摘要
为提高水性防腐涂料湿附着力,提出基于人工神经网络(ANN)和遗传算法(GA)的水性防腐配方工艺优化模型。采用正交法设计配方工艺,根据实验结果,建立配方(水性环氧乳液、石墨烯、水性环氧固化剂、填料、附着力促进剂)、工艺(干燥温度)和性能(湿附着力)的预测模型,以水性防腐涂料湿附着力预测结果为优化目标,通过GA得出最佳的水性防腐涂料配方和工艺,并对该配方和工艺进行了实验和理论验证。该方法为制备高性能水性防腐涂料提供一定指导依据。
In order to improve the wet adhesion of waterborne anticorrosive coatings,a process optimization model based on the artificial neural network(ANN)and genetic algorithm(GA)is proposed. First,the orthogonal design method is used to design the experimental formula of waterborne anticorrosive coatings. According to the experimental data,the prediction models for the formulas(e. g.,waterborne epoxy resin,graphene,pigment,waterborne epoxy curing agent,and adhesion promoter),process parameters(e.g.,drying temperature),and main properties(e.g.,wet adhesion)are established. Then,the prediction results of the wet adhesion of water-based anticorrosive coatings are taken as the optimization objective,and the genetic algorithm is used to get the best formula and process. Moreover,the performances of the best formula and process are tested and verified. This method provides certain guidance for the preparation of high-performance waterborne anticorrosive coatings.
作者
伍权
刘栓
卢光明
蒲吉斌
WU Quan;LIU Shuan;LU Guangming;PU Jibin(Ningbo Zhongke‑Yinyi New Material Co.,Ltd.,Ningbo 315000,Zhejiang,China;Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo 315201,Zhejiang,China)
出处
《上海航天（中英文）》
CSCD
2022年第6期135-141,共7页
Aerospace Shanghai（Chinese&English）
关键词
水性防腐涂料
湿附着力
人工神经网络
遗传算法
工艺优化
waterborne anticorrosive coating
wet adhesion force
artificial neural network
genetic algorithm
process optimization
