Data Processing of 2060-T8 Alloy Fatigue Test Results Using Statistical Methods to Improve Reliability and Accuracy

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.