Soft measurement model of ring’s dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm

• Published in: Journal of Central South University Vol. 24; no. 1; pp. 17 - 29
• Main Authors: Wang, Xiao-kai, Hua, Lin, Wang, Xiao-xuan, Mei, Xue-song, Zhu, Qian-hao, Dai, Yu-tong
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.01.2017; Springer Nature B.V
• Subjects: Engineering; Genetic algorithms; Mathematical analysis; Metallic Materials; Neural networks; Position measurement; Radial basis function; Ring rolling; genetic algorithm; artificial neural network; dimension precision; soft measurement model; vertical hot ring rolling
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-017-3404-1

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.