scholarly journals Hybrid residual fatigue life prediction approach for gear based on Paris law and particle filter with prior crack growth information

2017 ◽  
Vol 19 (8) ◽  
pp. 5908-5919 ◽  
Author(s):  
Yunxian Jia ◽  
Xin Liu ◽  
Zewen He ◽  
Lei Sun
Keyword(s):  
Fatigue Life ◽  
Particle Filter ◽  
Crack Growth ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Paris Law ◽  
Residual Fatigue Life ◽  
Residual Fatigue ◽  
Prediction Approach

Residual fatigue life prediction of ball bearings based on Paris law and RMS

2012 ◽  
Vol 25 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Dong Xu ◽  
Jin’e Huang ◽  
Qin Zhu ◽  
Xun Chen ◽  
Yongcheng Xu ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Ball Bearings ◽  
Paris Law ◽  
Residual Fatigue Life ◽  
Residual Fatigue

Equivalent damage-healing approach to residual fatigue life prediction for copper film by laser repair

2014 ◽  
Vol 24 (5) ◽  
pp. 750-766 ◽  
Author(s):  
Xiao-Dong Liu ◽  
De-Guang Shang ◽  
Li-Hong Zhang ◽  
Yu-Bo Guo ◽  
Yu-Juan Sun ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Life Prediction ◽  
Copper Film ◽  
Fatigue Life Prediction ◽  
Residual Fatigue Life ◽  
Damage Healing ◽  
Residual Fatigue

211 Study on Fatigue Strength properties of Structural steel S45C, SCM435 and Residual Fatigue life Prediction of an Axle

2009 ◽  
Vol 2009.58 (0) ◽  
pp. 95-96
Author(s):  
Yoshitaka SUZUKI ◽  
Keiichiro TOHGO ◽  
Hiroyasu ARAKI ◽  
Yoshinobu SHIMAMURA ◽  
Atsuo SUGIURA ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Fatigue Strength ◽  
Structural Steel ◽  
Life Prediction ◽  
Strength Properties ◽  
Fatigue Life Prediction ◽  
Residual Fatigue Life ◽  
Residual Fatigue

Adaptive Neural Network Modelling in Fatigue life Prediction under Load History effects

2011 ◽  
Vol 284-286 ◽  
pp. 1266-1270
Author(s):  
M. Abdul Razzaq ◽  
Kamal A. Ariffin ◽  
Ahmed El Shafie ◽  
Shahrum Abdullah ◽  
Z. Sajuri ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Crack Growth ◽  
Life Prediction ◽  
Experimental Tests ◽  
Fatigue Life Prediction ◽  
Load History ◽  
Adaptive Neural Network ◽  
Network Modelling ◽  
History Effects ◽  
Point Bend

Artificial intelligence (AI) techniques and in particular, adaptive neural networks (ANN) have been commonly used in order to Fatigue life prediction. The aim of this paper is to consider a new crack propagation principle based on simulating experimental tests on three point-bend (TPB) specimens, which allow predicting the fatigue life and fatigue crack growth rate (FCGR). An important part of this paper is estimation of FCG rate related to different load histories. The effects of different load histories on the crack growth life are obtained in different representative simulation and experiments.

scholarly journals Recent Developments on the Unified Fatigue Life Prediction Method Based on Fracture Mechanics and its Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 427 ◽  
Author(s):  
Fang Wang ◽  
Weicheng Cui
Keyword(s):  
Fatigue Life ◽  
Fracture Mechanics ◽  
Crack Growth ◽  
Life Prediction ◽  
Structural Reliability ◽  
Influential Factors ◽  
Fatigue Life Prediction ◽  
Model Parameters ◽  
Sensitivity Index ◽  
Marine Structure

Safety analysis and prediction of a marine structure is of great concern by many stakeholders and the general public. In order to accurately predict the structural reliability of an in-use marine structure, one needs to calculate accurately the fatigue crack growth at any service time. This can only be possible by using fracture mechanics approach and the core of fracture-mechanics-based method is to establish an accurate crack growth rate model which must include all the influential factors of the same order of sensitivity index. In 2011, based on the analysis of various influencing factors, the authors put forward a unified fatigue life prediction (UFLP) method for marine structures. In the following ten years of research, some further improvements of this method have been made and the applications of this UFLP are carried out. In this paper, these progresses are reported and its underlying principles are further elaborated. Some basic test data used to determine model parameters are also provided.

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