Structure recognition from high resolution images of ceramic composites

Abstract

Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness. Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (µ-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks and fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x faster than similar tools on a typical image file.

High temperature permeability of fibrous materials using direct simulation Monte Carlo

“…The plugin was used to set input parameters for the Octopus reconstruction software [17] that applied the classical filtered back-projection algorithm by Kak and Slaney [18] for reconstruction and a removal algorithm for ring artifacts [19]. A 3D bilateral filter, developed for tomography images [20], was finally applied to the reconstructed data to improve edge-detection and reduce…”

Fast detection of material deformation through structural dissimilarity

“…Specifically, we filter the data using our accelerated non-linear transformations in F3D [15], encompassing bilateral, median, and morphological operators in order to decrease noise and improve contrast. Next, we segment the ceramic composite using algorithms based on graphical models, such as Statistical Region Merging [6], and obtain a binary representation of the ceramic matrix that includes the fibers, as illustrated at the right of Fig.…”

“…The unstriped image stack carries prominent information about cracks, which can be detected through an iterative method based on the isodata algorithm [10], before the calculation of the crack-opening measurements. Other segmentation algorithms for micro-CT that may support different experimental data can be found in [13], [14], [15].…”

“…This paper refers to image stacks of 30GB [1], [15], with spatial resolution of 0.65μm, and under three different load (in newtons) conditions: 16.01N, 122.33N and 144.57N. Different phases of the material cannot be obtained through simple gray level cut-offs due to inherent artifacts, such as brightness variations across slices and noise, that may come from preceding image acquisition, reconstruction steps and/or sample composition.…”

Real-time data-intensive computing

“…A more recent capability CAMERA is developing is for fiber and crack analysis, which has been applied to ceramic matrix composites. 6,7 FIGURE 3. Screenshot of 3D web viewer; volume is rendered on a high performance computer, and the resulting image is passed to the web client.…”