Bintuan Wang

TC4 titanium alloy bolted structures are extensively utilized in aerospace engineering, particularly within the heat-affected zones of aircraft engines. However, current studies have predominantly focused on fatigue fracture of titanium alloys at temperatures exceeding 400 °C, leaving a gap in accurate fatigue life prediction for TC4 bolted structures subjected to moderate elevated temperatures up to 400 °C. To address this limitation, this study proposes an enhanced detail fatigue rating (DFR) method that is applicable to fatigue life prediction of TC4 bolted structures under thermal environments not exceeding 400 °C. Firstly, fatigue life data were acquired from base material specimens of TC4 titanium alloy tested at 20 °C, 200 °C, and 400 °C. Secondly, an enhanced DFR method that considered the temperature-dependent thermal influence was established based on the experimental results. The enhanced DFR approach was then applied to predict the fatigue life of double-shear TC4 bolted structures, and the results were compared with those obtained via the conventional DFR method. The findings demonstrate that the enhanced DFR method improves the average fatigue life estimation accuracy by 9.29% over the conventional DFR method within the 20~400 °C range. This establishes the proposed model as a highly promising tool for evaluating the fatigue performance of TC4 bolted structures under elevated thermal conditions below 400 °C.

Operational loads of an aircraft are the prerequisite for assessing its safety or fatigue life. Traditionally, numerous strain gauge sensors are installed to monitor the operational loads, which inevitably increase the weight and system complexity of the aircraft. Therefore, in order to decrease the maintenance costs and data redundancy, the number and location of strain sensors should be optimized for accurate and reliable operational load monitoring. In this paper, a novel two-stage strain gauge location optimization method is proposed to reduce the number of strain gauges while maintaining the operational load monitoring accuracy, which is validated by a numerical case study of an aircraft wing. In the first stage, the traditional Pearson correlation measure is harnessed to initially eliminate numerous correlated strain gauge monitoring points, reducing 996 original strain gauge measurement points to 13 for the aircraft wing box. In the second stage, an improved correlation measure method is proposed to further reduce the 13 strain gauge points to 2, which can evaluate the correlation degree of several variables and simultaneously determine the optimal strain monitoring locations for the two load actuators in this study. The relative errors between the predicted loads and the actual loads for both load actuators are less than 4% when only two optimized monitoring points are adopted. In addition, a comparison study with LASSO regression and principal component regression methods is conducted. The results demonstrate that the proposed method has the characteristics of less monitoring points and higher load prediction precision.