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中文摘要：
以Al2O3陶瓷材料为对摩件,采用销-盘式高温磨损试验机,考察了单一因素（温度、载荷和转速）变化时MoSi2材料的摩擦因数和磨损率。研究了温度、载荷及转速交叉变化下MoSi2材料磨损率的神经网络预测,建立了具有学习率自适应和附加二次动量项的BP神经网络预测模型,并给出了神经网络的训练过程。神经网络预测结果和实际测试结果表明,预测误差控制在3%以内,BP神经网络具有较高的预测精度,可以满足MoSi2磨损率的预测需要。
英文摘要：
Using the Al2O3 ceramic as friction pair,the effects of single influence factor（temperature,load or speed） on the friction factor of MoSi2 material were studied by high temperature friction and wear testing machine.The wear rate predication of MoSi2 material under interaction among temperature,load and speed were also studied.The adaptive learning rate and with additional secondary momentum BP neural networks prediction model were studied.The network training process was also given.Network prediction and the actual testing results show that the improved BP neural network has higher prediction accuracy and the prediction error was controlled less than 3%.Network prediction can meet the predicting needs of molybdenum disilicide under the complex conditions.
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