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Soft Measurement Model of Ring’s Dimensions for Vertical Hot Ring Rolling Process Using Neural Networks Optimized by Genetic Algorithm by Xiao-kai Wang; Lin Hua; Xiao-xuan Wang; Xue-song Mei; Qian-hao Zhu; Yu-tong Dai is a Engineering article available to read on EtoBox.

What is Soft Measurement Model of Ring’s Dimensions for Vertical Hot Ring Rolling Process Using Neural Networks Optimized by Genetic Algorithm about?

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 RB

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Author

Xiao-kai Wang; Lin Hua; Xiao-xuan Wang; Xue-song Mei; Qian-hao Zhu; Yu-tong Dai

Publisher

Springer Science and Business Media LLC

Published

2017

Language

EN

Field

Engineering (Physical Sciences)