Variational and deep learning segmentation of very-low-contrast X-ray computed tomography images of carbon/epoxy woven composites

The purpose of this work is to find an effective image segmentation method for lab-based micro-tomography (mu-CT) data of carbon fiber reinforced polymers (CFRP) with insufficient contrast-to-noise ratio. The segmentation is the first step in creating a realistic geometry (based on mu-CT) for finite element modelling of textile composites on meso-scale. Noise in X-ray imaging data of carbon/polymer composites forms a challenge for this segmentation due to the very low X-ray contrast between fiber and polymer and unclear fiber gradients. To the best of our knowledge, segmentation of mu-CT images of carbon/polymer textile composites with low resolution data (voxel size close to the fiber diameter) remains poorly documented. In this paper, we propose and evaluate different approaches for solving the segmentation problem: variational on the one hand and deep-learning-based on the other. In the author's view, both strategies present a novel and reliable ground for the segmentation of mu-CT data of CFRP woven composites. The predictions of both approaches were evaluated against a manual segmentation of the volume, constituting our "ground truth", which provides quantitative data on the segmentation accuracy. The highest segmentation accuracy (about 4.7% in terms of voxel-wise Dice similarity) was achieved using the deep learning approach with U-Net neural network

Micro-Computed Tomography Image Based Thermo-Elastic Properties Studies of Freeze-Cast MMCs.

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Micro-computed tomography image based numerical elastic homogenization of MMCs

Properties of an interpenetrating metal–ceramic composite with freeze-cast preforms are investigated. For the estimation of elastic properties of the composite numerical homogenization approaches for 2D and 3D finite element models are implemented. The FE models are created based on micro-computed tomography (μCT) images. The results of the numerical 2D and 3D modeling coincide and are in good agreement with available experimental measurements of elastic properties.</jats:p

A computationally efficient multi-scale strategy for predicting the elasto-plastic behaviour of short fiber composites

Predicting the elasto-plastic response of short fiber reinforced polymers (SFRPs) is a challenging task due to the important effects of microstructural details (e.g. fiber interactions, orientations, volume fraction distribution, etc). The main goal of this study is to provide a straightforward framework for estimating the nonlinear response of SFRPs having complex microstructures using intrinsic physical properties of the matrix phase without using any reverse engineering. To do so, simplified 3D unit cells considering the effects of fiber interactions, are selected in order to predict the elasto-plastic response of SFRPs with aligned fibers (see Fig. 1). The effective mechanical responses of such 3D unit cells under different loading conditions are then used to calibrate the Hill plasticity model [1] to estimate anisotropic responses of SFRPs at microscopic levels. By coupling the obtained plasticity model with Pseudo-grain decomposition techniques [2, 3] as well as different orientation averaging approaches, the effects of fiber misalignments are taken into account. The numerical accuracy and computational efficiency of the employed unit cells are first studied by comparing the obtained results with those of multi-fiber RVEs with aligned fibers. Second, the validity and efficiency of the orientation averaging strategy are investigated using RVEs with randomly distributed fibers. The obtained results reveal that the proposed anisotropic Hill’s model calibrated with simple FEM unit cells largely reduces the number of required calibration tests and provides a computationally efficient framework to predict the nonlinear response of SFRPs while the effects of microstructural details are taken into account

A hierarchical multi-scale analytical approach for predicting the elastic behavior of short fiber reinforced polymers under triaxial and flexural loading conditions

This paper presents a computationally efficient multi-scale analytical framework for predicting the effective elastic response of short fiber reinforced polymers (SFRPs) under triaxial and flexural loading conditions where the details of microstructure such as core/shell thickness, volume fraction distribution, fiber misalignment and fiber length variation are objectively taken into account. To this end, the mean-field homogenization and finite element approaches are compared to calculate the elastic response of SFRPs at the microscopic level while the orientation averaging approach is used to address the effects of fiber misalignment. The obtained mechanical behavior is then linked to an enhanced laminate theory to predict the effective triaxial and bending macrostructural behavior considering the core/shell effects and variation of volume fraction through the thickness. Using the second-order homogenization technique, the numerical validation of the proposed analytical approach is investigated based on the micro- and meso-scale analyses. Furthermore, the potential of the proposed strategy is demonstrated for hybrid composites. Finally, the accuracy of the suggested model is thoroughly studied using the available experimental tests in literature where the statistical information about the details of SFRP microstructures is presented

Geometrical and deep learning approaches for instance segmentation of CFRP fiber bundles in textile composites

Segmenting micro-Computed Tomography (mu CT) images of textile composites is a necessary step before modeling the material at the mesoscale. However, the accurate segmentation of fiber bundles (or tows) remains a challenge in carbon fiber reinforced textile composites. Segmentation approaches based on local fiber orientation perform well in recognizing individual tows only under ideal conditions, namely when the local fiber orientation bordering two tows' interface is different, or when the touching area is small relative to the thickness of a tow. Unfortunately, in many textile composite laminates used in the industry, these ideal conditions are not found. Such materials often consist of multiple plies, where each fiber is aligned in one of the two orthogonal directions, and where the touching area between similar-orientation tows is often much larger than the tow thickness. Therefore, we propose two new methodologies for splitting tow instances. One is based on the geometrical analysis of the material structure using conventional image analysis; the other is based on the deep learning prediction of ideal inputs for segmentation based on the watershed transform. The deep learning-based method is trained using randomly generated synthetic images of a woven composite material, which avoids an expensive human annotation step

Correction: Sinchuk et al. Variational and Deep Learning Segmentation of Very-Low-Contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Composites. Materials 2020, 13, 936

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