RBF神经网络在Cu-Al_2O_3复合电镀工艺中的应用  

Application of RBF Neural Network in Cu-Al_2O_3 Composite Electroplating Technology

作　　者：贾凌杉 JIA Lingshan(Hebei University of Environmental Engineering, Qinhuangdao 066100, China)

机构地区：[1]河北环境工程学院,河北秦皇岛066100

出　　处：《电镀与环保》2019年第1期21-23,共3页Electroplating & Pollution Control

基　　金：秦皇岛市重点研发计划科技支撑项目(201703A024)

摘　　要：采用复合电镀工艺制备了Cu-Al_2O_3复合镀层。用X射线衍射仪表征了镀层的微观结构,计算出平均晶粒尺寸,并用维氏硬度计测量了镀层的显微硬度。将电流密度和镀液中Al_2O_3微粒的质量浓度作为输入变量,并将镀层的平均晶粒尺寸和显微硬度作为输出结果,建立了RBF神经网络。仿真结果表明:RBF神经网络具有较强的预测能力,其预测结果与实测结果较为接近,平均误差约为0.15%,为Cu-Al_2O_3复合电镀工艺优化提供了参考。Cu-Al2O3 composite coatings were prepared by composite electroplating technology.The microstructure of the coatings was characterized by X-ray diffractometer,and the average grain size was calculated,the microhardness of the coatings was also measured by vickers hardness tester.RBF neural network was established by using current density and mass concentration of Al2O3 particles in plating bath as the input variable,and the average grain size and microhardness of the coatings as output results.The simulation results showed that RBF neural network has preferable predictive ability,the predicted value and experimental value were in good agreement,and the average error was about 0.15%.It provides references for the optimization of Cu-Al2O3 composite electroplating technology.

关 键 词：RBF神经网络 CU-AL2O3 复合电镀工艺 显微硬度 预测结果 实测结果 

分 类 号：TQ153[化学工程—电化学工业]