A deep learning convolutional neural network and multi-layer perceptron hybrid fusion model for predicting the mechanical properties of carbon fiber. (March 2023)

• Record Type: Journal Article
• Authors: Li, Mengze; Li, Shuran; Tian, Yu; Fu, Yihan; Pei, Yanliang; Zhu, Weidong; Ke, Yinglin
• Is Part Of: Materials & design. Volume 227(2023)
• Publication Date: 2023-03
• Subjects: Carbon fibers -- Polymer-matrix composites (PMCs) -- Mechanical properties -- Deep learning -- Multimodal fusion; Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2023.111760
• Languages: English
• ISSNs: 0264-1275

Abstract

Graphical abstract: Highlights: Stochastic microstructures containing randomly distributed fibers with non-circular cross-sections are generated. A novel deep learning multimodal fusion model is proposed for predicting the mechanical properties of carbon fibers. The prediction accuracy is significantly improved via multisource heterogeneous data compared with single data type. Abstract: Recently, deep learning methods have become one of the hottest topics in predicting material properties, however, one bottleneck in current research is the simultaneous analysis of heterogeneous data. In this study, a deep learning fusion model is developed for the first time to predict the material properties of carbon fiber monofilament using textual (macroscopic properties of composites and matrix) and visual (two-point statistics of microstructures) data. For this, 1200 stochastic microstructures are generated using the greedy-based generation (GBG) algorithm. Then, the statistical representations of microstructures are determined using two-point statistics and the macroscopic properties are calculated based on a micro-scale finite element (FE) simulation. Finally, the visual and textual data are fed into the convolutional neural network (CNN) and multi-layer perceptron (MLP) fusion model for predicting the mechanical properties of carbon fibers. The developed hybrid CNN-MLP fusion model achieves encouraging average testing R 2 of longitudinal modulus, transverse modulus, in-plane shear modulus, major Poisson's ratio, and out-of-plane shear modulus of carbon fibers with values of 0.991, 0.969, 0.984, 0.903, and 0.955, respectively. Thus, the proposed strategy provides a promising framework for predicting material properties via multisource heterogeneous data and is expected to accelerate the smart design and optimization of materials.