Stress-Dependent Dispersion of Corrosion–Fatigue Life in Aa 7075-T651 Under Multiaxial Loading: A Weibull-Based Analysis

Corrosion–fatigue of high-strength aluminium alloys is governed not only by reductions in mean fatigue life but, more critically, by systematic changes in fatigue-life dispersion that directly affect structural reliability. In this study, the corrosion–fatigue behaviour of AA 7075-T651 under multiaxial loading is analysed from a dispersion-centred statistical perspective using a Weibull-based framework to quantify how variability evolves with stress level, corrosion exposure, and loading mode. Fatigue data obtained under uncorroded conditions and after 7-day and 14-day NaCl exposure were analysed for bending and torsional loading. Stress-level binning was employed to ensure statistically robust parameter estimation without extrapolation beyond the experimentally tested domain. Rather than treating scatter as experimental noise, the Weibull shape parameter is explicitly interpreted as a stress- and environment-dependent descriptor of fatigue-life variability. The results show that, under uncorroded conditions, fatigue-life dispersion increases significantly at lower stress levels for both bending and torsional loading, consistent with a transition toward initiation-controlled failure. Corrosion exposure produces a pronounced and persistent reduction in the Weibull shape parameter, indicating a fundamental broadening of the fatigue-life distribution that intensifies with increasing exposure duration. Loading mode is further shown to modulate this behaviour, with torsional loading exhibiting distinct dispersion characteristics relative to bending, particularly in corrosive environments. These findings demonstrate that fatigue-life dispersion in AA 7075-T651 cannot be characterised using stress-independent statistical parameters or inferred reliably from mean-life trends alone. Instead, dispersion evolves systematically with stress level, environmental degradation, and loading mode, necessitating stress- and environment-dependent probabilistic descriptions. By explicitly quantifying these effects, the present study provides a statistical basis for advancing corrosion–fatigue assessment beyond conventional deterministic and mean-life-based approaches toward reliability-oriented evaluation under multiaxial loading.

Corrosion–fatigue, Weibull distribution, fatigue-life dispersion, multiaxial loading, AA 7075-T651

1. A. Karolczuk, K. Kluger, and T. Palin-Luc, “Fatigue failure probability estimation of the 7075-T651 aluminum alloy under multiaxial loading based on the life-dependent material parameters concept,” International Journal of Fatigue, vol. 147, 2021, Art. no. 106174, doi: 10.1016/j.ijfatigue.2021.106174. [Google Scholar] [Crossref]

2. Q. Wang et al., “Predicting the fatigue survival probability of high-strength steel wires: A Weibull model incorporating corrosion and stress ratios,” Engineering Failure Analysis, 2024, doi: 10.1016/j.engfailanal.2024.109063. [Google Scholar] [Crossref]

3. P. Strzelecki, “Determination of fatigue life for low probability of failure for different stress levels using 3-parameter Weibull distribution,” International Journal of Fatigue, vol. 145, 2021, Art. no. 106080, doi: 10.1016/j.ijfatigue.2020.106080. [Google Scholar] [Crossref]

4. J. Wang et al., “Probabilistic fatigue life framework of notched specimens based on the Weibull distribution under multiaxial loading,” Acta Mechanica Solida Sinica, vol. 37, pp. 673–684, 2024, doi: 10.1007/s10338-024-00472-w. [Google Scholar] [Crossref]

5. Q. Wu et al., “Probabilistic fatigue life prediction of notched specimens based on modified stress field intensity method under multiaxial loading,” International Journal of Pressure Vessels and Piping, 2024, doi: 10.1016/j.ijpvp.2024.105258. [Google Scholar] [Crossref]

6. H. Fakoor and A. Kaklar, “A modification in Weibull parameters to achieve a more accurate probability distribution function in fatigue applications,” Scientific Reports, vol. 13, 2023, doi: 10.1038/s41598-023-44907-9. [Google Scholar] [Crossref]

7. Y. XuanRui et al., “A data-driven model for estimating the fatigue life of 7075-T651 aluminum alloy based on the updated BP model,” Journal of Materials Research and Technology, 2023, doi: 10.1016/j.jmrt.2023.02.194. [Google Scholar] [Crossref]

8. A. Karolczuk and M. Słoński, “Application of the Gaussian process for fatigue life prediction under multiaxial loading,” Mechanical Systems and Signal Processing, 2022, doi: 10.1016/j.ymssp.2021.108599. [Google Scholar] [Crossref]

9. E. Akbari et al., “Physics-informed neural networks for multiaxial fatigue life prediction of aluminum alloy,” Computer Modeling in Engineering & Sciences, 2025, doi: 10.32604/cmes.2025.068581. [Google Scholar] [Crossref]

10. J. Halamka et al., “Using hybrid physics-informed neural networks to predict lifetime under multiaxial fatigue loading,” Engineering Fracture Mechanics, 2023, doi: 10.1016/j.engfracmech.2023.109351. [Google Scholar] [Crossref]

11. K. O. Iwuji and O. O. Ighodaro, “Impact of corrosion on the mechanical behavior and fatigue life of 7075-T651 aluminium alloy under multiaxial loading conditions,” Journal of Energy Technology and Environment, vol. 7, no. 3, pp. 161–180, 2025. [Google Scholar] [Crossref]

12. W. Weibull, “A statistical distribution function of wide applicability,” Journal of Applied Mechanics, vol. 18, no. 3, pp. 293–297, 1951. [Google Scholar] [Crossref]

13. W. Nelson, Applied Life Data Analysis. New York, NY, USA: Wiley, 1982. [Google Scholar] [Crossref]

14. E. Castillo and A. Fernández-Canteli, A Unified Statistical Methodology for Modeling Fatigue Damage. Dordrecht, The Netherlands: Springer, 2009. [Google Scholar] [Crossref]

15. J. Schijve, Fatigue of Structures and Materials, 2nd ed. Dordrecht, The Netherlands: Springer, 2009. [Google Scholar] [Crossref]

16. C. M. Sonsino, “Course of SN-curves especially in the high-cycle fatigue regime with regard to component design and safety,” International Journal of Fatigue, vol. 29, no. 12, pp. 2246–2258, 2007. [Google Scholar] [Crossref]

17. R. Branco et al., “Load sequence effects and cyclic deformation behaviour of 7075-T651 aluminium alloy,” International Journal of Fatigue, 2021, doi: 10.1016/j.ijfatigue.2021.106593. [Google Scholar] [Crossref]

18. E. Ferreira et al., “Fatigue life estimation model for plain concrete under uniaxial stress reversal loading,” Fatigue & Fracture of Engineering Materials & Structures, 2025, doi: 10.1111/ffe.70093. [Google Scholar] [Crossref]

19. B. Nečemer et al., “Fatigue behaviour of re-entrant auxetic structures made of the aluminium alloy AA7075-T651,” Thin-Walled Structures, 2022, doi: 10.1016/j.tws.2022.109917. [Google Scholar] [Crossref]

20. Z. Jie et al., “Effect of stress ratios on corrosion fatigue life of high-strength steel wires,” Fatigue & Fracture of Engineering Materials & Structures, 2021, doi: 10.1111/ffe.13620. [Google Scholar] [Crossref]

21. A. Turnbull, “Corrosion fatigue crack initiation: Implications of variability and scatter,” Corrosion Engineering, Science and Technology, vol. 47, no. 4, pp. 276–284, 2012. [Google Scholar] [Crossref]

22. R. P. Gangloff, “Fatigue crack initiation and propagation under environmental influence,” Comprehensive Structural Integrity, vol. 4, pp. 31–101, 2003. [Google Scholar] [Crossref]

23. B. Wang et al., “A multiaxial fatigue life prediction approach accounting for additional strengthening effect based on energy-critical plane model,” Materials, vol. 18, 2025, doi: 10.3390/ma18174089. [Google Scholar] [Crossref]

24. J. Liu et al., “Multiaxial notch fatigue probability modeling based on three-parameter Weibull distribution and effective strain energy density,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 45, 2023, doi: 10.1007/s40430-023-04312-9. [Google Scholar] [Crossref]

25. R. E. Melchers, “Structural reliability analysis and prediction of corrosion effects,” Structural Safety, vol. 42, pp. 3–13, 2013. [Google Scholar] [Crossref]

26. E. Poursaeidi et al., “Statistical investigation of corrosion fatigue life using Weibull distribution,” Engineering Failure Analysis, vol. 41, pp. 34–42, 2014. [Google Scholar] [Crossref]

27. Y. Zhang et al., “Reliability assessment of corrosion-fatigue life based on Weibull statistics,” International Journal of Fatigue, vol. 125, pp. 1–10, 2019. [Google Scholar] [Crossref]

28. R. P. Gangloff, “Environmental effects on fatigue,” in Comprehensive Structural Integrity, vol. 4, 2003. [Google Scholar] [Crossref]

29. [29] E. Castillo and A. Fernández-Canteli, “Fatigue life variability and reliability modeling,” Springer, 2009. [Google Scholar] [Crossref]

30. A. Karolczuk and E. Macha, “A review of critical plane approaches for multiaxial fatigue failure,” International Journal of Fatigue, vol. 27, pp. 1029–1041, 2005. [Google Scholar] [Crossref]

31. J. Papuga, “A survey on evaluating the fatigue limit under multiaxial loading,” International Journal of Fatigue, vol. 33, no. 2, pp. 153–165, 2011. [Google Scholar] [Crossref]

32. G. Sines, “Behavior of metals under complex static and alternating stresses,” in Metal Fatigue. New York, NY, USA: McGraw-Hill, 1959, pp. 145–169. [Google Scholar] [Crossref]

33. E. Castillo and A. Fernández-Canteli, A Unified Statistical Methodology for Modeling Fatigue Damage. Dordrecht, The Netherlands: Springer, 2009. [Google Scholar] [Crossref]

34. J. Schijve, Fatigue of Structures and Materials, 2nd ed. Dordrecht, The Netherlands: Springer, 2009. [Google Scholar] [Crossref]

• Investigating the Performance of Colour Oxide on the PS/CNSO Oil Paint Production
• Real-Time Fabric Defect Detection Using a Lightweight Deformable YOLO Network
• Recent Trends in the Stock Market: An Analytical Study of Market Dynamics, Investor Behaviour, and Technological Influence
• Evaluation of Structural Dynamics and Equilibrium State, with Case Study of Large Cantilever Projection for a 10- Storey Reinforced Concrete Building in Lagos, Nigeria.
• Multi-Criteria Evaluation of AI-Based Adaptive Learning Platforms in Global Higher Education: A Fuzzy AHP Perspective