Performance prediction of IPMC modified with SiO_(2)-SGO based on backpropagation neural network  

作　　者：Zhengxin Zhai Aifen Tian Xinrong Zhang Huiling Du Yaping Wang 

机构地区：[1]School of Materials Science and Engineering,Xi’an University of Science and Technology,Xi’an 710054,China [2]Key Laboratory of Road Construction Technology and Equipment,MOE,Chang’an University,Xi’an 710064,China [3]Shanxi College of Communication Technology,Xi’an 710018,China

出　　处：《Nanotechnology and Precision Engineering》2024年第4期65-74,共10页纳米技术与精密工程(英文)

基　　金：funded by the Digital workshop dynamic Reconstruction modeling Technology(Grant No.2020YFB1710701);the National Natural Science Foundation of China(Grant No.52172099);the Provincial Joint Fund of Shaanxi(Grant No.2021JLM-28).

摘　　要：Ionic polymer-metal composites(IPMCs)constitute a new type of artificial muscle material that is commonly used in bionic soft robots and medical devices because of its small driving voltage and considerable deformation.However,IPMCs are limited by performance issues such as low output force and small operating time away from water.Silicon dioxide sulfonated graphene(SiO_(2)-SGO)particles are often used to improve the performance of polymer membranes because of their hydrophilicity and high chemical stability.Reported here is the addition of SiO_(2)-SGO particles prepared by in situ hydrolysis to perfluorosulfonic acid in order to improve the IPMC properties.Also,a predictive model was constructed based on a backpropagation neural network,with the SiO_(2)-SGO doping amount and the IPMC excitation voltage in the input layer and the driving displacement in the output layer.The results show that the IPMC prepared with 1.0 wt.%doping content performed the best,with a maximum output displacement of 47.7 mm.The correlation coefficient(R2)was 0.9842 and the mean square error was 0.00037073,which show that the predictive model has high predictive accuracy and is suitable for predicting the performance of the SiO_(2)-SGO-modified IPMC.

关 键 词：Ionic polymer-metal composite SiO_(2)-SGO Backpropagation neural network Prediction model 

分 类 号：TB333[一般工业技术—材料科学与工程] TP183[自动化与计算机技术—控制理论与控制工程]