Evaluating Fiber Detection Models Using Neural Networks

Abstract

Ceramic matrix composites are resistant materials that withstand high temperatures, but quality control of such composites depends on microtomography image analysis to enable the spatial analysis of fibers, matrix cracks detection, among others. While there are several approaches for fiber detection from microtomography, materials scientists lack computational schemes to validate the accuracy of different fiber detection models. This paper proposes a set of statistical methods to analyse images of CMC in 3D and visualize respective fiber beds, including a lossless data reduction algorithm. The main contribution is our method based on a convolutional neural network that enables evaluation of results from automated fiber detection models, particularly when compared with human curated datasets. We build all the algorithms using free tools to allow full reproducibility of the experiments, and illustrate our results using algorithms designed to probe sample content from gigabyte-size image volumes with minimalistic computational infrastructure.