Abstract

This paper presents crack growth analysis approach on the basis of neural networks, a branch of cognitive science to high temperature low cycle fatigue that shows strong nonlinearity in material behavior. As the number of data patterns on crack growth increase, pattern classification occurs well and two point representation scheme with gradient of crack growth curve simulates crack growth rate better than one point representation scheme. Optimal number of learning data exists and excessive number of learning data increases estimated mean error with remarkable learning time J-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).

Keywords

• Crack Growth Modelling;
• Neural Network;
• Modified J Integral;
• Learning Pattern;
• Generalization;
• Estimated Mean Error

• 균열성장 모델링;
• 신경회모랑;
• 수정 J 적분;
• 학습패턴;
• 일반화;
• 추정평균오차;