Fatigue Strength Characterization of E-glass Fibers Using Fiber Bundle Test

Abstract

An experimental approach has been developed to characterize the fatigue strength distribution of E-glass fibers using fiber bundle test. In this approach, survival probability, P, of the fiber bundles was first calculated from the stress reduction observed in strain-controlled fatigue tests. Based on the fiber bundle model, Weibull distribution and fatigue P –" – N curves, the fatigue strength exponent b, Weibull scale parameter σ₀, and Weibull shape parameter β were then determined to characterize the fatigue strength distribution of E-glass fiber bundles. A numerical model, based on Monte-Carlo simulation, was also established to predict the load-controlled fatigue S–N diagram of E-glass fiber bundles. The simulated strain-cycle curves and stress-cycle curves based on the model developed were in good agreement with the test data.

Keywords

E-glass fiber fiber bundle fatigue statistical analysis Monte-Carlo simulation

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References

1. Andersons, J. , Joffe, R. , Hojo, M. and Ochiai, S. (2002). Glass Fibre Strength Distribution Determined by Common Experimental Methods, Composites Sci. and Tech., 62: 131–154.

2. Manders, P.W. and Chou, T.W. (1983). Variability of Carbon and Glass Fibers, and the Strength of Aligned Composites, J. Reinf. Plastics and Composites, 2: 43–59.

3. Chi, Z. , Chou, T.W. and Shen, G. (1984). Determination of Single Fibre Strength Distribution from Fibre Bundle Testings, J. Mater. Sci., 19: 3319–3324.

4. Coleman, B.D. (1958). Statistics and Time Dependence of Mechanical Breakdown in Fibers, J. Appl. Phys., 29: 968–983.

5. Kelly, A. and McCartney, L.N. (1981). Failure by Stress Corrosion of Bundles of Fibers, Proc. Royal Soc. of London, Series A, 374: 475–489.

6. Phoenix, S.L. (1978). Stochastic Strength and Fatigue of Fiber Bundles, Inter. J. Fracture, 14: 327–344.

7. Yuanming, X. , Jianming, Y. and Baochang, Y. (1994). A Statistical Model and Experimental Study of the Strain Rate Dependence of the Strength of Fibers, Composites Sci. and Tech., 52: 499–504.

8. Zhou, Y. , Jiang, D. and Xia, Y. (2001). Tensile Mechanical Behavior of T300 and M40J Fiber Bundles at Different Strain Rate, J. of Mater. Sci., 36: 919–922.

9. Goda, K. and Phoenix, S.L. (1994). Reliability Approach to the Tensile Strength of Unidirectional CFRP Composite by Monte Carlo Simulation in a Shear-lag Model, Composites Sci. and Tech., 50: 457–468.

10.

10. Lara-Curzio, E. and Russ, C.M. (1999). On the Relationship between the Parameters of the Distributions of Fiber Diameters, Breaking Loads, and Fiber Strengths, J. Mater. Sci. Lett., 18: 2041–2044.

11.

11. Stumpf, H. , Schwartz, P. , Lienkamp, M. and Schulte, K. (1995). S-glass/Kevlar-149 Hybrid Microcomposites in Stress-rupture: A Monte Carlo Simulation, Composites Sci. and Tech., 54: 211–221.

12.

12. Xia, Y. and Zhen, W. (1999). A Dynamic Monte Carlo Microscopic Damage Model Incorporating Thermo-mechanical Coupling in a Unidirectional Composite, Composites Sci. and Tech., 59: 947–955.

13.

13. Zhou, Y. , Huang, W. and Xia, Y. (2002). A Microscopic Dynamic Monte Carlo Simulation for Unidirectional Fiber Reinforced Metal Matrix Composites, Composites Sci. and Tech., 62: 1935–1946.

14.

14. MacDonald, F. and Coon, D. (2001). Monte Carlo Simulation of the Effect of Fiber Characteristics on Creep Life of a Fiber Tow, J. Mater. Sci., 36: 1681–1684.