Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data—A Simulation Study

Abstract

We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

“…Every real-world problem is associated with some noise due to measurement error, equipment error, calibration error, wrong assumptions, wrongly assigned classes/labels, modeling error and many other unavoidable errors [397]. In the case of polymer composites, data obtained from tomographic studies, X-ray projection, non-destructive testing, and acoustic emission is often noisy [156,[398][399][400]. Consequently, the pattern recognition and identification of defects in the composites become difficult and result in inaccurate predictive modeling.…”

A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science

“…The second algorithm we have been evaluating, Parametric Reconstruction (PARE), has recently been published by Elberfeld et al [Edd*18]. It directly inputs the CT projection images, performs an initial fiber characterization from a SIRT reconstruction using the ASTRA toolbox [vPC*16] and then refines this characterization by projecting the model of the recognized fibers forward (again) into the space of the projection images.…”

“…More recently, techniques for a direct extraction of fiber characteristics from CT projection data have been proposed. For example, Elberfeld et al [Edd*18] reconstruct volumes from a small numbers of projection angles and then estimate position, direction and length of the contained fibers using a priori knowledge of their shape, modeled as a geometric representation. This direct step is indicated by the red arrow from (b) to (e) in Figure .…”

“…where || || 2 denotes the Euclidean distance. This measure is used in the evaluation of the optimizing algorithm by Elberfeld et al [Edd*18]. The problem with this measure is that the components of the vectors have different physical dimensions and are therefore not comparable.…”

Analysis and comparison of algorithms for the tomographic reconstruction of curved fibres

“…Konopczynski et al [8] show that deep neural networks can be used for detecting fibres as well. Elberfeld et al optimise the characteristics for each fibre by iteratively reconstructing directly to a fibre model [9]. Methods increasingly also start to get released in source code, such as the Insegt Fibre approach by Emerson et al [10], enabling better reproducibility.…”