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中南大学学报(英文版)

Journal of Central South University

Vol. 28    No. 8    August 2021

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Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling
JI Ya-feng(姬亚锋)1, SONG Le-bao(宋乐宝)1, SUN Jie(孙杰)2, PENG Wen(彭文)2, LI Hua-ying(李华英)3, MA Li-feng(马立峰)1

1. School of Mechanical Engineering, Taiyuan University of Science and Technology,
Taiyuan 030024, China;
2. State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China;
3. School of Materials Science and Engineering, Taiyuan University of Science and Technology,
Taiyuan 030024, China

Abstract: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.

 

Key words: strip crown; support vector machine; principal component analysis; cuckoo search algorithm; particle swarm optimization algorithm

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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