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通过所提出的分析模型预测树脂传递模塑中多层纤维预制件在树脂浸渍过程中的缺陷形成

Prediction of Defect Formation during Resin Impregnation Process through a Multi-Layered Fiber Preform in Resin Transfer Molding by a Proposed Analytical Model.

作者信息

Seong Dong Gi, Kim Shino, Lee Doojin, Yi Jin Woo, Kim Sang Woo, Kim Seong Yun

机构信息

Department of Polymer Science and Engineering, Pusan National University, Busan 46241, Korea.

Aircraft Research and Development Division, Korea Aerospace Industries, Gyeongnam 52529, Korea.

出版信息

Materials (Basel). 2018 Oct 22;11(10):2055. doi: 10.3390/ma11102055.

DOI:10.3390/ma11102055
PMID:30360372
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6213041/
Abstract

It is very important to predict any defects occurring by undesired fiber deformations to improve production yields of resin transfer molding, which has been widely used for mass production of carbon fiber reinforced composite parts. In this study, a simple and efficient analytic scheme was proposed to predict deformations of a multi-layered fiber preform by comparing the forces applied to the preform in a mold of resin transfer molding. Friction coefficient of dry and wet states, permeability, and compressive behavior of unidirectional (UD) and plain woven (PW) carbon fabrics were measured, which were used to predict deformations of the multi-layered fiber preforms with changing their constitution ratios. The model predicted the occurrence, type, and position of fiber deformation, which agreed with the experimental results of the multi-layered preforms.

摘要

预测由于不期望的纤维变形而出现的任何缺陷对于提高树脂传递模塑的生产产量非常重要,树脂传递模塑已广泛用于碳纤维增强复合材料部件的大规模生产。在本研究中,提出了一种简单有效的分析方案,通过比较在树脂传递模塑模具中施加到预成型件上的力来预测多层纤维预成型件的变形。测量了单向(UD)和平纹编织(PW)碳纤维织物在干态和湿态下的摩擦系数、渗透率和压缩行为,这些用于预测多层纤维预成型件在其组成比例变化时的变形。该模型预测了纤维变形的发生、类型和位置,与多层预成型件的实验结果一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/2b5ba4394234/materials-11-02055-g011a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/9392011a3d7e/materials-11-02055-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/ae309968bd2e/materials-11-02055-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/2137eada8b0d/materials-11-02055-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/d22d0f5b5cf0/materials-11-02055-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/c06484087756/materials-11-02055-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/c6d72efe670e/materials-11-02055-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/26e309fc873e/materials-11-02055-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/3bd827796fff/materials-11-02055-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/d95af33793dc/materials-11-02055-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/29fe58d04b2e/materials-11-02055-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/2b5ba4394234/materials-11-02055-g011a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/9392011a3d7e/materials-11-02055-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/ae309968bd2e/materials-11-02055-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/2137eada8b0d/materials-11-02055-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/d22d0f5b5cf0/materials-11-02055-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/c06484087756/materials-11-02055-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/c6d72efe670e/materials-11-02055-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/26e309fc873e/materials-11-02055-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/3bd827796fff/materials-11-02055-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/d95af33793dc/materials-11-02055-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/29fe58d04b2e/materials-11-02055-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7566/6213041/2b5ba4394234/materials-11-02055-g011a.jpg

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