Journal of the Computational Structural Engineering Institute of Korea (한국전산구조공학회논문집)

Computational Structural Engineering Institute of Korea (한국전산구조공학회)
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Prediction of the Loading Characteristics by Neural Networks Using Structural Analysis of Composite Cylindrical Shells

복합재료 원통쉘의 구조해석을 이용한 신경회로망의 하중특성 추론에 관한 연구

  • 명창문 (정회원·국방과학연구소 중앙전산실) ;
  • 이영신 (충남대학교 기계설비공학과) ;
  • 서인석 (국가보안기술연구소 기반기술연구부)
  • Published : 2002.03.01
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Abstract

The predictions of the loading characteristics was performed by the neural networks which use the results through structural analysis. The momentum backperpagtion which can be modified the teaming rate and momentum coefficient, was developed. Input patterns of the neural networks are the 9 strains which positioned at the side of the shell and output layers is the loading characteristics. Hidden layers were increased from 1 layers to 3 layers. Developed program which were trained by 9 strains predict the loading characteristics under 0.5%. Inverse engineering can be applicable to the composite laminated cylindrical shells with developed neural networks.

본 연구에서는 복합재료 원통쉘의 정적 구조해석 결과를 신경회로망에 적용하여 원통쉘에 가해진 하중특성을 추론하였다. 신경회로망 알고리즘은 역전파 학습법의 학습율이 가변적으로 조정될 수 있도록 프로그램을 개발하였으며, 입력패턴은 원통쉘에 하중이 가해졌을 때, 원통쉘의 측면에서 발생하는 9지점의 변형률을 이용하였다 출력층은 가해진 하중특성으로 설정하였으며, 학습결과 원통쉘의 하중특성 추론 학습에 성공하였다. 은닉층의 층수를 1층에서 3층까지 학습결과를 비교분석하였으며, 하중특성은 0.5% 이내로 추론이 가능해졌다. 본 연구 결과 신경회로망을 이용한 복합재료 원통쉘의 역공학이 가능해졌다.

Keywords

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