Visual classification of braided and woven fiber bundles in X-ray computed tomography scanned carbon fiber reinforced polymer specimens

Weissenböck, Johannes; Bhattacharya, Arindam; Plank, Bernhard; Heinzl, Christoph; Kastner, Johann

doi:10.1016/j.csndt.2016.05.006

Cited by 5 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each textile architecture, samples of 15 × 15 × 3 mm 3 were cut from the resin-infused plates in close proximity to the coupons used for the mechanical tests and scanned with a GE Phoenix/X-Ray Nanotom 180 at a voxel size of 9 µm 3 at the University of Applied Sciences Upper Austria. Since an automatic segmentation of resin-infused bundles poses significant challenges [39], a semi-automatic segmentation strategy was developed. Initially, the raw CT data is imported into a Matlab script and sliced using three planes, with each of them corresponding to a cut orthogonal to one of the in-plane fibre directions.…”

Section: Internal Geometrymentioning

confidence: 99%

A meso-scale simulation framework for predicting the mechanical response of triaxial braided composites

Wehrkamp-Richter

Carvalho

Pinho

2018

Composites Part A: Applied Science and Manufacturing

View full text Add to dashboard Cite

Section: Internal Geometrymentioning

confidence: 99%

A meso-scale simulation framework for predicting the mechanical response of triaxial braided composites

Wehrkamp-Richter

Carvalho

Pinho

2018

Composites Part A: Applied Science and Manufacturing

View full text Add to dashboard Cite

“…A number of studies have been carried out using micro computed tomography (CT) scans of composites to explore the morphology of fibers along the fiber direction. [23][24][25][26][27][28][29] These works have shown that the spatial variability in fiber positions is subject to change from cross-section to cross-section due to the degree of misalignment of the fibers. The degree of fiber misalignment and its effects on strength have also been studied extensively.…”

Section: Introductionmentioning

confidence: 99%

Identification and Quantification of 3D Fiber Clusters in Fiber-Reinforced Composite Materials

Schey

Beke

Appel

et al. 2021

JOM

View full text Add to dashboard Cite

Microscale computed tomography scans of fiber-reinforced composites reveal that fibers are most often not strictly parallel to each other but exhibit varying degrees of misalignment and entanglement. One characteristic of this entanglement is the degree to which fibers stay together as clusters. In this study, a method for identifying and isolating fiber clusters was established, and scans of two different composite microstructures were analyzed. To identify clusters, fiber center points of the first cross-section were triangulated, and the variation of the perimeter and area of triangles along the fiber direction was used to identify fiber triads which stay together. A filtering process eliminated fiber triads not part of a larger cluster. Geometric properties of the clusters such as cluster orientation, radius of gyration, cluster density, and volume fraction were calculated and compared. The metrics revealed fundamental differences between the two samples, suggesting that clusters have origins in manufacturing.

show abstract

“…For example, in [104] novel graphical tools are described which can performed pores' cluster segmentation of even characterize the fiber orientation in complex CFRP woven geometries (Figure 1.9). Finally, the X-Ray CT is also used for observing the evolution of damage in composite materials [105][106][107][108] subjected to fatigue loads or for measuring accurately the fracture toughness via in-situ CT measurements [109][110] Even though the researchers' attention was gained by the XCT, there are some restrictions of this NDT method categorized into three main sectors, namely the objects/samples dimensions, the equipment cost and the accuracy of the method. The first and last parameters are highly related as seen in [98,100].…”

Section: Characterization Of Pores In Fibre-reinforced Polymers Utilizing X-ray Ctmentioning

confidence: 99%

A numerical methodology for predicting the mechanical properties of unidirectional composite laminates with pores

Stamopoulos¹

View full text Add to dashboard Cite

In the present thesis, a numerical methodology was developed to predict the mechanical properties of unidirectional carbon fiber reinforce polymers (CFRPs) using data from NDT tests. To achieve the goal of the thesis, X-Ray CT scan data analysis was conducted, optical microscopy was utilized, multi-scale FE models were created, mechanical tests were conducted and Artificial Neural Networks were trained by utilizing the developed FE models.Originally, UD CFRP plates with different pore content were manufactured by implementing a variety of autoclave pressures. The pressure values were the optimum, most often implemented and lowest respectively. To this end, four different types of plates of the same material were manufactured. The pores developed in the samples cut from these plates was characterized by means of X-Ray computed tomography. Prior to the final porosity quantification, a parametrical study was conducted for defining the most efficient parameters set on the software’s defect detection module. In parallel, optical microscopy was utilized for defining the large void’s dimensions in the sample with the highest pore content. By comparing the measurements obtained by the optical microscope and the XCT, the defect detection parameters were properly calibrated and validated. Small pores and micro-pores were also attributed to the analysis. At the end of this framework, porosity developed in all four samples was characterized.Subsequently, a numerical methodology was developed which employs the pore’s characteristics. Due to the pore’s dimension high ratio between the smallest and bigger observed, the methodology was designed in three distinct levels, namely the epoxy resin with small pores, porous epoxy resin with large voids and CFRP specimens. The output of each level was the input of the consecutive. The methodology was implemented in four porosity levels’ pores characteristics resulting the mechanical properties of the CFRP laminate, namely the transverse strength and stiffness, short beam strength, flexural strength and modulus. The results in terms of mechanical behavior were validated against mechanical tests. All the matrix dominated properties appeared to be highly affected by the pores and voids leading to a degradation up to 14% of the interlaminar shear strength and roughly 20% of the in-plane shear properties.After having validated the numerical methodology, an Artificial Neural Network was developed to link the pore’s characteristics defined by means of XCT and the mechanical properties of the porous UD CFRP laminate. To this end, a number of 30 different autoclave pressure scenarios were created excluding the 2 out of 4 actual cases, thus 30 pores dataset. After simulating the corresponding mechanical tests with the use of the numerical methodology developed previously, the ANN was trained utilizing the 30 input and output data. The two excluded cases which correspond to the most often implemented autoclave pressures, validated the accuracy of the developed ANN.The main conclusion of the thesis is that the developed numerical methodology can accurately predict the mechanical properties of porous CFRP laminates. Additionally, the ANN based on this methodology is equally accurate and capable of predicting the degraded mechanical properties of the CFRP material using a pore’s characteristics dataset in a direct, effective and fast manner. Consequently, both two methods –after minor modifications- may be used as tools for the scopes of designing and optimizing materials saving cost and increasing the design’s effectiveness.

show abstract

Visual classification of braided and woven fiber bundles in X-ray computed tomography scanned carbon fiber reinforced polymer specimens

Cited by 5 publications

References 3 publications

A meso-scale simulation framework for predicting the mechanical response of triaxial braided composites

A meso-scale simulation framework for predicting the mechanical response of triaxial braided composites

Identification and Quantification of 3D Fiber Clusters in Fiber-Reinforced Composite Materials

A numerical methodology for predicting the mechanical properties of unidirectional composite laminates with pores

Contact Info

Product

Resources

About