摘要
采用神经网络技术,构建结构为3×8×1型的BP神经网络模型,并利用该模型对超声电沉积Ni-SiC纳米镀层的耐磨性能进行预测。通过磨损试验测试并研究Ni-SiC纳米镀层的耐磨性能,利用扫描电镜(SEM)、原子力显微镜(AFM)和X射线衍射(XRD)观察不同参数下Ni-SiC纳米镀层的组织结构及成分。结果表明,在BP神经网络模型的隐含层数和神经元数分别为1和8时,该BP神经网络模型的均方根误差最小,其最小值为1.24%。该BP神经网络模型的预测值与实验值相差不大,其最大误差为1.51%。当采用SiC粒子浓度8 g/L、电流密度2 A/dm^2、温度40℃时,SiC粒子均匀分布于Ni-SiC纳米镀层中,且镀层镍晶粒显著细化,其镍晶粒的衍射峰变宽、变矮。
The BP neural network model with a structure of 3×8×1 was established by using artificial neural network technology.The wear resistance of the coating prepared by ultrasonic electrodeposition was predicted by this model.The wear resistance of Ni-SiC nanocoating was tested by a wear test and the microstructure and component of the coatings prepared at different parameters were observed via scanning electron microscope(SEM),atomic force microscope(AFM)and X-ray diffraction(XRD).The results showed that when the number of hidden layers was 1 and the number of neurons was 8,the root mean square error of the BP neural network model was the smallest,and the minimum value was only 1.24%.The predicted value of the BP neural network model was not much different from the experimental value,and the maximum error was 1.51%.When the concentration of SiC particles was 8 g/L,the current density was 2 A/dm^2,and the temperature was 40℃,the SiC particles were uniformly distributed in the Ni-SiC nanocoating.The nickel grains of the coating were obviously refined,and their diffraction peak became wider and shorter.
作者
李心源
马春阳
赵旭东
LI Xinyuan;MA Chunyang;ZHAO Xudong(Information Center, Daqing Normal University, Daqing 163712 China;College of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China)
出处
《功能材料》
EI
CAS
CSCD
北大核心
2020年第1期1126-1130,共5页
Journal of Functional Materials
基金
国家自然科学基金资助项目(51974089)
黑龙江省自然科学基金资助项目(LC2018020)
