A New Nonlinear Fatigue Cumulative Damage Model Based on Enhanced Whale Optimization Algorithm and Manson–Halford Model

Abstract

In the field of modern mechanical engineering, structures often endure multi-level variable stress loading. The nonlinear fatigue cumulative damage process of these structures is highly complex due to the significant influence of loading sequences and interactions, which makes fatigue life prediction difficult. To accurately describe the impacts of these factors on fatigue damage, this paper proposes a nonlinear fatigue cumulative damage model (EWOA-MH) based on the enhanced whale optimization algorithm (EWOA) and the Manson–Halford (M-H) model. This model obtains weight factors through EWOA and incorporates them into the M-H model. Verified by experimental data of multi-level variable stress loading and calculated with a weighted method considering different materials' sample numbers, the prediction accuracy is increased by approximately 43%. Its application to the analysis of high-speed train bogie frames effectively demonstrates the model's effectiveness. The research shows that the EWOA-MH model performs outstandingly in fatigue life prediction and can effectively solve fatigue damage problems under multi-level variable stress loading conditions.