The 2xxx series aerospace aluminum alloys, vital for aircraft structures due to their excellent mechanical strength and corrosion resistance, face challenges with corrosion fatigue failure, impacting material integrity and aircraft safety. Accurately predicting the corrosion fatigue crack growth rate is crucial for these materials’ reliability in aerospace applications, ensuring safety and operational efficiency. Various unique incremental learning strategies like structural optimization, regularization, and replay were used to enhance the predictive model’s adaptability to continually updated data without the need to retrain the entire model, thus overcoming “catastrophic forgetting.” Based on these strategies, a series of XGBoost models for the prediction of corrosion fatigue crack growth rate in aerospace aluminum alloys have been developed. The results demonstrate that the replay strategy excels in maintaining the accuracy of historical data, while structural optimization and regularization strategies stand out in adapting to new data and effectively preventing overfitting. Combining these three strategies, the models maintain good accuracy and a low forgetting rate across all steps, with an accuracy rate exceeding 0.95 and a forgetting rate below 0.05 in all incremental steps, showcasing excellent knowledge retention capabilities. The models achieve a balanced performance in learning new tasks and retaining old knowledge.
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