High-temperature random vibration fatigue is a critical cause of aerospace structural failure, while obtaining high-temperature fatigue curves (S-N curves) remains time-consuming and costly. Thus, a novel and engineering-oriented estimation approach is proposed to predict fatigue strength and high-cycle S-N curves of metallic materials over a wide temperature range. The method requires only room-temperature S-N curve data and limited tensile and yield strengths at multiple temperatures, to establish a direct quantitative relationship between mechanical property degradation and fatigue behavior. The approach was validated through literature data and high-temperature random vibration fatigue tests on TA15 titanium alloy. The results confirm its accuracy and generality, demonstrating that fatigue strength decreases non-linearly with temperature and is strongly correlated with mechanical properties. The predicted high-temperature S-N curves of TA15, applied to fatigue life prediction, showed good agreement with experimental data, confirming the method’s predictive reliability. Further investigations reveal that, under high-temperature random vibration, both stress and velocity response power spectral densities shift toward lower frequencies while maintaining their overall spectral shapes. The combined effects of temperature-dependent stiffness degradation, modal damping, and excitation spectrum distribution lead to a non-monotonic variation in fatigue life with temperature. A moderate temperature rise improves fatigue life owing to higher damping, whereas further heating reduces it as stiffness degradation dominates. This paper presents an efficient, experimentally validated framework for estimating temperature-dependent S-N curves that markedly reduces high-temperature fatigue testing costs. It provides theoretical and engineering guidance for fatigue design and durability assessment of aerospace structures under thermal-vibrational coupling conditions.
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