Application of convolutional neural networks and ensemble methods in the fiber volume content analysis of natural fiber composites

Abstract

The incorporation of natural fibers into fiber-reinforced polymer composites (FRPC) has the potential to bolster their sustainability. A critical attribute of FRPC is the fiber volume content (FVC), a parameter that profoundly influences their thermo-mechanical characteristics. However, the determination of FVC in natural fiber composites (NFC) through manual analysis of light microscopy images is a labor-intensive process. In this work, it is demonstrated that the pixels from light microscopy images of NFC can be utilized to predict FVC using machine learning (ML) models. In this proof-of-concept investigation, it is shown that convolutional neural network-based models predict FVC with an accuracy required in polymer engineering applications, with a mean average error of 2.72 % and an $R^2$ coefficient of 0.85. Finally, it is shown that much simpler ML models, non-specialized in image recognition, besides being much easier and more efficient to optimize and train, can also deliver good accuracies required for FVC characterization, which not only contributes to the sustainability, but also facilitates the access of such models by researchers in regions with little computational resources. This study marks a substantial advancement in the area of automated characterization of NFC, and democratization of knowledge, offering a promising avenue for the enhancement of sustainable materials.