Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites  

作　　者：Chengkan Xu Xiaofei Wang Yixuan Li Guannan Wang He Zhang 

机构地区：[1]Transportation and Municipal Engineering Institute,Powerchina Huadong Engineering Corporation,Hangzhou,310014,China [2]College of Civil Engineering and Architecture,Zhejiang University,Hangzhou,310058,China [3]Anhui Transport Consulting&Design Institute Co.,Ltd.,Hefei,230088,China [4]Center for Balance Architecture,Zhejiang University,Hangzhou,310058,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第7期957-974,共18页工程与科学中的计算机建模（英文）

基　　金：the support from the National Key R&D Program of China underGrant(Grant No.2020YFA0711700);the National Natural Science Foundation of China(Grant Nos.52122801,11925206,51978609,U22A20254,and U23A20659);G.W.is supported by the National Natural Science Foundation of China(Nos.12002303,12192210 and 12192214).

摘　　要：Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.

关 键 词：Periodic composites localized stress recovery conditional generative adversarial network 

分 类 号：TB33[一般工业技术—材料科学与工程]