Evaluating Fiber Detection Models Using Neural Networks
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.
KeywordsComputer vision Materials science CNN
This research is funded in part by the Gordon and Betty Moore Foundation through Grant GBMF3834 and by the Alfred P. Sloan Foundation through Grant 2013-10-27 to the University of California, Berkeley. Algorithmic work is partially supported by the Office of Science of the US Department of Energy (DOE) under Contract No. DE-AC02-05CH11231, Advanced Scientific Computing Research (ASCR) Early Career Award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and they do not necessarily reflect the views of DOE or the University of California.
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