Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology-Reference-Cited by-同舟云学术

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Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Published:2021-01-20 Issue:4 Volume:32 Page:1853-1865
ISSN:1042-7147
Container-title:Polymers for Advanced Technologies
language:en
Short-container-title:Polym Adv Technol

Author:

Luo Ling¹^ORCID,Zhang Boming¹,Lei Yongpeng²,Zhang Guowei¹,Zhang Zhenchong³,Meng Bangke⁴,Liu Zhen⁴

Affiliation:

1. School of Materials Science and Engineering Beihang University Beijing China

2. Key Laboratory of Advanced Ship Materials and Mechanics, College of Aerospace and Civil Engineering Harbin Engineering University Harbin China

3. Lightweight department Beijing New Energy Automotive Technology Innovation Center Co., Ltd. Beijing China

4. Technology department Bjcomposites Co., Ltd. Beijing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Polymers and Plastics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pat.5226

Reference36 articles.

1. The influence of porosity on the interlaminar shear strength of carbon/epoxy and carbon/bismaleimide fabric laminates

2. Influence of voids on interlaminar shear strength of carbon/epoxy fabric laminates

3. Effect of curing cycle on void distribution and interlaminar shear strength in polymer-matrix composites

4. Evaluating vibration assisted vacuum infusion processing of hexagonal boron nitride sheet modified carbon fabric/epoxy composites in terms of interlaminar shear strength and void content

5. Evaluating random vibration assisted vacuum processing of carbon/epoxy composites in terms of interlaminar shear strength and porosity

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1. Annotator bias and its effect on deep learning segmentation of uncured composite micrographs;NDT & E International;2024-06

2. Deep-learning versus greyscale segmentation of voids in X-ray computed tomography images of filament-wound composites;Composites Part A: Applied Science and Manufacturing;2024-02

3. Solid epoxy prepregs with patterned resin distribution: Influence of pattern and process parameters on part quality in vacuum‐bag‐only processing;Polymer Composites;2023-08-31

4. Optimal design of thin-layered composites for type IV vessels: Finite element analysis enhanced by ANN;Thin-Walled Structures;2023-06

5. The effect of convolutional neural network architectures on phase segmentation of composite material X-ray micrographs;Journal of Composite Materials;2023-05-31

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