An accurate and stable data‐driven model is proposed in this work for fatigue life prediction
in presence of mean stresses. Multiple independent extreme learning machines are …

The mean stress effect plays a vital role in fatigue life analysis, affecting both macro-
mechanical response and micro-crack evolution of materials. Even though semiempirical …

Fatigue stress concentration factor (FSCF) plays a vital role in studying the limitation of
material fatigue resistance. Theoretically, FSCF not only reflects the level of fatigue stress …

Remaining fatigue life estimations are not trivial problems for most engineering applications.
They could even become quite challenging for general multistep load spectrums, particularly …

The fatigue life evaluation of metallic materials plays an important role in ensuring the safety
and long service life of metal structures. To further improve the accuracy and efficiency of the …

Deep learning is a widely used tool for multiaxial fatigue life prediction. However, neural
network still needs to solve various problems in the solution dominated by physical …

The research on life prediction for mechanical structures in very high cycle fatigue regime is
pivotal to improve structure service, but it can be costly and time-consuming to collect fatigue …

A versatile data-driven model integrating domain knowledge and deep neural networks
(DNNs) is proposed for fatigue life prediction with small samples. In the model, traditional …

In this article, machine learning is used to predict lifetime under isothermal low-cycle fatigue
and thermo-mechanical fatigue loading, both of which represent the most complex loadings …

A tensile properties-related fatigue strength prediction framework based on machine
learning (ML) methods was proposed. Firstly, 200 data containing six materials used in …