Deep learning is a particular kind of machine learning, which achieves great power and
flexibility by a nested hierarchy of concepts. A general life prediction method for components …

In the present study, a computational-experimental framework is developed for fatigue
reliability assessment of turbine bladed disks. Within the framework, the overspeed testing is …

In this study, a dual-scale numerical procedure is developed to reveal the creep-fatigue
damage mechanisms and estimate the crack initiation life for notched structures made of …

High-reliability life design process not only can ensure system safety in service, but also can
provide scientific life management during the maintenance period. The objective of the …

This paper proposes a surrogate modeling approach based on XGboost machine learning
technique, in order to establish a data-driven map** relationship between input and …

In order to investigate the probabilistic damage distribution under creep-fatigue interaction,
machine learning framework with the divide-and-conquer methodology is proposed to …

This paper conducts a comparative evaluation on typical critical plane criteria, including
Fatemi-Socie, Wang-Brown, modified Smith-Watson-Topper (MSWT) and proposed modified …

In the present work, a probabilistic framework for fatigue life prediction and reliability
assessment of an engine high pressure turbine disc is proposed to incorporate the effects of …

Progressive degradation of material mechanical properties in the low cycle fatigue (LCF)
and creep-fatigue (CF) interaction at high temperature affects the safe operation of in-service …

The present work focuses on the quantification of creep and oxidation effects in the
prediction of both isothermal and thermomechanical fatigue life for a nickel-based …