基于人工神经网络的短纤维增强复合材料设计

打开文本图片集

中图分类号：TP183；TB334 文献标志码：B

Short fiber reinforced composite material design based on artificial neural network

LI Xiaoa，XIA Jiajiaa， ZHANG Xiangkui （a.School of Automotive Engineering；b. School of artificial Intelligence，Dalian University of Technology， Dalian ，Liaoning，China）

Abstract： In order to guide the design of short fiber reinforced composites，a method based on artificial neural network is proposed.First，a mathematical model is established by combining orientation averaging method and self-consistent hypothesis，to calculate the elastic properties of SFRCs under diferent volume fractions.Then，two diferent artificial neural networks are built，and the data sets obtained from the mathematical model are trained. The elastic properties of SFRCs can be quickly predicted from the volume fraction of SFRCS.The fiber volume fraction can also be inversely derived from the known elastic properties of SFRCs. Compared with the traditional method，it can effctively reduce the number of repeated experiments，reduce the cost and cycle，and has certain reference significance for the setting of composite experiments.

Key words： SFRCs； mesomechanics； artificial neural network ；elastic property；train；volume fraction

0 引言

些年越来越受到不同行业的关注[1]。（剩余8504字）