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J. Ocean Eng. Technol. 1996;10(1):65-74.    

A study on fatigue crack growth modelling by back propagation neural networks
역전파 신경회로망을 이용한 피로 균열성장 모델링에 관한 연구
주원식,조석수
동아대학교 기계공학과,동아대학교 기계공학과
Copyright © 1996 The Korean Society of Ocean Engineers     Open access / Under a Creative Commons License
Key Words: Fatigue crack growth modelling, Back propagation neural networks, Function mapping, Modified J integral, Learning, Generalization
핵심용어: 피로균열성장 모델링, 역전파 신경회로망, 함수사상, 수정 J 적분, 학습, 일반화
Abstract
Up to now, the existing crack growth modelling has used a mathematical approximation but an assumed function have a great influence on this method. Especially, crack growth behavior that shows very strong nonlinearity needed complicated function which has difficulty in setting parameter of it. The main characteristics of neural network modelling to engineering field are simple calculations and absence of assumed function. In this paper, after discussing learning and generalization of neural networks, we performed crack growth modelling on the basis of above learning algorithms. J'-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).


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