A highly interpretable materials informatics approach for predicting microstructure-property relationship in fabric composites

Multiscale properties of fabric-reinforced composites are commonly modeled via numerical and experimental methods, which are often highly time-consuming and complex. In this paper, a materials informatics-based approach has been developed to link the micro/meso-level features of woven fabric composites, obtained via nondestructive micro-CT images, to their effective Young's moduli. To this end, a reduced-order quantification of a typical glass fiber/polypropylene lamina's microstructure is established using two-point spatial correlations and the principal component analysis. Next, a machine-learning model is implemented to predict the material microstructure-property (modulus) relationship via the captured images. Despite the limited number of samples, the presented data-driven techniques led to a model with highly interpretable components and excellent accuracy for different ply orientations, regardless of apparent uncertainties such as waviness. The findings appear to be a promising step forward for the potential use of materials informatics for smart design and optimization of woven fabric composites in prominent industries, including aerospace and transportation.

Automated summary

In this study, a materials informatics-based approach was used to link micro/meso-level features of woven fabric composites to their effective Young's moduli using nondestructive micro-CT images. Despite the limited number of samples, the data-driven techniques led to a highly interpretable model with excellent accuracy for different ply orientations, demonstrating the potential use of materials informatics for smart design and optimization in industries such as aerospace and transportation.