Fatigue reliability analysis of bogie frames considering parameter uncertainty

With the increasing service life of bogie frames, the risk of fatigue failure becomes significant, making fatigue reliability analysis crucial for ensuring operational safety. However, accurately analyzing fatigue reliability presents a significant challenge with uncertain factors such as load fluctuations, unstable material shaping, and dimensional manufacturing deviations. To address this, this paper establishes a comprehensive active learning reliability framework based on surrogate models, enabling high-fidelity modeling and precise fatigue reliability analysis of welded frames under parameter uncertainty. The material utilization method was developed using APDL for secondary development to efficiently evaluate frame fatigue failure indicators. The effectiveness of this method was validated by combining the improved Goodman-Smith fatigue limit diagram and test bench fatigue tests, which helped identify the locations on the frame most prone to fatigue fractures. An Atom Search Optimization-BP Neural Network surrogate model was established with the objective of maximum material utilization, and the fatigue reliability of the bogie frame was obtained by combining the active learning function and the Monte Carlo method. The results show that the uncertainty design parameters greatly impact the fatigue reliability of critical welded structures. The proposed method improves the accuracy and efficiency of the fatigue reliability analysis of the bogie frame.