摘要
电刷镀制备Al2O3-13%TiO2(AT13)复合陶瓷涂层是1个多参数耦合的非线性过程。在分析工艺参数对涂层厚度影响的基础上,通过实验采集样本,建立预测涂层厚度的误差反向传播(back propagation,BP)人工神经网络模型。为验证人工神经网络预测模型的准确性,将该模型的预测结果与多元线性回归模型(multiple linear regression model,MLR)的预测结果进行对比。结果表明:与传统多元线性回归模型相比,人工神经网络模型能捕捉工艺参数的非线性规律,能更好地预测涂层厚度,拟合优度R2达到0.86,模型具有较强的泛化能力和自适应能力,为实现电刷镀制作过程中涂层厚度的实时预测与控制提供参考。
The forming process of Al2O3-13TiO2 （mass fraction,%） composite ceramic coating by electro-brush plating is a non-linear process with multi-parameters coupling. Based on the analysis of the influences of process parameters on coating thickness, the back propagation （BP） artificial neural network prediction model was carried out through the samples acquired by electro-brush plating experiment. In order to verify the accuracy of coating thickness prediction based on BP artificial neural network model, multiple linear regression model （MLR） and BP artificial neural network model were compared. The results show that BP artificial neural network model can be trained to model the highly non-linear relationships between coating thickness and process parameters, and to provide better results than the traditional multiple linear regression models with R2 of 0.86; meanwhile, the BP artificial neural network model has strong generalization ability and adaptive capacity, it referes for the theoretical foundation for real-time coating thickness prediction and control.
作者
时礼平
吴玉国
迟开红
陈彬
吴胜
Shi, Li-Ping[1]; Wu, Yu-Guo[1]; Chi, Kai-Hong[2]; Chen, Bin[1]; Wu, Sheng[1]
出处
《粉末冶金材料科学与工程》
EI
北大核心
2013年第5期621-626,共6页
Materials Science and Engineering of Powder Metallurgy
基金
安徽工业大学青年基金资助项目(QZ201023)
马鞍山市博望区资助项目(BW20120016)
关键词
AT13
复合陶瓷涂层
电刷镀
BP人工神经网络
AT13
composite ceramic coating
electro-brush plating
BP artificial neural network
