基于自组织神经网络的军用车辆装备涂层腐蚀行为研究  

Corrosion Behavior of Military Vehicle Equipment Coatings Based on Self-Organizing Feature Map Network

作　　者：蔡志强[1,2] 吕荣胜 张振楠[4] 

机构地区：[1]天津大学管理与经济学部,天津300072 [2]军事交通学院军用车辆系,天津300161 [3]天津理工大学管理学院,天津300384 [4]军事交通学院研究生管理大队,天津300161

出　　处：《材料保护》2018年第1期47-50,54,共5页Materials Protection

摘　　要：为探究军用车辆有机涂层在全浸泡条件下的腐蚀行为特征,寻找评价涂层腐蚀防护性能的有效方法,通过对某型军绿有机涂层全浸泡条件下腐蚀行为电化学阻抗谱(EIS)特征的研究,提取并研究了低频阻抗模值Z0.1Hz、高频相位角θ10 k Hz和高频阻抗模值变化率k这3种特征参数的变化规律,并结合自组织神经网络(SOM)对涂层防护性能变化进行了辅助分析。特征参数变化规律与SOM分析结果均证明涂层在1 330 d浸泡过程中出现4个阶段的性能变化,反映了SOM神经网络辅助分析有机涂层浸泡性能的有效性。Under immersion conditions, the corrosion behaviors of green organic coating were studied using electrochemical impedance spectroscopy （EIS） method, and three characteristic parameters （low frequency impedance ｜Z｜0.1Hz , high frequency phase angle θ10kHz and value rate of high frequency impedance k ） from the EIS plot were selected to evaluate the protective performance of the coating. Moreover, the analysis of coating protection was also assisted by self-organizing feature map （SOM） network. Results showed that both the change law of feature parameter and SOM analysis result proved that the protective performance of organic coatings during 1 330 d could be divided into four stages, which represented SOM neural network was a helpful method for assistanalyzing the protective performance of organic coating at immersion state.

关 键 词：军用车辆 有机涂层 全浸泡条件 特征参数 自组织神经网络 

分 类 号：TG172[金属学及工艺—金属表面处理]