Publication

Visual analysis of void and reinforcement characteristics in X-ray computed tomography dataset series of fiber-reinforced polymers

Publication, 2018

Outline

M. Schiwarth, J. Weissenböck, B. Plank, B. Fröhler, C. Heinzl, J. Kastner - Visual analysis of void and reinforcement characteristics in X-ray computed tomography dataset series of fiber-reinforced polymers - Proceedings of the 13th International Conference on Textile Composites, Mailand, Italy, 2018, pp. 12

Abstract

Fiber-reinforced polymers (FRPs) are of great importance in various industries because of their superior properties as compared to conventional materials, their versatile processing, and their wide application possibilities. To fulfil the high-quality standards in its respective applications, industrial 3D X-ray computed tomography (XCT) is increasingly used. It enables an accurate, non-destructive characterization of material features such as inclusions, voids, fibers, or other reinforcements, which is of core importance for material and component design. In this work we present FeatureAnalyzer, a generalization of the previously introduced PorosityAnalyzer tool, which allows to analyze dataset series as generated for exploring the parameter space of image processing workflows (including pre-filtering, segmentation, post-processing or quantification) applied to XCT datasets of fiber-reinforced polymers. With a scatter plot matrix (SPLOM), the characteristics of the features of interest may be examined in more detail regarding the used input and output parameters. Individual results may be selected in the SPLOM and analyzed using 2D slice views and 3D renderings. For this work, three different samples (sample #1 - #3) were scanned by means of XCT and were evaluated by using FeatureAnalyser. For sample #1 and #2 containing porosity in the range of 1.7 vol. %. By using the FeatureAnalyzer in combination with SPLOM threshold parameters could be analyzed before the over segmentation of voids occurs. Additional evaluations by parallel coordinates clearly shows, that sample #2 has a higher number of spherical voids in the center of the specimen compared to sample #1. By evaluating the resin content of sample #3, the individual layer thickness could be measured. The source code of the tool is available on github: https://github.com/3dct/open_iA/