祝贺李子琪关于基于力学的机器学习预测纤维增强复合材料弹塑性性能的论文“A mechanics-informed machine learning approach for modeling the elastoplastic behavior of fiber-reinforced composites”被 Composite Structures 接收 !

Details：Ziqi Li,Xin Li,Yang Chen,Chao Zhang.A mechanics-informed machine learning approach for modeling the elastoplastic behavior of fiber-reinforced composites.Composite Structures,2023,323(8):117473

Link：https://doi.org/10.1016/j.compstruct.2023.117473

ABSTRACT

When machine learning (ML) techniques are used to predict the elastoplastic behavior of a fiber-reinforced composite, a large training database is typically required due to the complicated network architecture that is built to characterize the anisotropic plasticity. In this paper, a mechanics-informed ML approach that enables to employ a small training database is proposed to predict the elastoplastic behaviors of a unidirectional fiber reinforced composite by incorporating mechanics-based decompositions of strain and stress into an artificial neural network (ANN). The built ANNs have simple structures and greatly enhance the prediction capability of the ML-based constitutive model when using a small database. The ML approach is further improved to predict the effect of the loading path. Direct numerical simulations (DNSs) based on a representative volume element are carried out to generate the datasets used for training and validating the ML-based constitutive model. By comparing the results obtained when using DNS and ML, it is shown that the proposed ML-based constitutive model offers excellent predictive accuracy even when using a small training database, and it provides much better results than a ML approach that does not include the decomposition of strain and stress.