Yuanbo Lv

Fatigue life testing is a crucial method for evaluating the fatigue performance of a material or part. However, the reliability and accuracy of the analysis methods applied to the data generated by such tests remain insufficient owing to their considerable scatter. This study accordingly conducted fatigue testing on fourth-generation 2060-T8 aluminum–lithium alloy specimens before and after low- and high-intensity cast steel shot peening. The resulting fatigue test data were evaluated for normality, processed using outlier identification, and subjected to hypothesis testing and comparative analysis. Finally, the fatigue reliability of each specimen group was calculated based on the significant differences between their observed fatigue lives. This study pioneers the systematic integration of normality testing, outlier identification, and hypothesis testing, constructing a multi-tiered analytical framework specifically tailored for shot peening effect evaluation. This approach fundamentally overcomes the constraints of traditional methodologies characterized by their sole reliance on mean fatigue life analysis. The determined effects of shot peening on the specimen fatigue life and data dispersion indicated that analysis using statistical methods can effectively improve the reliability of fatigue test results and support the selection of materials for use in engineering structures.

The angular motion of aircraft landing gear retraction and extension must be accurate to ensure flight safety. Therefore, this study experimentally evaluated the motion accuracy of the landing gear retraction and extension processes associated with a specific aircraft to construct a reliability evaluation model for the landing gear angle. Considering the limitations of data acquisition in practical applications, the Bayesian method, which combines prior knowledge with experimentally measured data to reasonably estimate the variable parameters in the evaluation model, was applied to obtain more accurate parameter distributions. The constructed Bayesian-updated iterative model was shown to effectively expand upon limited test data to provide a novel approach for accurately evaluating landing gear angle reliability. The results of this study not only enrich the theoretical basis underpinning aircraft landing gear reliability assessment but also provide a valuable reference for technical support and decision-making in related engineering practice.