汽车活塞销表面纳米复合镀层的制备及耐磨性预测
发布时间：2018-08-31 17:34
【摘要】：在汽车发动机活塞销表面制备Ni-ZrO_2纳米复合镀层,以期提高活塞销表面的耐磨性。通过正交试验,得到影响纳米复合镀层磨损量的因素主次顺序,并在正交试验基础上建立BP神经网络模型,对纳米复合镀层的磨损量进行预测,进而评价其耐磨性。研究表明:BP神经网络模型能用于预测纳米复合镀层的磨损量,磨损量的预测值与实验值具有较高的拟合度。通过正交试验与运用BP神经网络模型优化的最佳参数组合相同,采用最佳参数组合制备的纳米复合镀层的磨损量较低(仅为4.26mg),其耐磨性良好。
[Abstract]:Ni-ZrO_2 nanocomposite coating was prepared on the surface of motor piston pin in order to improve the wear resistance of piston pin surface. The primary and secondary order of the factors influencing the wear amount of nano-composite coating was obtained by orthogonal test. Based on the orthogonal test, the BP neural network model was established to predict the wear rate of nano-composite coating and then to evaluate its wear resistance. The results show that the ratio BP neural network model can be used to predict the wear of nano-composite coatings, and the predicted value of wear quantity has a high fitting with the experimental value. The orthogonal test is the same as the optimal parameter combination optimized by using BP neural network model. The wear rate of nano-composite coating prepared by the best parameter combination is lower (only 4.26mg), and its wear resistance is good.
【作者单位】： 四川航天职业技术学院;
【分类号】：TG174.4;U464.133.1
[Abstract]:Ni-ZrO_2 nanocomposite coating was prepared on the surface of motor piston pin in order to improve the wear resistance of piston pin surface. The primary and secondary order of the factors influencing the wear amount of nano-composite coating was obtained by orthogonal test. Based on the orthogonal test, the BP neural network model was established to predict the wear rate of nano-composite coating and then to evaluate its wear resistance. The results show that the ratio BP neural network model can be used to predict the wear of nano-composite coatings, and the predicted value of wear quantity has a high fitting with the experimental value. The orthogonal test is the same as the optimal parameter combination optimized by using BP neural network model. The wear rate of nano-composite coating prepared by the best parameter combination is lower (only 4.26mg), and its wear resistance is good.
【作者单位】： 四川航天职业技术学院;
【分类号】：TG174.4;U464.133.1
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