Research on Fatigue Crack Growth Rate Prediction of 2024-T3 Aluminum Alloy Friction Stir Welded Joints Driven by Machine Learning
Abstract
Fatigue crack propagation in friction stir welded joints significantly affects aircraft structural integrity. This study investigates the influence of welding speed, rotational speed, specimen thickness, loading frequency, and stress ratio on the fatigue crack growth rate. Four classical machine learning models with different structures—Deep Back-Propagation Network, Random Forest, Support Vector Regression, and K-Nearest Neighbors—were employed to predict fatigue crack growth behavior. The results show that all models achieve strong predictive performance. For FSWed joints, Deep BP and KNN exhibit comparable performance (R2 > 0.98) on the training data, indicating similar learning capabilities with sufficient data coverage. Notably, KNN achieves the fastest training time (
Fields of Study & Subjects
Advanced Welding Techniques Analysis
Fatigue and fracture mechanics
Numerical methods in engineering
Publication details
- Journal / source
- Aerospace
- Publisher
- MDPI AG
- Volume
- 13
- Issue
- 2
- Pages
- 134–134
- Publication date
- 30 January 2026
- Language
- EN