Fatigue Life Prediction of Aluminum Alloy Welded Joints Based on CDEGWO-SVR

To address the uncertainty in fatigue life prediction of welded joints under small-sample conditions, this study proposes a prediction model based on support vector regression (SVR) enhanced by an improved Grey Wolf Optimizer (GWO). First, a CDE-GWO algorithm is developed by optimizing the convergence factor and integrating differential evolution (DE) to enhance population search ability; its effectiveness is verified via benchmark functions. Subsequently, a CDEGWO-SVR model is constructed and validated against SVR, GWO-SVR, DE-SVR, and DEGWO-SVR using UCI datasets, demonstrating superior fitting accuracy and lower error. Finally, the model is applied to aluminum welded joint fatigue data. Comparative analysis with radial basis function (RBF) neural networks and least squares S-N curve fitting across five evaluation metrics indicates that the proposed model achieves better performance in MSE, MAPE, R2, and CC, with competitive RSD. Experimental results confirm that the CDEGWO-SVR model possesses stable and higher prediction precision, offering an effective solution for fatigue life prediction involving small samples and multiple uncertainty factors.