Inverse determine the interfacial strength and surface geometry effects concerning carbon fiber reinforced epoxy composites by experiment aided <scp>FFT</scp>‐based spectral analysis

Wang, Bing; Wang, Zhangwen; Li, Menglei; Zhu, Jiaqi; Zhao, Yuan; Meng, Sheng

doi:10.1002/pc.27098

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…Clearly, deconvolution of the C1s spectra of M-CFF and M-SNWF reveals five characteristic peaks at 284.6, 285.7, 286.3, 287.2 and 288.8 eV, corresponding to C-C, C-N, C-O, C=O, and O-C=O groups, respectively. 39,40 These findings provide compelling evidence of the successful modification of the HFs surface with a multiscale hybridized modified layer, which is anticipated to further improving the wettability of EP for HFs, 41,42 consequently improving the interfacial properties between HFs and EP (Table S2).…”

Section: Surface Morphology Of the M-hfsmentioning

confidence: 75%

Effects of surface modification on mechanical properties and electromagnetic interference shielding properties of carbon fiber/silk fiber hybrid composites

Song,

He,

et al. 2024

Polymer Composites

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In this paper, a hybrid approach was employed by integrating a high‐modulus, high‐tensile‐strength carbon fiber fabric (CFF) with a low‐density, high‐toughness silk nonwoven fabric (SNWF) to fabricate CFF‐SNWF reinforced epoxy (CFF‐SNWF/EP) hybrid composites. Through the surface freeze‐induced self‐assembly process, the multiscale hybrid modified layers were successfully constructed on the surfaces of both CFF and SNWF. This novel approach effectively enhanced the interfacial strength of the hybrid fibers (HFs). Comparing the performance of the modified composites with unmodified CFF reinforced epoxy (CFF/EP), significant improvements in both tensile strength and interlaminar shear strength were observed. Specifically, the tensile strength and interlaminar shear strength of the modified composites reached 626.7 and 39.16 MPa, a rise scope of 17.1% and 14.8%, respectively. In addition, the hybrid fibers reinforced polymers (HFRPs) exhibited a superior electromagnetic interference (EMI) shielding capabilities. For insistence, the modified CFF/EP with a thickness of 2.0 mm showed an impressive EMI shielding effectiveness value of 50 dB. These findings underscore the promising application potential of CFF‐SNWF/EP hybrid composites, particularly in the field of consumer electronics.Highlights The surface of hybrid fiber was modified by PDA‐GO‐OCNTs‐WPU. Multi‐scale structure contributes a lot in composite performance. Hybrid effect before and after interfacial modification is performed. The hybrid interface modification and hybrid structure were analyzed.

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Section: Surface Morphology Of the M-hfsmentioning

confidence: 75%

Effects of surface modification on mechanical properties and electromagnetic interference shielding properties of carbon fiber/silk fiber hybrid composites

Song,

He,

et al. 2024

Polymer Composites

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Image driven deep learning method with FFT solver for predicting the microscale full field stress of stochastic boundary composites

Liao,

Wang,

Wang

et al. 2024

Polymer Composites

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A novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online.Highlights Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images. Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface. Incorporate adaptive algorithms for streamlined training optimization. An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online.

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Effects of microstructure characteristics on the transverse moisture diffusivity of unidirectional composite

Wang,

Hang

2023

Science and Engineering of Composite Materials

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To reveal the effects of the microstructure characteristics including fiber shape, void, fiber distribution pattern, and interphase on the transverse moisture diffusivities of unidirectional composites, the steady analysis method based on Fick’s law is adopted. The predicted numerical results are compared with the results from the analytical models to demonstrate the accuracy. From the simulation results, it is found that the increase in the oscillation amplification of non-circular fibers contributes to the orthotropy of diffusivity properties, which attributes to different barrier effects along different directions. The consideration of interphase relieves the barrier effects and the predicted diffusivity values are increased significantly. The effects of voids increase with the void volume fraction and are dependent on the voids’ location. If the fiber random distribution pattern is considered, it is found that the average values of predicted diffusivity decrease gradually with the increase in the number of oscillations.

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Inverse determine the interfacial strength and surface geometry effects concerning carbon fiber reinforced epoxy composites by experiment aided FFT‐based spectral analysis

Cited by 3 publications

References 60 publications

Effects of surface modification on mechanical properties and electromagnetic interference shielding properties of carbon fiber/silk fiber hybrid composites

Effects of surface modification on mechanical properties and electromagnetic interference shielding properties of carbon fiber/silk fiber hybrid composites

Image driven deep learning method with FFT solver for predicting the microscale full field stress of stochastic boundary composites

Effects of microstructure characteristics on the transverse moisture diffusivity of unidirectional composite

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