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

Abstract

7050 aluminum alloy has high strength and excellent fracture toughness. However, damage can be caused to the 7050 aluminum 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 paper proposes a creep fracture life prediction method, which combines the affinity propagation (AP) clustering algorithm, GAN, and deep neural networks (DNN) for accurate creep fracture life prediction. The AP clustering algorithm for adaptive clustering was utilized to better reflect the creep sample distribution. Independent GAN models were trained for each cluster to better capture distribution characteristics of the data and generate synthetic data that was highly similar to the real data. The DNN models were trained using the synthetic data and then predicted using real creep fracture life data. The presented method was compared with traditional physical and machine learning methods, and the method combining K-Means, GAN, and DNN. The experimental results show that the method proposed in this paper has better prediction accuracy in a small sample creep fracture life dataset.