Jing Yang1, Zhiming Liu1,*, Xingchao Li2, Zhongyao Wang3, Beitong Li1, Kaiyang Liu1, Wang Long4
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068353
- 10 November 2025
Abstract Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service. However, due to the complexity of fatigue failure mechanisms, achieving accurate multiaxial fatigue life predictions remains challenging. Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions, making it difficult to maintain reliable life prediction results beyond these constraints. This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life, using Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Fully Connected Neural… More >