Development and Validation of a S-N Based Two Phase Bending Fatigue Life Prediction Model

Author(s):  
Avinash Singh

Abstract The stress-life (S-N) method along with the Palmgren-Miner cumulative damage theory is the simplest and the most commonly used fatigue life prediction technique. Its main advantage is that the material properties needed are easy to collect and life calculation is simple. However under many variable amplitude loading conditions, life predictions have been found to be unreliable. Various modifications have been proposed to the Palmgren-Miner theory, but they have not lead to more reliable life predictions. In this paper, a two-stage cumulative damage model will be developed and validated. This model divides fatigue life into two phases — a crack initiation phase and a crack propagation phase. It will be shown that the proposed method results in greatly improved life prediction capabilities. Also, the proposed method retains the simplicity of the S-N based approach in that the material data is still relatively simple to generate and the calculations are straightforward.

2003 ◽  
Vol 125 (3) ◽  
pp. 540-544 ◽  
Author(s):  
Avinash Singh

The stress-life S-N method along with the Palmgren-Miner cumulative damage theory is the simplest and the most commonly used fatigue life prediction technique. Its main advantage is that the material properties needed are easy to collect and life calculation is simple. However under many variable amplitude loading conditions, life predictions have been found to be unreliable. Various modifications have been proposed to the Palmgren-Miner theory, but they have not lead to more reliable life predictions. In this paper, a two-stage cumulative damage model will be developed and validated. This model divides fatigue life into two phases—a crack initiation phase and a crack propagation phase. It will be shown that the proposed method results in greatly improved life prediction capabilities. Also, the proposed method retains the simplicity of the S-N based approach in that the material data is still relatively simple to generate and the calculations are straightforward.


2014 ◽  
Vol 1055 ◽  
pp. 161-164
Author(s):  
Tao Wang ◽  
Wei Zhong Zhang ◽  
Chen Xie ◽  
Deng Xia Zhang ◽  
Yan Ru

With the study subject of the gear transmission in an unmanned system, several common methods of fatigue life prediction are analyzed. According to the actual running state, S-N nominal stress method is used to predict the fatigue life of the gears. Based on the S-N data of the gear material and the linear cumulative damage theory, ANSYS is used to estimate the bending fatigue life of the gears, so as to obtain the fatigue life loss coefficient of the gears. It provides a reliable data reference of the design, use and maintenance of the gear transmission in unmanned system.


2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199530
Author(s):  
Bixiong Huang ◽  
Shuci Wang ◽  
Shuanglong Geng ◽  
Xintian Liu

To more accurately predict the fatigue life of components under the action of random loads, it is necessary to explore the influence of the interaction between the load sequence and the load on the life prediction. Based on the Manson-Halford method and Corten-Dolan model, this paper establishes a fatigue cumulative damage model that takes into account both the load order and the interaction between loads, and also takes into account the loads near the fatigue limit. The fatigue life of mechanical parts under random load can be calculated through this model, which provides a theoretical basis for life prediction under random load spectrum. The fatigue life of mechanical parts under random load can be calculated through this model, which provides a theoretical basis for life prediction under random load spectrum. Comparing the calculation results of the proposed model with the results of Palmgren Miner, Manson-Halford method, and Corten-Dolan model, it is found that the fatigue damage model established can reasonably predict the fatigue life of parts. Comparison and verification of examples further prove the accuracy and reliability of the proposed model.


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