论文部分内容阅读
该文通过钢-钢摩擦副的摩擦学性能的对比研究,发现在磨损自补偿添加剂SW4作用下,钢-钢摩擦副也象钢-铜摩擦副一样具有磨损自补偿性能,其摩擦系数和磨损量相对下降了很多;同时,利用BP神经网络理论对钢-钢摩擦副的摩擦学过程,特别是其磨损自补偿过程进行了预测,该方法与传统的建模方法相比,具有运算时间短,使用数据少,不需要严格的数学模型等优点,其预测值与试验值非常接近.“,”Through the contrast research of the tribological characteristic of steel - steel tubbing pair, it was found that the steel -steel rubbing pair of using additive SW4 is of wear-setf-compensation's characteristic as well as steel- copper rubbing pair and the frictional coefficient and wear drop down largely in this paper. The method of BP neural network theory that was used to predict the tribological process of steel-steel nibbing μair and specially the wear-self-compensation's process need not to build up mathematical model in contrast withtraditional modeling methods, and reduce computer time and application data. The predicting results are close to experiment data.