基于神经网络遗传算法的超疏水涂层优化  

Optimization of Superhydrophobic Coatings Based on Neural Network and Genetic Algorithm

作　　者：苑昭阔 吴俐俊[1] 王骏 张萍[1] 韦增志 YUAN Zhao-kuo;WU Li-jun;WANG Jun;ZHANG Ping;WEI Zeng-zhi(School of Mechanical Engineering,Tongji University,Shanghai 201804,China;Nanjing Tongcheng Energy Saving and Environmental Protection Equipment Research Institute,Nanjing 211100,China)

机构地区：[1]同济大学机械与能源工程学院,上海201804 [2]南京同诚节能环保装备研究院,南京211100

出　　处：《表面技术》2022年第1期240-246,271,共8页Surface Technology

基　　金：国家重点研发计划(2020YFC1910100)。

摘　　要：目的探究超疏水涂层各成分的含量对涂层水接触角和导热系数的影响,找到最优成分组合,使涂层水接触角和导热系数同时获得最大值。方法根据设计的L_(25)(5^(5))正交试验,制作和测试涂层试样,借助Matlab软件建立结构为5-8-2的BP神经网络,通过正交试验结果训练和测试神经网络,得到涂层水接触角和导热系数的预测模型。调用训练好的预测模型,采用遗传算法对涂层各成分含量进行全局寻优。使用寻优得到的参数和调整后的参数进行试验,检验寻优计算结果。结果BP神经网络预测模型水接触角的最大误差为0.06198,导热系数的最大误差为0.06577。基于遗传算法的优化结果,涂层成分(质量分数)为纳米SiO_(2)10.1%+TiO_(2)6.4%+碳粉5%+纳米石墨烯0.6%+MTES 1.8%时,涂层的水接触角达到164.24°,导热系数达到14.19 W/(m·K),其误差分别为3.80%和2.31%。采用调整后的参数进行试验,测试得到涂层的水接触角为155.02°,导热系数为13.25 W/(m·K),其误差分别5.64%和5.58%。结论通过BP神经网络预测模型和遗传算法寻优,可以使涂层的水接触角和导热系数都获得较大的提高。The work aims to explore the influence of the content of each coatings component on the coatings water contact angle(WCA)and thermal conductivity,and find the optimal composition so as to maximize the WCA thermal conductivity simultaneously.The coatings samples were made and tested according to the L_(25)(5^(5))orthogonal experimental design.The BP neural network with the structure of 5-8-2 was established by Matlab software.The prediction model of WCA and thermal conductivity of coatings was obtained by training and testing the neural network with the results of orthogonal test.The genetic algorithm was used to optimize the content of each component by calling the trained prediction model.The optimized and adjusted parameters were used to test and verify the optimization results.After the BP neural network model was trained,the prediction results showed that the maximum error was 0.06198 and WCA 0.06577 for thermal conductivity.Based on the optimization results of genetic algorithm,the coatings would have 164.24°for WCA and 14.19 W/(m·K)for thermal conductivity,with 10.1wt%Nano-SiO_(2),6.4wt%TiO_(2),5wt%carbon powder,0.6wt%nano graphene and 1.8wt%MTES.In the meanwhile,the error of the WCA and thermal conductivity was 3.80%and 2.31%,respectively.The coatings made with adjusted parameters had 155.02°for WCA and 13.25 W/(m·K)for thermal conductivity,with errors of 5.64%and 5.58%,respectively.Through BP neural network prediction model and genetic algorithm optimization,the water contact angle and thermal conductivity of coatings both got greatly improved.

关 键 词：超疏水涂层 BP神经网络 遗传算法 水接触角 导热系数 

分 类 号：TB17[生物学—生物工程]