Optimization design of RBF‐ARX model and application research on flatness control system

Zhang, Xiuling; Cheng, Long; Hao, Shuang; Gao, Wu-Yang; Lai, Ying–Cheng

doi:10.1002/oca.2240

Cited by 11 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When an iteration point is on the boundary of a constraint, if the feasible direction is not obtained properly, then only a small movement can be made along the direction due to the proximity of another constraint boundary. Otherwise, the iteration point will run out of bounds [26][27][28][29]. The Zoutendijk algorithm steps are as follows:…”

Section: Zoutendijk Algorithmmentioning

confidence: 99%

Numerical simulation analysis of the rolling process based on the particle swarm hybrid algorithm

Yan

et al. 2023

Ironmaking & Steelmaking

View full text Add to dashboard Cite

It is very complicated to study the three-dimensional deformation of metal during rolling. The conventional finite element numerical analysis method generally adopts a fixed algorithm to calculate the whole rolling process. This method consumes huge computing power and sacrifices some computing accuracy. In this paper, according to the characteristics of different rolling stages, on the basis of considering the degree of metal deformation, a particle swarm hybrid algorithm with adaptive weight-learning factor is proposed.The Zoutendijk algorithm, Rosen algorithm, Wolfe algorithm and particle swarm hybrid algorithm are used to numerically simulate the rolling transverse thickness distribution. The accuracy of the rolling model and the particle swarm hybrid algorithm are verified. The influence of work roll edge contact and asymmetric roll bending on the deformation of rolling metal is analysed based on particle swarm mixing algorithm.

show abstract

Section: Zoutendijk Algorithmmentioning

confidence: 99%

Numerical simulation analysis of the rolling process based on the particle swarm hybrid algorithm

Yan

et al. 2023

Ironmaking & Steelmaking

View full text Add to dashboard Cite

show abstract

“…Not only can the analysis of actual flatness condition be conducted in real time but also the reasonable adjustment strategy is intelligently selected in the intelligent flatness control system [23][24][25]. As a consequence, the overall regulation capacity of flatness adjustment actuator after the combination is made to match with the flatness defect.…”

Section: Flatness Actuator Group Collaboration Strategymentioning

confidence: 99%

Collaboration Strategy Based on Conflict Resolution for Flatness Actuator Group

Yan

Wang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

During the flatness control process, there are frequently some uncoordinated regulating behaviors in the flatness actuator group. This has a bad influence on the flatness control accuracy and the flatness control efficiency. Therefore, a collaboration strategy based on conflict resolution for the flatness actuator group has been proposed in this paper. First of all, the feature of flatness measurement value is extracted through establishing the actual flatness condition discriminating factor. After that, the coordination cooperation that is appropriate to the actual flatness condition is developed for the flatness actuator group. Finally, the optimal adjustment of the actuator population is solved by the coordinated algorithm of Topkis-Veinott and genetic algorithm collaborative optimization. The collaboration strategy proposed in this paper has been successfully applied to a flatness control system of a 1450 mm five-stand cold rolling mill.

show abstract

“…Simulation results under various methods show that the various models proposed can identify common defects in strip shape with high accuracy. Deng et al [25,26] constructed a hybrid model based on the combination of hot strip rolling production data and deep learning networks to predict strip outlet crown, achieving an absolute error of less than 5 µm for 97.04% of the predicted data in the modeling data. Song et al [27] used machine learning algorithms to establish an accurate prediction model for the strip crown of hot rolling.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data

Wang

Yu²,

Liu

et al. 2023

Metals

View full text Add to dashboard Cite

The strip crown in hot rolling has the characteristics of multivariablity, strong coupling and, nonlinearity. It is difficult to describe accurately using a traditional mechanism model. In this paper, based on the industrial data of a hot continuous rolling field, the modeling dataset of a strip steel prediction model is constructed through the collection and collation of the on-site data. According to the classical strip crown control theory, the important process parameters that affect the strip crown are determined as input variables for the data-driven model. Some new intelligent strip crown prediction models integrating the shape control mechanism model, artificial intelligence algorithm, and production data are constructed using four machine learning algorithms, including XGBoost, Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). The overall performance of the models is evaluated using error indicators, such as Mean Absolute Percentage Error (MAPE), Root Mean square Error (RMSE), and Mean Absolute Error (MAE). The research results showed that, for the test set, the determination coefficient (R2) of the predicted value of the strip crown model based on the XGBoost algorithm reached 0.971, and the three error indexes are at the lowest level, meaning that the overall model has the optimal generalization performance, which can realize the accurate prediction of the outlet strip crown in the hot rolling process. The research results can promote the application of industrial data and machine learning modeling to the actual strip shape control process of hot rolling, and also have important practical value for the intelligent preparation of the whole process of steel.

show abstract

Optimization design of RBF‐ARX model and application research on flatness control system

Cited by 11 publications

References 21 publications

Numerical simulation analysis of the rolling process based on the particle swarm hybrid algorithm

Numerical simulation analysis of the rolling process based on the particle swarm hybrid algorithm

Collaboration Strategy Based on Conflict Resolution for Flatness Actuator Group

Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data

Contact Info

Product

Resources

About