A fiber-segmentation algorithm for composites imaged using X-ray microtomography: Development and validation

Creveling, Peter J.; Whitacre, William W.; Czabaj, Michael W.

doi:10.1016/j.compositesa.2019.105606

Cited by 27 publications

(10 citation statements)

References 33 publications

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“…Computerized tomography (CT) scans of the sheets of interest can provide high-quality detailed images of the paper microstructure before and after the compaction process and serve as a comparison to the numerically compacted sheet. Currently, the process of rigorously analyzing fiber geometry changes from CT images is an open research question [14,39,54,70]. Manual segmentation remains the best method of tracking fibers, but the cost associated with such segmentation is prohibitive.…”

Section: Resultsmentioning

confidence: 99%

Micro-mechanical modeling of the paper compaction process

et al. 2021

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Double-roll compaction is a process to create extensible paper and paperboard suitable for replacing plastic in 3D forming applications. Understanding the macro- and micro-mechanisms governing the compaction process allows increasing the stretch potential while maintaining sufficient strength and bending stiffness. In this work, we approach the compaction process of paperboard with micro-mechanical methods featuring the unprecedented level of details otherwise inaccessible with currently available experimental tools. The loading scheme is based on experiments and continuum level simulations. The different levels of compaction and their continuous impact on the fibers’ geometry, void closures, and irreversible deformation of the fibers are thoroughly characterized. We find that the structural changes are concentrated in the fibers oriented within 30 degrees of the direction of compaction. The deformation accumulates primarily in the wall of the fibers in the form of irreversible strains. The spring-back effect beyond the compaction is negligible. For the first time, the role of normal and frictional fiber-to-fiber interactions in the compaction process is investigated and quantified. The frictional interaction between the fibers has a surprisingly low impact on the outcome of the compaction process, and the normal interaction between the fibers has a dominant response. The consequence of this finding is potentially limited impact of the surface modifications targeting the friction.

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Section: Resultsmentioning

confidence: 99%

Micro-mechanical modeling of the paper compaction process

et al. 2021

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“…Researchers have been studying fiber-analysis detection for a while, using a variety of tools. Approaches include tracking, statistical methods, and classical image processing 32 – 39 . To the best of our knowledge, there are two different deep learning approaches applied to this problem: Yu et al .…”

Section: Discussionmentioning

confidence: 99%

A reusable neural network pipeline for unidirectional fiber segmentation

2022

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Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al. To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.

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“…The procedure of increasing the volume surrounding the segmented fibers has also been reported in the literature as a necessary step to obtain accurate fiber orientation. 24…”

Section: Methodsmentioning

confidence: 99%

“…The procedure of increasing the volume surrounding the segmented fibers has also been reported in the literature as a necessary step to obtain accurate fiber orientation. 24 When conducting the orientation quantification, there are several analysis modes to select from. The reference plane mode and the plane projection mode were explored using the two fiber sample.…”

Section: Alignment Measurementmentioning

confidence: 99%

Fiber orientation quantification utilizing X-ray micro-computed tomography

Sietins

Sun

Knorr

2020

Journal of Composite Materials

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It is well known that the mechanical performance of composite materials is highly dependent on the fiber orientation. Several techniques have historically been used to quantify fiber orientation experimentally. Newer methods have involved 3 D X-ray computed tomography (CT) scans due to the high resolution that is now achievable within a laboratory setting. The accuracy of the analysis, however, is a function of the resulting scan image quality and the specific parameters influencing the resulting orientation analysis. This report summarizes a methodology to quantify fiber orientation from 3 D CT scans. Optimal scanning parameters are presented taking into account both the necessary resolution, geometric unsharpness, and the scan volume size. The influence of varied software analysis parameters and their effects on the resulting orientation data is discussed. The selection of software analysis parameters was independently validated with optical microscopy on a sample with only two fibers. Lastly, the orientation analysis was applied to a 0/+45/−45/90 composite to demonstrate this technique on a larger scale.

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A fiber-segmentation algorithm for composites imaged using X-ray microtomography: Development and validation

Cited by 27 publications

References 33 publications

Micro-mechanical modeling of the paper compaction process

Micro-mechanical modeling of the paper compaction process

A reusable neural network pipeline for unidirectional fiber segmentation

Fiber orientation quantification utilizing X-ray micro-computed tomography

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