Modeling of small carbon fiber-reinforced polymers for X-ray imaging simulation

Bliznakova, Kristina; Dermitzakis, Aris; Bliznakov, Zhivko; Kamarianakis, Zacharias; Buliev, Ivan; Pallikarakis, N.

doi:10.1177/0021998314550219

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…When investigating new GBI-PCCT acquisition and reconstruction procedures, computational x-ray simulations are a valuable tool for benchmarking and testing. Previous efforts have already been put into x-ray absorption simulations specifically designed for CFRP-samples [7], but using a ray-tracing approach becomes insufficient when adequate physics modelling is of interest, as is the case for GBI-PCCT. Furthermore, assessing the small-angle x-ray scattering caused by the internal fibre structure requires a much more detailed modelling of both the phase and absorption related interaction physics and the internal structure of the CFRP, taking into account individual fibres rather than just the larger fibre bundles.…”

Section: Introductionmentioning

confidence: 99%

Simulated grating-based x-ray phase contrast images of CFRP-like objects

Sanctorum¹,

Beenhouwer²,

Weissenböck³

et al. 2019

eJNDT

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Because of their microstructure, carbon fibre reinforced polymers outperform conventional materials in many aspects. These materials are, given the low x-ray absorption contrast they provide, ideally suited for phase contrast x-ray computed tomography. For system prototyping and for benchmarking and testing acquisition and reconstruction procedures, computer simulations are a valuable tool. In this paper, we propose the application of an alternative simulation strategy that unites Monte Carlo simulations with numerical wave optics and involves detailed modelling of the internal carbon fibre reinforced polymer structure down to the individual fibre scale.

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Section: Introductionmentioning

confidence: 99%

Simulated grating-based x-ray phase contrast images of CFRP-like objects

Sanctorum¹,

Beenhouwer²,

Weissenböck³

et al. 2019

eJNDT

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“…Matrecano [100] assessed the pore development in porous media utilizing the laboratorial X-Ray CT. Other notable works were conducted by Salvo et al [101] who implemented XCT on several materials including CFRPS and Bliznakova et al [102] who assessed the capabilities of the Computed Tomography on detecting pre-defined defects in curved and complex CFRP parts. Costin et al [103] explored the methods improvement through multiresolution XCT for the accurate detection of voids, thus for the increment of the effectiveness of the method for characterizing the material.…”

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

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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.

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Automated Detection of Delamination Defects in Composite Laminates from Ultrasonic Images Based on Object Detection Networks

Cheng,

Qi,

et al. 2024

J Nondestruct Eval

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Modeling of small carbon fiber-reinforced polymers for X-ray imaging simulation

Cited by 3 publications

References 29 publications

Simulated grating-based x-ray phase contrast images of CFRP-like objects

Simulated grating-based x-ray phase contrast images of CFRP-like objects

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

Automated Detection of Delamination Defects in Composite Laminates from Ultrasonic Images Based on Object Detection Networks

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