Prediction of Wear Volumes to 45 Steel of NBR Based on Neural Network

Xuan Luo; Shi Jie Wang; Xiao Ren Lv

doi:10.4028/www.scientific.net/AMM.66-68.788

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 66-68Prediction of Wear Volumes to 45 Steel of NBR...

Prediction of Wear Volumes to 45 Steel of NBR Based on Neural Network

Abstract:

The wear to orthogonal metals of NBR is the main cause of affecting the endurance of ESPCP. The rotational speed, load and temperature are main influence factors of the wear of 45 steel. The BP neural network model used in the forecast of the 45 steel wear volume was established. The 45 steel wear volume was obtained using friction and wear machine under different experimental parameters. The wear volumes of different experimental parameters were forecasted using BP neural network. The results indicate that it is feasible to forecast the rotational speed, load and temperature to 45 steel wear volume.

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Periodical:

Applied Mechanics and Materials (Volumes 66-68)

Pages:

788-792

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.66-68.788

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Online since:

July 2011

Authors:

Xuan Luo, Shi Jie Wang, Xiao Ren Lv

Keywords:

ESPCP, Neural Network (NN), Nitrile-Butadiene Rubber (NBR), Wear

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References

[1] Wang guiyi. Friction and Testing of Rubbers [J]. Special Purpose Rubber Products, 2000, 21(3): 55-62. (In Chinese).

Google Scholar

[2] Lv Renguo; Li Tongsheng; Huang Xinwu. Study on tribological behavior of NBR under variable velocity [J]. China Synthetic Rubber Industry, 2002, 25(2): 10-103.

Google Scholar

[3] Qiu Ming, Zhang Yong-zhen, Zhu Jun. Prediction Modeling of Friction Behavior of Aluminum Matrix Composites Using Neural Network and Genetic Algorithms under High Velocity and Dry Sliding Condition [J]. Tribology, 2005，125（6）：545-549.

Google Scholar

[4] Fei si technological products research and development center. Neural network theory and MATLAB7 realize [M]. Beijing: Publishing House of Electronics Industry, 2005, 7. (in Chinese).

Google Scholar

[5] Zhang siwei. Wear of rubber principle [J]. Beijing: Petroleum Industry Press, (1998).

Google Scholar