Artificial Neural Network for Predicting Wear Properties of Brake Lining Materials

摘要：

Many factors influence the wear of friction material performance such as formulation, manufacturing condition and operating regimes, and so on. in this paper, the wear rate variation has been modeled by means of artificial neural network, the network have been developed with all these relevant factors taking into consideration. 16 influence factors and wear rate selected as input and output respectly, 16 1082 1 is regarded as the best architecture of neural network, the Levenberg-Marquardt algorithm is used for training the network. The result shows that the model is valid to predict the wear property, as well as that it is useful for optimizing the formulation and manufacturing conditions, the relatively excellent combination of the ingredients and the appropriate manufacturing condition parameters can be obtained by this approach.

关键词： Friction materials Artificial neural network Prediction

作者: Junhua Han Qisheng Wu

作者单位: School of material Sci & Eng of Jiangsu University, Jiangsu Zhenjiang, China School of Materials Engineering, Yancheng Institute of Technology, Jiangsu Yancheng, China

会议类型: 国际会议

会议名称: 2011 International Conference on Mechatronics and Materials Processing(2011年机电一体化与材料加工国际会议 ICMMP)

会议地点: 广州

会议语种:英文

页码: 237-240

在线出版日期: 2011-11-18（万方平台首次上网日期，不代表论文的发表时间）