Results 311 to 320 of about 8,667,132 (350)

Fatigue Life Prediction of Complex Structures

Journal of Mechanical Design, 1978
Because of the complex nature of the fatigue process, it is only recently that reasonably effective analysis procedures for predicting finite-fatigue life for simple notched coupons have evolved. One of the more vexing problems in adapting these procedures to making life predictions for complex components and structures is that of the multiplicity of ...
openaire   +1 more source

Fatigue-life prediction of additively manufactured metals by continuous damage mechanics (CDM)-informed machine learning with sensitive features

International Journal of Fatigue, 2022
Haijie Wang, Bo Li, Fuzhen Xuan
semanticscholar   +1 more source

A new cyclical Generative Adversarial Network based data augmentation method for multiaxial fatigue life prediction

International Journal of Fatigue, 2022
Xingyue Sun, Kun Zhou, S. Shi, Kai Song, Xu Chen   +4 more
semanticscholar   +1 more source

Development of a fatigue life prediction system for a bogie frame- Actual life prediction using the fatigue life prediction system

The Proceedings of the Transportation and Logistics Conference, 2021
Daisuke SHINAGAWA, Yoshiyuki SHIMOKAWA, Seita ITOH, Takahiro FUJIMOTO, Osamu KONDO, Junichi NAKAGAWA, Hideki MINAMI   +6 more
openaire   +1 more source

Fatigue life prediction of nickel base superalloys. [PDF]

, 2007
Neural networks have been used extensively in material science with varying success.It has been demonstrated that they can be very effective at predicting mechanical properties such as yield strength and ultimate tensile strength. These networks require large amounts of input data in order to learn the correct data trends.
openaire   +1 more source

A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions

International Journal of Fatigue, 2021
Jian-Guo Gong, Fu-Zhen Xuan
exaly  

Fatigue life prediction: metals and composites

1984
The structural design philosophy based on durability and damage tolerance requires prediction of fatigue crack growth due to anticipated service loads. The essential elements of this analysis are: service load histories (random spectrum loads); characterization of constant amplitude data for material in question; and fatigue model.
openaire   +1 more source

Physics-informed machine learning for low-cycle fatigue life prediction of 316 stainless steels

International Journal of Fatigue
Lvfeng Jiang, Ya-Nan Hu, Yuxuan Liu, Xu Zhang, Guo-Zheng Kang, Q. Kan   +5 more
semanticscholar   +1 more source

Application of the Gaussian process for fatigue life prediction under multiaxial loading

Mechanical Systems and Signal Processing, 2022
Aleksander Karolczuk, Marek Słoński
exaly  

Neural network integrated with symbolic regression for multiaxial fatigue life prediction

International Journal of Fatigue
Peng Zhang, Ke-Ke Tang, Anbin Wang, Hao Wu, Zheng Zhong   +4 more
semanticscholar   +1 more source