首页|In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning
In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning
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Dielectric elastomers(DEs)require balanced electric actuation performance and mechanical integrity under ap-plied voltages.Incorporating high dielectric particles as fillers provides extensive design space to optimize con-centration,morphology,and distribution for improved actuation performance and material modulus.This study presents an integrated framework combining finite element modeling(FEM)and deep learning to optimize the microstructure of DE composites.FEM first calculates actuation performance and the effective modulus across varied filler combinations,with these data used to train a convolutional neural network(CNN).Integrating the CNN into a multi-objective genetic algorithm generates designs with enhanced actuation performance and ma-terial modulus compared to the conventional optimization approach based on FEM approach within the same time.This framework harnesses artificial intelligence to navigate vast design possibilities,enabling optimized microstructures for high-performance DE composites.
Dielectric elastomer compositesMulti-objective optimizationFinite element modelingConvolutional neural network
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NSTL
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