Study of Fatigue Damage Accumulation and Fatigue Reliability Based on Rotating Bending Test Data

Author(s):  
Wen-Fang Wu ◽  
Tseng-Ti Fu ◽  
Chia-Han Yang
2018 ◽  
Author(s):  
DC Pham

A new 3D damage model is developed to predict the progressive failure and accumulated fatigue damage of woven fabric composite materials. Stress-based failure criteria are used to predict the damage initiation in x-tow, y-tow, and matrix constituent. An S-N based damage accumulation model is implemented to characterize the cycle dependent strength of the x- and y- fiber tows and matrix subjected to axial tension, compression, or in-plane share loading. A curve-fit non-linear shear model is also employed based on the static coupon test data of (+45/-45) woven fabric laminates. A static failure progression module is used to predict the damage and failure at the peak load prior to fatigue cycling. Stiffness degradation, fatigue damage accumulation, and failure mode detection are performed during the fatigue marching process. The developed user-defined material model for Abaqus features: 1) description of initial nonlinear shear before the damage initiation; 2) characterization of failure initiation based on a maximum stress criterion; and 3) performance of fatigue damage accumulation using a phenomenological model based on S-N test data. The predictive capabilities of the developed model are demonstrated using tension-tension fatigue of SYNCOGLAS R420 E-glass woven fabrics.


2004 ◽  
Vol 46 (6) ◽  
pp. 309-313
Author(s):  
Yutaka Iino ◽  
Hideo Yano

2013 ◽  
Vol 81 (4) ◽  
Author(s):  
Son Hai Nguyen ◽  
Mike Falco ◽  
Ming Liu ◽  
David Chelidze

Estimating and tracking crack growth dynamics is essential for fatigue failure prediction. A new experimental system—coupling structural and crack growth dynamics—was used to show fatigue damage accumulation is different under chaotic (i.e., deterministic) and stochastic (i.e., random) loading, even when both excitations possess the same spectral and statistical signatures. Furthermore, the conventional rain-flow counting method considerably overestimates damage in case of chaotic forcing. Important nonlinear loading characteristics, which can explain the observed discrepancies, are identified and suggested to be included as loading parameters in new macroscopic fatigue models.


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