Tribological Properties Prediction of Brake Lining for Automobiles Based on BP Neural Network
By many tribological experiments of brake lining for automobiles, the original experimental data were firstly obtained, which contains the influencing rules of braking conditions on tribological performance. Based on the artificial neural network technology and the experimental data specimens, the BP neural network model was established to predict the tribological properties. Three parameters of braking conditions (braking pressure, sliding velocity and surface temperature) were selected as input vectors, and two parameters of tribological performance (friction coefficient and wear rate) were selected as output vectors. By contrast of prediction values and experimental results, it is found that the neural network can predict properly the influencing rules of braking conditions on tribological performance. What is more, the neural network has quite favorable ability for predicting of friction coefficient. While it has bad ability for predicting of wear rate, especially when the pressure, velocity and temperature are high. As a whole, this paper has proved that it is feasible and valuable to use neural network for predicting tribological properties of friction materials.
neural network friction coefficient wear rate prediction brake lining
Yan Yin Jiusheng Bao Lei Yang
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, Xuhai College, China University of Mining and Technology, Xuzhou, 221008
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
2678-2682
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）