Benchmarking and sensitivity analysis of segmentation methods for image-based fiber detection in composites

Abstract

The accurate characterization of the microstructure of fiber-reinforced polymer composites is crucial for quality control in manufacturing, material design, and robust performance prediction. Most of the characterization methods rely on the analysis of 2D or 3D images. However, the composites community lacks commonly shared best practices and a clear quantitative understanding of how different image processing approaches compare. This work presents a benchmarking exercise on image processing of fiber-reinforced composite materials to address this challenge. Processing three cross-section micrographs, acquired from a single unidirectional composite sample using typical yet distinct imaging protocols, 11 participants from 8 research institutions extracted fiber centroids and radii. The estimated fiber volume fraction (Vf) values for a single cross-section varied between participants from 0.42 to 0.65. The results highlight the sensitivity of different methods to factors like illumination inhomogeneities, pixel density, polishing-related surface defects, and fiber packing. Considering these observations, several best practices for image analysis are identified, providing critical insight into the methods’ sensitivities. To promote transparency and community uptake, the benchmark dataset, evaluation scripts, documentation, and algorithms that were open-sourced are made openly available through a dedicated website, paving the way towards more reliable microstructural analysis in composite materials and ultimately more standardized protocols.