Abstract
The multi-parameter and nonlinear characteristics of the Smith Watson Topper (SWT) equation present considerable challenges for predicting the fatigue life of 2024-T3 clad Al alloy. To overcome these challenges, a novel model integrating traditional fatigue analysis methods with machine learning algorithms is introduced. An improved SWT fatigue life prediction equation is developed by incorporating key factors such as the mean stress effect, stress concentration factor, and surface roughness coefficient. Extreme gradient boosting, Random Forest, and their derived models are used to construct the fatigue life prediction model. The L-BFGS algorithm was then integrated with the established machine learning model to solve for the multi-parameter of the improved SWT equation. Thus, an accurate modified SWT prediction equation for 2024-T3 clad Al alloy was obtained. To further optimize the solution, the deep deterministic policy gradient and deep reinforcement learning algorithms are introduced to dynamically optimize the nonlinear equation, achieving a more efficient and accurate solution. The improved SWT fatigue life prediction equation and its solution method proposed in this study provide new insights for fatigue life prediction of clad metallic materials.