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

• Published in: Composites science and technology 2022-01, Vol.217, p.109080, Article 109080
• Main Authors: Olfatbakhsh, Tina, Milani, Abbas S.
• Format: Article
• Language: English
• Subjects: Aerospace industry; Composite materials; Computed tomography; Design optimization; Glass fiber reinforced plastics; Informatics; Machine learning; Materials information; Mechanical property; Microstructure; Modulus of elasticity; Multiscale modeling; Nonwoven fabrics; Numerical analysis; Numerical methods; Principal components analysis; Two-point statistics; Waviness; Woven fabric; X-ray computed tomography
• ISSN: 0266-3538; 1879-1050
• DOI: 10.1016/j.compscitech.2021.109080

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. [Display omitted]