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A Study on Fatigue Crack Growth and Life Modeling using Backpropagation Neural Networks

역전파신경회로망을 이용한 피로균열성장과 수명 모델링에 관한 연구

Jo, Seok-Su;Ju, Won-Sik
조석수;주원식

  • Published : 2000.03.01

Abstract

Fatigue crack growth and life is estimated by various fracture mechanical parameters but affected by load, material and environment. Fatigue character of component without surface notch cannot be e valuated by above-mentioned parameters due to microstructure of in-service material. Single fracture mechanical parameter or nondestructive parameter cannot predict fatigue damage in arbitrary boundary condition but multiple fracture mechanical parameters or nondestructive parameters can Fatigue crack growth modelling with three point representation scheme uses this merit but has limit on real-time monitoring. Therefore, this study shows fatigue damage model using backpropagatior. neural networks on the basis of X-ray half breadth ratio B/$B_o$ fractal dimension $D_f$ and fracture mechanical parameters can predict fatigue crack growth rate da/dN and cycle ratioN/$N_f$ at the same time within engineering estimated mean error(5%).

Keywords

Fatigue Damage Modelling;Backpropagation Neural Networks;Fatigue Crack Rate;Cycle Ratio;Estimated Mean Error

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