Machine Learning‐Based Strength Prediction for Two‐Stage Aged 7050 Aluminum Alloy Forgings in Aircraft Main Support Joints

Aluminum alloys, widely regarded as lightweight structural materials, are extensively used in the aerospace industry. The aging process is essential for reducing residual stresses and ensuring alloys quality. Traditional methods for optimizing aging are often time‐consuming and expensive. In contrast, machine learning (ML) accelerates material design and performance prediction, significantly minimizing the need for extensive experimentation. In this study, the 7050 aluminum alloy forgings in aircraft main support joints are selected as the research object. A forward prediction model is developed using common ML algorithms, incorporating two‐stage aging process parameters and microstructural features as inputs, with yield strength (YS) and ultimate tensile strength (UTS) as outputs. The results demonstrate that the extreme gradient boosting regression model is the most effective for predicting the strength of aluminum alloys, with R2 values exceeding 0.7. By the Shapley additive explanation (SHAP) method and microscopic morphology analysis, the second‐stage aging time (t2) significantly influences YS and UTS. Hence, t2 was selected as the output for constructing the reverse classification model. The support vector machine classification model exhibits optimal performance, attaining macro‐accuracy and macro‐recall rates of 0.91 and 0.90, respectively. This study utilizes machine learning (ML) algorithms to predict the strength of 7050 aluminum alloys in aircraft main support joints by integrating two‐stage aging process parameters and microstructure, establishing a forward regression model by the extreme gradient boosting algorithm. Based on the aging parameters, a reverse classification model is developed using the support vector machine algorithm.