基于机器学习的短纤维增强复合材料弹性力学性能预测  

Prediction of elastic properties of short fiber reinforced composites based on machine learning

作　　者：王吉玲 金浩 郭瑞文 史晨曦 杨礼芳 李梅娥 周进雄[2] WANG Jiling;JIN Hao;GUO Ruiwen;SHI Chenxi;YANG Lifang;LI Mei'e;ZHOU Jinxiong(State Key Laboratory for Mechanical Behavior of Materials,School of Materials Science and Engineering,Xi'an Jiaotong University,Xi'an 710049,China;School of Aerospace Engineering,Xi'an Jiaotong University,Xi'an 710049,China;Beijing Institute of Mechanical and Electrical Engineering,Beijing 100074,China)

机构地区：[1]西安交通大学材料科学与工程学院金属材料强度国家重点实验室,西安710049 [2]西安交通大学航天航空学院,西安710049 [3]北京机电工程研究所,北京100074

出　　处：《复合材料学报》2024年第11期6261-6272,共12页Acta Materiae Compositae Sinica

摘　　要：短纤维增强复合材料弹性力学性能受其内部结构和基础材料性能影响显著,参数化分析这些影响需要极高的实验或数值分析成本。针对这一问题,本文将基于周期性代表性体积单元(RVE)的数值均匀化方法与人工神经网络(ANN)进行结合,分别构建了空间随机分布、层内随机分布和定向排列3种形式的短纤维增强复合材料力学性能预测代理模型。每个代理模型均可以快速实现不同参数组合(纤维长度、长径比、体积分数及纤维和基体材料属性)下复合材料的等效弹性性能预测,拟合优度R2均在0.98以上,计算所用时间与常规模拟计算相比可忽略不计,大大节省了实验和计算成本,为短纤维增强复合材料的设计定制创造了重要条件。The elastic and mechanical properties of short fiber reinforced composites are significantly affected by their internal structure and the properties of the underlying materials,and the parametric analysis of these effects requires extremely high experimental or numerical analysis costs.In order to solve this problem,this paper combines the numerical homogenization method based on periodic representative volume units(RVE)and artificial neural network(ANN)to construct three forms of mechanical property prediction surrogate models of short fiber reinforced composites:Spatial random distribution,intralayer random distribution and aligned distribution,respectively.Each surrogate model can quickly predict the equivalent elastic properties of composites under different parameter combinations(fiber length,aspect ratio,volume fraction,and fiber and matrix material properties),and the goodness of fit R2 is above 0.98,the calculation time is negligible compared to conventional simulation calculations,which greatly saves experimental and computational costs and creates important conditions for the design and customization of short fiber-reinforced composites.

关 键 词：短纤维复合材料 RVE 参数化计算 代理模型 弹性性能预测 

分 类 号：TB330.1[一般工业技术—材料科学与工程]