AP-GAN-DNN based creep fracture life prediction for 7050 aluminum

7050 is a high-strength aluminum alloy with high strength and excellent fracture toughness. However, Damage can be caused to the alloy by creep at elevated temperatures. The accuracy of conventional creep life prediction methods may fall short of meeting the desired standards. And the application of machine learning (ML) methods is limited due to the scarcity and dispersion of creep sample data. Generative Adversarial Networks (GAN) are often used for data enhancement, but suffer from “pattern collapse” under small sample conditions. To solve the above problems, this research proposes a method, which combines the affinity propagation (AP) clustering algorithm, GAN, and deep neural networks (DNN) for accurate creep fracture life prediction. The method utilizes the AP clustering algorithm for adaptive clustering to better reflect the creep sample distribution. Independent GAN models are trained for each cluster to better capture data distribution features and generate synthetic data that is highly similar to the real data. The DNN models are trained using the synthetic data and then predicted using real creep fracture life data. The method was compared with traditional physical and machine learning methods. The experimental results show that the method proposed in this paper has better prediction accuracy in a small sample creep fracture life dataset.