technique used to control the width of continuous casting slabs in the hot-rolling field. Accurate prediction of vertical rolling force is a core point maintaining rolling-mill equipment. Owing to the limitation of the algorithm in use, the prediction accuracy of most vertical rolling force models based on the energy method can only reach more than 10%. Therefore, it is challenging to optimize the rolling-force model to improve prediction accuracy. An innovative approach for optimizing the calculation of vertical rolling force with a unified yield criterion is presented in this paper. First, the maximal width of a dog-bone region is determined by the slip-line method, and the dog-bone shape is described using a sine-function model. Second, the velocity and corresponding strain-rate fields satisfying kinematically admissible conditions are proposed to calculate the total power of the vertical rolling process. Finally, the analytical solution of the rolling force and the dog-bone-shape model is obtained by repeatedly optimizing the weighted coefficient b of intermediate principal shear stress on the yield criterion. Moreover, the effectiveness of the proposed mechanical model is verified by measured data in the strip hot-rolling field and other models' results. Results show that the prediction accuracy of the vertical rolling force model can be improved by optimizing the value of b. Then, the impacts of reduction rate, initial thickness, and friction factor on dog-bone shape size and vertical rolling force are discussed. [ABSTRACT FROM AUTHOR]

This article studies the problem of the optimal stealth attack strategy design for linear cyber-physical systems (CPSs). Virtual systems that reflect the attacker’s target are constructed, and a linear attack model with varying gains is designed based on the virtual models. Unlike the existing optimal stealth attack strategies that are designed based on sufficient conditions, necessary and sufficient conditions are, respectively, established to achieve the optimal attack performance while maintaining stealth in virtue of the solvability of certain coupled recursive Riccati difference equations (RDEs). Under those conditions, an optimal stealth attack strategy is constructed by an offline algorithm. A simulation example is applied to verify the effectiveness of the presented technical scheme. [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]

This article is concerned with the problem of event-triggered (ET) fault detection (FD) observer design for Takagi–Sugeno (T–S) fuzzy systems. The nonlinear systems considered are presented as T–S models, and the system outputs are selected as the premise variables (PV) of T–S models. New types of observers, namely, switched fuzzy FD observers, are designed, which are switched at different triggering instants based on the online asynchronous premise reconstruction (APR) method. By employing new triggering instants-dependent Lyapunov functions (LFs) established via the idea of event interval partitioning, it is proved that the proposed FD observers can guarantee the stability and desired performance of the systems. Finally, a simulation example is applied to show that the proposed FD scheme can achieve better FD results than the existing design methods. [ABSTRACT FROM AUTHOR]

To study the transverse temperature uniformity with transverse flux induction heating (TFIH) during endless strip production (ESP) rolling, a coupled electromagnetic-thermal finite element model of transverse flux induction heating is established. The initial temperature distribution at the TFIH inlet is taken into consideration, which is the basis of subsequent numerical studies. Magnetic screens are designed to weaken the influence of edge overheating, and the influence of magnetic shielding amounts on temperature uniformity is discussed. Moreover, an evaluation criterion is provided to determine the temperature uniformity. Furthermore, the effect of different shielding amounts on the edge temperature is studied. An ideal temperature distribution at the TFIH outlet is obtained based on a model combining various shielding amounts, and the temperature standard deviation of this method is 2.01% lower than the traditional arrangement of magnetic shielding amounts. Actual data are collected to verify the results of the proposed model. The temperature deviation between the middle and edge is approximately 12°C, and the temperature uniformity is enhanced with the optimized combination of magnetic shielding amounts. [ABSTRACT FROM AUTHOR]

Purpose: In the cold rolling process, friction coefficient, oil film thickness and other factors vary dramatically with the change in the rolling speed, which seriously affects the strip thickness deviation. This paper aims to improve the strip control precision with the forecast roll gap model based on CF-PSO-SVM approach in the rolling process. Design/methodology/approach: In this paper, a novel forecasting model of the roll gap based on support vector machine (SVM) optimized by particle swarm optimization with compression factor (CF-PSO) is proposed. Based on lots of online data, the roll gap models regressed by PSO-SVM, genetic algorithm (GA)-SVM and CF-PSO-SVM are obtained and verified through evaluating the performances with the decision coefficient (R2), mean absolute error and root mean square error. In addition, with the good forecasting performances of CF-PSO-SVM, a roll gap compensation model is studied. Findings: The results indicate that the proposed CF-PSO-SVM has excellent learning regression ability compared with other optimization algorithms. Meanwhile, a roll gap compensation model based on the rolling speed and plastic coefficient is obtained, which has been proved validated in product. Originality/value: In this paper, the SVM algorithm is combined with traditional rolling technology to solve the problems in actual production, which has great supporting significance for the improvement of production efficiency. [ABSTRACT FROM AUTHOR]

This paper established a model based on an ensemble method to predict strip crown using 230,000 coils of data obtained from a hot rolling line. Before modeling, a specific method was proposed to reduce noise and remove outliers, and a new dataset of 5657 samples was generated. Parameter tuning considering mean squared error (MSE) was carried out to establish three machine learning models including support vector machine (SVC), regression tree (RT), and random forest (RF). Determination coefficient (R2), mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to evaluate the prediction of models. Results showed that the RF had the best performance with the highest R2 of 0.707, as well as the lowest RMSE of 5.66 μm. Moreover, an additional method that repeated the three models 100 times was developed, and box plots were used to visualize the distribution of R2, MAE and RMSE. RF can correct for decision trees to reduce overfitting to their training set, improving the generalization, and in this paper, the trained RF which had stable performance is considered as the most recommended model. After that, for RF, rankings of rolling process variable were validated to make a comparison with the existing physical understanding. [ABSTRACT FROM AUTHOR]

Summary: This article studies the problem of data‐driven (DD) fault detection (FD) for linear systems. First, based on the DD realization of kernel representation, new residual generators are designed via a q‐step residual iteration method. Then, it is proved that the proposed residual generators guarantee the stability and L1 performance of the FD error systems, and the presented residual design method is more sensitive to faults than the existing ones constructed directly from available process data. Finally, two simulation examples are provided to verify the effectiveness and advantages of the designed method. [ABSTRACT FROM AUTHOR]

Strip shape prediction is one of the most important technical to improve the quality of products in hot strip rolling process. In this paper, three hybrid models, including GA-MLP, MEA-MLP and PCA-MEA-MLP, are proposed for profile and flatness predictions by combining genetic algorithm (GA), mind evolutionary algorithm (MEA), principal component analysis (PCA) and multi-layer perceptron (MLP) neural networks. Mean absolute error (MAE), mean absolute percentage error, root mean squared error are adapted to evaluate the performance of the models. The results show that the data-driven model based on intelligent algorithm optimization neural networks can achieve good prediction of profile and flatness. Comparing with the hybrid GA-MLP model, the training speed of the hybrid MEA-MLP model is faster and the training time is greatly reduced. The model establishing with the input data after dimensionality reduction by PCA can reduce training time and become simple. The innovation of this paper is to propose a data-driven fast response model based on intelligent algorithm optimization neural network to replace the traditional mechanism model based on mathematical formula analysis to study complex, non-linear strip shape control in hot rolling process. [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]

Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm-Online Sequential-Deep Multilayer Extreme Learning Machine Algorithm The strip shape prediction model is proposed by combining a sparrow search algorithm (SSA) and a deep multilayer extreme learning machine (DELM). In article number 2200832, Jingguo Ding and co-workers show that rolling data in information space comes from rolling production lines in physical space. [Extracted from the article]

A parabolic velocity field is proposed to get the minimum required rolling force in hot rolling process, the equal area yield criterion is used to calculate the plastic deformation power, the collinear vector inner product method is used to get friction power, the analytical equation of strip rolling power functional is obtained, and the required rolling force was calculated by minimizing the power functional. Meanwhile, the location of neutral point, shape factor, and stress state coefficient are discussed. The accuracy of the proposed theoretical model is examined through comparing with the experimental measured ones and online measured ones, it shows good agreement, the relative error of rolling force is less than 9.1%, and the proposed model is considered to be effective in hot tandem rolling process. [ABSTRACT FROM AUTHOR]

Fluid flow characteristics in a novel self-stirring reactor have been studied by numerical simulation method and particle image velocity (PIV) in this article. The novel self-stirring reactor is furnished with a driving turbine that can make impellers agitate under high-pressure energy of fluid. It is found that areas of dead zone decrease with an increase of the liquid level in range of 60-200 mm for the driving turbine and impeller types used in this study. The dead zone almost disappears with the liquid level 200 mm. Some minor-cycle flows that appear in the axial direction promote the mixing and exchange of fluid, which is beneficial to the process of dissolution reaction. Free-fall of liquid becomes more distinct and more energy losses occur as the situation gets further from the inlet. The pressure energy of fluid is converted into kinetic energy, and the velocity near the driving turbine is greater. While rotation drag is smaller at liquid level of 180 mm, the effect of circulation is obvious. The flow field recorded by PIV is consistent with the result achieved by simulation. Agitation leads to the level fluctuation. The velocity direction is away from the stirring shaft and outward toward the wall of the reactor. [ABSTRACT FROM AUTHOR]

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

Optimization of the schedule is an effective way to reduce energy consumption in hot strip manufacturing. The heating energy and the rolling energy contribute the vast majority of the energy consumption. In order to reduce the energy consumption for the whole process, an optimization method based on the multi-objective function was applied, and the heating energy and rolling energy were both taken into consideration during the function modeling. Meanwhile, process constraints and equipment limitations were taken as objective function boundary conditions, and the tapping temperature in the furnace exit and thickness of each pass were decision variables. Further, to obtain the optimal solution of the objective function, the differential evolution algorithm was used, finally, the optimal tapping temperature and the thickness were obtained successfully. Meanwhile, the validity of the calculated results was verified by comparing with the actual process in a hot rolling production line in Hebei province, the results indicated that with the proposed method, a more reasonable schedule were obtained; compared with the existing actual schedule of typical specification, the energy consumption can be reduced by about 1.5%. [ABSTRACT FROM AUTHOR]