Comparison of HCF life prediction methods based on different critical planes under multiaxial variable amplitude loading

2014 ◽  
Vol 38 (4) ◽  
pp. 392-401 ◽  
Author(s):  
C. Wang ◽  
D.-G. Shang ◽  
X.-W. Wang ◽  
H. Chen ◽  
J.-Z. Liu
2021 ◽  
Vol 120 ◽  
pp. 105000
Author(s):  
Bowen Wang ◽  
Liyang Xie ◽  
Jiaxin Song ◽  
Xuehong He ◽  
Weifeng Luo ◽  
...  

1996 ◽  
Vol 118 (3) ◽  
pp. 371-374 ◽  
Author(s):  
C. H. Wang ◽  
M. W. Brown

An extensive multiaxial random fatigue test programme was conducted at room temperature using tubular specimens. Experiments were performed under combined tension/torsion and triaxial loading, covering proportional and nonproportional variable amplitude loading cases. The two proposed life prediction methods discussed in Part 1 are evaluated using the experimental results, demonstrating that these two methods provide satisfactory predictions.


2016 ◽  
Vol 2016 ◽  
pp. 1-26 ◽  
Author(s):  
E. Santecchia ◽  
A. M. S. Hamouda ◽  
F. Musharavati ◽  
E. Zalnezhad ◽  
M. Cabibbo ◽  
...  

Metallic materials are extensively used in engineering structures and fatigue failure is one of the most common failure modes of metal structures. Fatigue phenomena occur when a material is subjected to fluctuating stresses and strains, which lead to failure due to damage accumulation. Different methods, including the Palmgren-Miner linear damage rule- (LDR-) based, multiaxial and variable amplitude loading, stochastic-based, energy-based, and continuum damage mechanics methods, forecast fatigue life. This paper reviews fatigue life prediction techniques for metallic materials. An ideal fatigue life prediction model should include the main features of those already established methods, and its implementation in simulation systems could help engineers and scientists in different applications. In conclusion, LDR-based, multiaxial and variable amplitude loading, stochastic-based, continuum damage mechanics, and energy-based methods are easy, realistic, microstructure dependent, well timed, and damage connected, respectively, for the ideal prediction model.


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