Study on Temperature Rise Modeling of Main Motor of Hot Rolling Mill Based on Support Vector Machines

Gao, Yi; Cai, Ping; Chen, F.; Ru, Qin

doi:10.4028/www.scientific.net/amm.870.427

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…In this experiment, the model structure with three hidden layers is used for the experiment, and the learning rate is 0.1. In addition, the nonlinear function rectified linear unit is used for the activation function of hidden neurons, and its expression is given in equation (10). In this experiment, 32 groups of abnormal data are removed through the Pauta standard, and a total of 3550 groups of preprocessing experimental data are selected to establish the DNN prediction model, including rolling data and mechanism data.…”

Section: Dnn Prediction Modelmentioning

confidence: 99%

“…In addition, SVM easily obtains better generalisation capabilities in the case of small sample data. 9 Gao et al 10 analysed the temperature rise mechanism of the main motor, and then the SVM was used for parameter identification of the model. By comparison with ANNs and regression trees, a hot-rolled strip crown prediction method based on SVM was developed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Edge drop prediction of hot-rolled silicon steel based on a mechanism and data model

Wang,

Song,

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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In this article, a high-precision edge drop prediction model for hot-rolled silicon steel based on a rolling mechanism and deep neural network (DNN) is proposed. Considering the initial roll shape, roll wear and roll thermal expansion, a mechanism model of the roll system is established according to the hot rolling 4-high finishing mill. In order to reduce the data dimension and effectively improve the prediction accuracy, the random forest algorithm is employed to analyse the feature parameters that affect the edge drop of hot-rolled silicon steel. Finally, the DNN is introduced to establish an accurate silicon steel edge drop prediction model based on mechanism data and rolling data. The experimental results demonstrated that the proposed DNN model has strong generalisation and reliability with a squared correlation coefficient ( R2) of 0.9656, and it is suitable for application in hot-rolled silicon steel lines for predicting edge drop. Furthermore, the proposed model has a certain reference value for the hot rolling process.

show abstract

Section: Dnn Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Edge drop prediction of hot-rolled silicon steel based on a mechanism and data model

Wang,

Song,

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

View full text Add to dashboard Cite

show abstract

“…SHEN et al [19] developed a new model by combining SVM with small experimental data and an advanced material was designed successfully. A temperature rise parameter identification model of the main motor of the hot rolling mill was established by SVM method, and the feasibility of the model was verified by actual data [20]. Therefore, based on industrial big data, artificial intelligence high-precision strip crown prediction model undoubtedly has a broad theoretical significance and practical application value.…”

Section: Introductionmentioning

confidence: 99%

Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

Song

Sun

et al. 2021

J. Cent. South Univ.

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To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown, an optimized model based on support vector machine (SVM) is put forward firstly to enhance the quality of product in hot strip rolling. Meanwhile, for enriching data information and ensuring data quality, experimental data were collected from a hot-rolled plant to set up prediction models, as well as the prediction performance of models was evaluated by calculating multiple indicators. Furthermore, the traditional SVM model and the combined prediction models with particle swarm optimization (PSO) algorithm and the principal component analysis combined with cuckoo search (PCA-CS) optimization strategies are presented to make a comparison. Besides, the prediction performance comparisons of the three models are discussed. Finally, the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed. Furthermore, the root mean squared error (RMSE) of PCA-CS-SVM model is 2.04 µm, and 98.15% of prediction data have an absolute error of less than 4.5 μm. Especially, the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.

show abstract

Application of machine learning to predict and diagnose for hot-rolled strip crown

Song

Wang

et al. 2022

Int J Adv Manuf Technol

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Study on Temperature Rise Modeling of Main Motor of Hot Rolling Mill Based on Support Vector Machines

Cited by 4 publications

References 3 publications

Edge drop prediction of hot-rolled silicon steel based on a mechanism and data model

Edge drop prediction of hot-rolled silicon steel based on a mechanism and data model

Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

Application of machine learning to predict and diagnose for hot-rolled strip crown

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