Characterizing heterogenous microstructures of fiber-reinforced composite materials using an advanced image processing-based approach through optical microscopic images

Abstract

The microstructures of heterogeneous fiber-reinforced composite materials exhibit high variations, which may affect the localized material mechanical properties, create the material machinability issue, and further cause severe surface integrity issues during machining processes. Most existing approaches to studying the heterogenous microstructure of composite usually require mechanical testing and extensive post-material characterizations. Even worse, experimental testing can damage material permanently, and offline characterizations (e.g., SEMs) are expensive and time-consuming. Therefore, creating a noncontact, fast, accurate, and affordable material characterization tool can help analyze heterogenous microstructures of composite materials and boost their industrial applications. This study presents an image process-based analytic approach to characterizing microstructural features. This approach performs well in extracting microstructures from blurred and translucent optical microscopic images. The further generated features, including fiber volume fraction and other physical descriptors, capture the microstructural features and quantify the material's heterogeneity. An exemplary case study using a two-dimension spatial pattern and an experimental case study show that the presented approach allows rapid and accurate characterizations of fiber-reinforced composites with anomaly detections on the local microstructural heterogeneity.