自然科学版 英文版
自然科学版 英文版
自然科学版 英文版

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中南大学学报(英文版)

Journal of Central South University

Vol. 24    No. 1    January 2017

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Soft measurement model of ring’s dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm
WANG Xiao-kai(汪小凯)1, 2, HUA Lin(华林)1, 3, WANG Xiao-xuan(汪晓旋)4, MEI Xue-song(梅雪松)2, ZHU Qian-hao(朱乾浩)5, DAI Yu-tong(戴玉同)5

1. Hubei Key Laboratory of Advanced Technology for Automotive Components,
Wuhan University of Technology, Wuhan 430070, China;
2. College of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
3. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China;
4. National Automobile Quality Supervision Test Center, Xiangyang 441004, China;
5. Zhangjiagang Hailu Annular Forgings Co., Ltd., Zhangjiagang 215626, China

Abstract:Vertical hot ring rolling (VHRR) process has the characteristics of nonlinearity, time-variation and being susceptible to disturbance. Furthermore, the ring’s growth is quite fast within a short time, and the rolled ring’s position is asymmetrical. All of these cause that the ring’s dimensions cannot be measured directly. Through analyzing the relationships among the dimensions of ring blanks, the positions of rolls and the ring’s inner and outer diameter, the soft measurement model of ring’s dimensions is established based on the radial basis function neural network (RBFNN). A mass of data samples are obtained from VHRR finite element (FE) simulations to train and test the soft measurement NN model, and the model’s structure parameters are deduced and optimized by genetic algorithm (GA). Finally, the soft measurement system of ring’s dimensions is established and validated by the VHRR experiments. The ring’s dimensions were measured artificially and calculated by the soft measurement NN model. The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data. In addition, the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model. The research results suggest that the soft measurement NN model has high precision and flexibility. The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.

 

Key words: vertical hot ring rolling; dimension precision; soft measurement model; artificial neural network; genetic 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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