AI-Based Design of Hybrid Ionic Polymer–Metal Composite with CNT and Graphene

被引：3

作者：

Chaitanya K.S.K. ^{[1
]}

Datta S. ^{[1
]}

机构：

[1] Department of Mechanical Engineering, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, Chennai

来源：

Journal of The Institution of Engineers (India): Series D | 2022年 / 103卷 / 01期

关键词：

Artificial neural network; Carbon nanotube; Genetic algorithm; Graphene oxide; Ion exchange capacity; Ionic polymer metal composites; Nafion; Water uptake;

D O I：

10.1007/s40033-021-00314-w

中图分类号：

学科分类号：

摘要：

Ionic polymer metal composites (IPMC) are an emerging class of electro-active polymers, which are used as sensors and actuators in soft robots. In this study, the methodology of performance improvement of the Nafion-based IPMC actuators by using nanoparticles. The implementation of artificial neural network (ANN) for developing data-driven models and genetic algorithm (GA) as multi-objective optimization technique to achieve the required hybrid IPMC actuator. ANN models are developed for water uptake and ion exchange capacity of the IPMC with graphene and/or carbon nanotubes. The ANN models are used as the objective function for the GA-based maximization of both the objectives. The optimization is for designing a new hybrid IPMC actuator with higher blocking force. The Pareto solutions are used to find the combination of nanoparticles suited for better performance of the IPMC as actuator. It seen that for the designed hybrid IPMC a length of 20–22 mm and the with high weight percentages of GO (7%) and CNT (6%) is preferred to achieve the desired properties. © 2021, The Institution of Engineers (India).

引用

页码：37 / 44

页数：7

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