Special Issue
  • Stochastic Strength Analysis according to Initial Void Defects in Composite Materials
  • Seung-Min Ji*, Sung-Wook Cho*, S.S. Cheon*†

  • * Department of Mechanical Engineering, Graduated School, Kongju National University

  • 복합재 초기 공극 결함에 따른 횡하중 강도 확률론적 분석
  • 지승민*· 조성욱*· 전성식*†

  • This article is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study quantitatively evaluated and investigated the changes in transverse tensile strength of unidirectional fiber-reinforced composites with initial void defects using a Representative Volume Element (RVE) model. After calculating the appropriate sample size based on margin of error and confidence level for initial void defects, a sample group of 5000 RVE models with initial void defects was generated. Dimensional reduction and density-based clustering analysis were conducted on the sample group to assess similarity, confirming and verifying that the sample group was unbiased. The validated sample analysis results were represented using a Weibull distribution, allowing them to be applied to the reliability analysis of composite structures.

본 연구는 Representative Volume Element(RVE) 모델을 사용하여 초기 공극 결함이 있는 단방향 섬유강화 복합재의 횡방향 인장 강도 변화에 대해 정량적 평가 및 조사되었다. 초기 공극 결함을 표본오차와 신뢰 수준을 기준으로 적정 표본의 수가 계산된 후, 총 5000개의 초기 공극 결함이 있는 RVE 모델이 표본 집단으로 생성되었다. 표본 집단은 차원 축소법과 밀도 기반 군집 분석을 통해 유사도 분석이 진행되었으며 편향되지 않은 표본 집단임이 확인 및 검증되었다. 검증된 표본 분석 결과는 복합재 구조의 신뢰성 해석에 적용될 수 있게 Weibull 분포로 표현되었다

Keywords: 대표 체적 요소(Representative volume element), 초기 공극 결함(Initial void defects), t-분포 확률적 임베딩(t-distributed stochastic neighbor embedding), 밀도 기반 군집화(Density-based spatial clustering of applications with noise)

This Article

Correspondence to

  • S.S. Cheon
  • Department of Mechanical Engineering, Graduated School, Kongju National University

  • E-mail: sscheon@kongju.ac.kr