| XU Meng-yue 1 ， QI Hong-yu 1，2 ， LI Shao-lin 1，2 ， SHI Duo-qi 1，2 ， YANG Xiao-guang 1，2.Machine-Learning-Based Fatigue Life Prediction Method for Welded Joints[J].航空发动机,2025,51(1):96-102 |
| Machine-Learning-Based Fatigue Life Prediction Method for Welded Joints |
| DOI： |
| Key Words:machine learning random forest algorithm LightGBM (Light Gradient Boosting Machine) algorithm welded joints fatigue life geometry prediction model |
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| Abstract:Welded joints are characterized by non-uniform microstructure, gradient transitions in mechanical properties, and
randomly distributed welding defects, which are more prone to fatigue fracture than other structures. Therefore, studying the strength and
life of welded joints (especially under fatigue loading) has become a hot research topic in engineering and academia. A new study of a
fatigue life prediction model for welded joints based on a random forest model was carried out to study the fatigue behavior of welded joints.
To select a machine learning model with better prediction performance, the fatigue data set of welded joints was analyzed and predicted
using two different machine learning algorithm models, the Random Forest Model, and LightGBM. The random forest algorithm was used to
rank the importance of the input conditions to analyze the factors influencing the fatigue life of welded joints; The fatigue life results of the
model were calculated with different materials to verify the generalization ability of the machine learning model. The results show that the
machine learning model performs well in predicting the fatigue life of welded joints with different geometries and can be used to predict the
fatigue life of welded joints with different materials. The results are of great importance for the strength design of welded structures and the
optimization of welding process parameters. |
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