Strip Rolling Process Research Articles

Research on roll profile thermal-force driving characteristics and joint control strategy in the multi-zone roll profile electromagnetic control technology

Roll profile electromagnetic control technology (RPECT) is a new roll gap shape control technology that can flexibly control the roll profile to meet the flatness control requirements of cold rolling mill. As basic control elements, electromagnetic sticks (ES) can drive the local zone bulging of electromagnetic control roll (ECR) to generate thermal-force contribution roll profiles. Due to the high order profile defects in the thin-gauge strip rolling process, the requirement of roll gap shape control in cold rolling mills is usually characterized by multi-zone and micro-scale. Therefore, a multi-zone roll profile electromagnetic control technology is proposed in this paper, which includes a multi-zone joint control strategy and a parameter preset method for basic control elements targeting roll profile. To conduct the parameterized impact analysis of stick distance and multi-stick control modes, a multi-zone joint control simulation model is established. Through the simulation analysis, the results show that it is feasible to use multiple basic control elements to adjust the multi-zone roll profile in the mechanism of action, and the total roll profile curve can be equivalent to the superposition of the individual bulging curve of different basic control elements with different space-time parameters. Meanwhile, the reasonable distance setting can effectively avoid the instability of thermal contribution roll profile control caused by too small distance between sticks and too strong thermal bulging driving. For the double-stick control mode or the three-stick control mode, using different temperature control method can assist ECR to realize the roll profile control goal that the low-order roll profile of the basic control element is superimposed to construct the high-order roll profile curve. According to strip flatness electromagnetic control experiment, the multi-zone joint control of RPECT can adjust the flatness defects above 100 IU to flat state, and can flexibly regulate local shape problems and control local buckling.

Integrated AGC Approach for Balancing the Thickness Dynamic Response and Shape Condition of a Hot Strip Rolling Control System

Thickness and shape are two significant indices in the tandem hot strip rolling process. Excessive thickness correction can cause rolling force imbalance, leading to deteriorated shape conditions. Aiming at this problem, we propose a new monitor automatic gauge control (M-AGC) with consideration of the rolling force distribution to not only keep the shape condition but also improve the robustness of the rolling process. First, the M-AGC with the Smith predictor control structure was studied. Second, based on the rolling force and thickness correction redistribution algorithm, shape balance automatic gauge control (SB-AGC), the phenomenon of rolling force imbalance was improved. Third, the transient response was improved by adding a command prefilter to the upstream stands, which is asynchronous shape balance automatic gauge control (ASB-AGC). The proposed algorithms can be applied to all steel grades and are easy to implement. Finally, cross-validation was conducted through numerical simulations and application results, confirming the alignment between the theoretical derivation and practical implementation. The simulation results show improvements in both the rolling force balance and the thickness response. The practical application results also confirm that better flatness conditions and thickness response can be achieved, significantly reducing the amount of head-end cutoff losses.

Effect of Strip Rolling and Wire Drawing Processes on NiTi Shape Memory Alloy Properties: A Comparative Study

Effect of Strip Rolling and Wire Drawing Processes on NiTi Shape Memory Alloy Properties: A Comparative Study

Joint prediction method for strip thickness and flatness in hot strip rolling process: A combined multi-indicator Transformer with embedded sliding window

Thickness and flatness are important quality indicators for strip. It is important that the rapid and accurate prediction of the exit thickness and flatness for the optimal control of the hot strip rolling process. Due to the fast and long rolling process, there are time delays, non-linearity and strong coupling among the variables, which cause difficulties in the establishment of prediction models. In this paper, the variables related to thickness and flatness are selected by analyzing the rolling process mechanism and data. Based on the data related to the rolling quality, a rolling exit thickness and flatness joint prediction model combined multi-indicator Transformer with embedded sliding window (SW-MTrans) is proposed. First, a sliding window is embedded into the input layer of the model in order to address the effect of the time delay among variables. Then a Transformer network is improved to achieve accurate prediction of thickness and flatness simultaneously. It is verified that the proposed method can predict the thickness and flatness at the same time with higher prediction accuracy and generalization ability compared with other methods through actual production data. The mean absolute error (MAE) for thickness prediction was reduced by 19.37% and MAE for flatness prediction was reduced by 14.03% compared to the existing prediction model.

Evolutions of Cube ({001}) and {115} Orientations in Cold-Rolled Ultra-Thin Non-Oriented Silicon Steel.

In this study, the evolutions of Cube and {115}<161> orientations of a cold-rolled ultra-thin non-oriented silicon steel were investigated using a combination of experimental investigation and the crystal plasticity finite element method (CPFEM). The results show that Cube orientations remain relatively stable when their initial deviation angles from the ideal Cube orientation are less than 12°, even after a 60% cold rolling reduction. However, larger deviations occur due to higher strain near grain boundaries. Furthermore, the {115}<161> orientations, with an initial deviation of ~18° from the ideal Cube orientation, become separated into different orientation regions during cold rolling. Some regions gradually approach the ideal Cube orientation as cold rolling progresses and reach ~12.5° deviation from the ideal Cube at a 40% reduction. This study demonstrates good agreement between simulation and experimental results, highlights the micro-deformation mechanisms during rolling, and offers insights for optimizing the ultra-thin strip rolling process.

Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

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.

Interval prediction of bending force in the hot strip rolling process based on neural network and whale optimization algorithm

In the hot strip rolling process, accurate prediction of bending force is beneficial to improve the accuracy of strip crown and flatness, and further improve the strip shape quality. Due to outliers and noise are commonly present in the data generated in the rolling process, not only the prediction accuracy should be considered, but also the uncertainty of prediction results should be described quantitatively. Therefore, for the first time, the authors establish an interval prediction model for bending force in hot strip rolling process. In this paper, we use Artificial Neural Network (ANN) and whale optimization algorithm (WOA) to produce a prediction interval model (WOA-ANN) for bending force in hot strip rolling. Based on the point prediction by ANN, interval prediction is completed by using lower upper bound estimation (LUBE) and WOA, and three indexes are used to evaluate the performance of the model. This paper uses real world data from steel factory to determine the optimal network structure and parameters of the interval prediction model. Furthermore, the proposed WOA-ANN model is compared with other interval prediction models established by other three optimization algorithms. The experimental results show that the proposed WOA-ANN model has high reliability and narrow interval width, and can well complete the interval prediction of bending force in hot strip rolling. This study provides a more detailed and rigorous basis for setting bending force in hot strip rolling process.

A Novel Named Entity Recognition Algorithm for Hot Strip Rolling Based on BERT-Imseq2seq-CRF Model

Named entity recognition is not only the first step of text information extraction, but also the key process of constructing domain knowledge graphs. In view of the large amount of text data, complex process flow and urgent application needs in the hot strip rolling process, a novel named entity recognition algorithm based on BERT-Imseq2seq-CRF model is proposed in this paper. Firstly, the algorithm uses the BERT preprocessing language model to mine the dependencies in the domain text and obtain the corresponding representation vector. Then, the representation vector is sent to the encoder layer, and the output vector is input to the decoder at the same time, on the premise that the original model only considers the semantic vector. The Teacher-Forcing mechanism is integrated into the decoder layer to randomly modify the labeling results, and error accumulation is avoided to guarantee the sequence recognition effect. Finally, the validity of the labeling results is checked according to the conditional random field constraints, and the overall labeling quality of the algorithm is improved. The experimental results show that this model can efficiently and accurately predict the physical label of hot strip rolling, and the model performance index is better than other models, with the F1-Score reaching 91.47%. This model further provides technical support for information extraction and domain knowledge graph construction of hot strip rolling.

Strip Thickness and Profile–Flatness Prediction in Tandem Hot Rolling Process Using Mechanism Model‐Guided Machine Learning

This study is focused on the multivariable, nonlinear, strong‐coupling, and time‐varying characteristics of the quality control process of hot strip rolling. Using the rolling process data from a continuously variable crown (CVC) mill of a 1580 mm hot tandem rolling line, a new prediction model of the strip thickness, profile, and flatness in hot rolling based on rolling mechanism model‐guided machine learning (ML) is developed. The weighted processing (WP) method is used to fuse the rolling mechanism and process data, to improve the relationship between the strong correlation features and the model. Combined with nondominated sorting genetic algorithm III (NSGA‐III), multiple parameters of multioutput support vector regression (M‐SVR) are optimized. The results show that the root mean square error (RMSE), mean square correlation coefficient (R 2), and mean absolute error (MAE) of the strip thickness, crown, and flatness are 2.0159, 1.3191, and 0.9355; 0.9854, 0.9895, and 0.9873; and 16.601, 1.126, and 0.604, respectively. Moreover, the established method of data fusion rolling mechanism shows strong capability to improve the model prediction accuracy, increasing it by 60%. Thus, it can offer theoretical guidance for realizing accurate control of the quality of strips and improving the quality of hot‐rolled products.

Imbalanced multiclass classification with active learning in strip rolling process

In the strip rolling process, conventional supervised methods cannot effectively address data with an imbalanced number of normal and faulty instances. In this paper, based on a deep belief network, a resampling method is combined with active learning (AL) to address imbalanced multiclass problems. The support vector machine-based synthetic minority oversampling technique was adapted to enrich the training data, whereas the true data distribution and model generalization were changed. A new selection strategy of AL is proposed that forms a function using uncertainty and diversity. AL uses an optimizing set that has a similar distribution with the whole dataset to calculate the informativeness of instances to optimize the model. Based on this step, the model study instances approach decision boundaries to promote performance. The proposed method is validated by five UCI benchmark datasets and strip rolling data, and experiments show that it outperforms conventional methods in tackling imbalanced multiclass problems.

A Novel Subspace-Aided Fault Detection Approach for the Drive Systems of Rolling Mills

This brief proposes a subspace-aided fault detection approach for the drive systems of strip rolling mills. Considering the impact of the unknown periodic load generated by the strip rolling process, the primary contributions are concluded as follows. First, this brief presents an approach to describe the subspace of the unknown/unmeasurable periodic load. Second, a fundamental frequency identification approach for the drive systems is proposed and then the subspace of the unknown periodic load can be constructed by the fundamental frequency. Third, this brief presents a subspace-aided fault detection approach to identify the data-driven stable kernel representation (SKR) of the closed-loop system by projecting the input–output (I/O) process data, so as to obtain a robust residual against the unknown periodic load. In addition, the effectiveness and performance of the approaches are verified by numerical examples and experimental data of the test rig for the drive systems of strip rolling mills. The results show that a robust residual generation against the unknown periodic load in the drive systems can be obtained and the robust subspace-aided fault detection can be achieved. Compared with the traditional method, the proposed approaches can improve the fault detection rate more effectively and be applied to the drive systems of strip rolling mills reliably.

FEM simulation of thermo-mechanical stress and thermal fatigue life assessment of high-speed steel work rolls during hot strip rolling process

Work rolls used for hot strip rolling undergo the cyclic sequence of heating-cooling over the roll surface due to contact with the hot strip and the following cooling systems during every rolling revolution. Severe temperature gradient and thermal stress caused by cyclic heating-cooling loading are dominant factors for thermal fatigue failure of work rolls. Therefore, investigations on temperature and thermal stress-strain are essential for understanding the thermal fatigue of work roll. In this study, a thermo-mechanical analysis with elastic-plastic material parameters via a simplified finite element model is carried out to compute the stress-strain range experienced by the work roll during rolling and idling. Then, a model with the initial crack in the roll surface considering different depths and a model with the hot strip is conducted to better understand the thermal behaviors of the work roll during hot rolling respectively. Furthermore, the thermal fatigue life of work roll is assessed based on the modified Coffin-Manson relation, considering the effect of multi-axial stress state and temperature-dependent material properties caused by temperature fluctuations in work roll during hot rolling.

Modeling and Monitoring for Laminar Cooling Process of Hot Steel Strip Rolling with Time–Space Nature

The laminar cooling process is an important procedure in hot steel strip rolling. The spatial distribution and the drop curve of the strip temperature are crucial for the production and the quality of the steel strip. Traditionally, lumped parameter methods are often used for the modeling of the laminar cooling process, making it difficult to consider the impact of the variation of state variables and related parameters on the system, which seriously affect the stability of the steel strip quality. In this paper, a modeling and monitoring method with a time–space nature for the laminar cooling process is proposed to monitor the spatial variation of the strip temperature. Firstly, the finite-dimensional model is obtained by time–space separation to describe the temperature variation of the steel strip. Next, a global model is constructed by using the multi-modeling integration method. Then, a residual generator is designed to monitor the strip temperature where the statistics and the threshold are calculated. Finally, the superiority and reliability of the proposed method are verified by the actual-process data of the laminar cooling process for hot steel strip rolling, and different types of faults are detected successfully.

Mathematical Modeling of the Cold Rolling Process and Heat Treatment for DC01 Steel

The paper presents the elaboration of a mathematical model of the cold strip rolling process combined with the recrystallization annealing after the rolling at LBR Liberty Galati.The elaborated mathematical model allows the prediction of the mechanical properties of cold rolled strips subsequently subjected to a heat treatment.The realization of this mathematical model was based on statistical measurements of the mechanical properties Rm, Rp0.2 (Rc) and A5 for the rolled steel strip DC01 from Liberty Steel Galati. To achieve this mathematical model, the active experiment method was used.With the help of this mathematical model, it is possible to optimize the rolling process by significant savings of time and materials in the process of testing the mechanical properties for cold rolled tape, but also by choosing the most appropriate process parameters.&#x0D;

Prediction of Bending Force in the Hot Strip Rolling Process Using Multilayer Extreme Learning Machine

In the hot strip rolling process, accurate prediction of bending force can improve the control accuracy of the strip flatness and further improve the quality of the strip. In this paper, based on the production data of 1300 pieces of strip collected from a hot rolling factory, a series of bending force prediction models based on an extreme learning machine (ELM) are proposed. To acquire the optimal model, the parameter settings of the models were investigated, including hidden layer nodes, activation function, population size, crossover probability, and hidden layer structure. Four models are established, one hidden layer ELM model, an optimized ELM model (GAELM) by genetic algorithm (GA), an optimized ELM model (SGELM) by hybrid simulated annealing (SA) and GA, and two-hidden layer optimized ELM model (SGITELM) optimized by SA and GA. The prediction performance is evaluated from the mean absolute error (MAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The results show that the SGITELM has the highest prediction accuracy in the four models. The RMSE of the proposed SGITELM is 11.2678 kN, and 98.72% of the prediction data have an absolute error of less than 25 kN. This indicates that the proposed SGITELM with strong learning ability and generalization performance can be well applied to hot rolling production.

Optimization-based estimator for the lateral strip position in tandem hot rolling

Abstract This paper deals with the estimation of the lateral motion of steel strips during the hot strip rolling process in a tandem finishing mill. The method presented in this paper uses an image sequence captured by a 2D-CMOS camera. In the considered application, just a small segment of the strip is inside the camera field of view and thus an image sequence has to be used for online estimation. An optimization-based algorithm uses the image data and the knowledge about the constraints of the strip movement in the rolling gap for robust estimation of the lateral position and width of the strip. Measurement data from a tandem finishing mill show the performance of the designed algorithm.

Effects of Thermal Shields on Temperature of a Hot Steel Strip in Rolling Process

Effects of Thermal Shields on Temperature of a Hot Steel Strip in Rolling Process

Nonlinear fault detection for batch processes via improved chordal kernel tensor locality preserving projections

The quality and stability of products are seriously influenced by the process conditions. A large number of modern production processes can be considered as batch processes, with nonlinear relationships between the process variables. How to troubleshoot batch processes has attracted considerable attention in the literature. The research object of batch processes is expressed as the third-order tensor data of batch × variable × time. The traditional methods convert the tensors into second-order forms through matrix expansion. A novel method named improved chordal kernel tensor locality preserving projections (ICK-TLPP) is proposed for fault detection of batch processes. First, the chordal distance is introduced as a measurement of the similarity of matrix, and an improved method is proposed for describing the variation of time series data. Then, the chordal kernel function is introduced to preserve the spatial structure of the tensor data without the information loss caused by vectorization, and describe the nonlinear correlation during the multivariate control system. Next, the locality preserving projections algorithm is applied to detect the intrinsic manifold structure. Parallel analysis is applied to optimize the hyper-parameters in the model. Finally, Granger causality analysis is performed to locate the root cause of the process fault. The proposed method is validated on two datasets, penicillin fermentation process and the hot strip rolling process. The best results of false alarm rate and fault detection rate are 16% and 94% respectively. The proposed method performs better compared with the traditional algorithms.

Modeling and Optimization of Rolling Process: A Multi-Objective Approach

In a high-speed cold strip rolling process, it is necessary to optimize the process parameters for improved quality in the product. In this study, two separate multi-objective optimization problems for a cold rolling process are formulated. The objectives in one of the cases are minimum isothermal film thickness and film temperature rise in the inlet zone and in another case it is minimum thermal film thickness and film temperature rise in the inlet zone. Particle swarm optimization algorithm has been used for solving the optimization problem. The key input parameters for the cold rolling process are identified and prioritized through the convergence study and the coefficient of variation analysis. A response analysis is performed on the critical input variables. This study assists the process engineer to understand the lubrication in cold strip rolling at high speed and select an appropriate lubricant for a given combination of strip and rolls.

A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process

In the hot strip rolling (HSR) process, accurate prediction of bending force can improve the control accuracy of the strip crown and flatness, and further improve the strip shape quality. In this paper, six machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVR), Classification and Regression Tree (CART), Bagging Regression Tree (BRT), Least Absolute Shrinkage and Selection operator (LASSO), and Gaussian Process Regression (GPR), were applied to predict the bending force in the HSR process. A comparative experiment was carried out based on a real-life dataset, and the prediction performance of the six models was analyzed from prediction accuracy, stability, and computational cost. The prediction performance of the six models was assessed using three evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results show that the GPR model is considered as the optimal model for bending force prediction with the best prediction accuracy, better stability, and acceptable computational cost. The prediction accuracy and stability of CART and ANN are slightly lower than that of GPR. Although BRT also shows a good combination of prediction accuracy and computational cost, the stability of BRT is the worst in the six models. SVM not only has poor prediction accuracy, but also has the highest computational cost while LASSO showed the worst prediction accuracy.