Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data
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 ste...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/13/5/900 |
| _version_ | 1797599081523052544 |
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| author | Zhenhua Wang Yu Huang Yuanming Liu Tao Wang |
| author_facet | Zhenhua Wang Yu Huang Yuanming Liu Tao Wang |
| author_sort | Zhenhua Wang |
| collection | DOAJ |
| description | 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 (R<sup>2</sup>) 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. |
| first_indexed | 2024-03-11T03:30:41Z |
| format | Article |
| id | doaj.art-bf5537a4d5da4651991dcf952ffc6858 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-11T03:30:41Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-bf5537a4d5da4651991dcf952ffc68582023-11-18T02:27:27ZengMDPI AGMetals2075-47012023-05-0113590010.3390/met13050900Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial DataZhenhua Wang0Yu Huang1Yuanming Liu2Tao Wang3College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaNational Key Laboratory of Metal Forming Technology and Heavy Equipment, Xi’an 710018, ChinaCollege of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaThe 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 (R<sup>2</sup>) 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.https://www.mdpi.com/2075-4701/13/5/900strip crown predictionindustrial datamachine learninghot strip rollingXGBoost algorithm |
| spellingShingle | Zhenhua Wang Yu Huang Yuanming Liu Tao Wang Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data Metals strip crown prediction industrial data machine learning hot strip rolling XGBoost algorithm |
| title | Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data |
| title_full | Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data |
| title_fullStr | Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data |
| title_full_unstemmed | Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data |
| title_short | Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data |
| title_sort | prediction model of strip crown in hot rolling process based on machine learning and industrial data |
| topic | strip crown prediction industrial data machine learning hot strip rolling XGBoost algorithm |
| url | https://www.mdpi.com/2075-4701/13/5/900 |
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