Corrosion Fatigue Behavior and Life Prediction Method under Changing Temperature Condition

2009 ◽  
pp. 267-267-15
Author(s):  
H Kanasaki ◽  
A Hirano ◽  
K Iida ◽  
Y Asada
Keyword(s):  
Corrosion Fatigue ◽  
Life Prediction ◽  
Fatigue Behavior ◽  
Prediction Method

scholarly journals High-Cycle Microscopic Severe Corrosion Fatigue Behavior and Life Prediction of 25CrMo Steel Used in Railway Axles

Metals ◽  
2017 ◽  
Vol 7 (4) ◽  
pp. 134 ◽  
Author(s):  
Yan-Ling Wang ◽  
Xi-Shu Wang ◽  
Sheng-Chuan Wu ◽  
Hui-Hui Yang ◽  
Zhi-Hao Zhang
Keyword(s):  
Corrosion Fatigue ◽  
Life Prediction ◽  
Fatigue Behavior

scholarly journals High-Cycle Microscopic Corrosion Fatigue Behavior and Life Prediction of 25CrMo Steel Used in Railway Axle

2017 ◽  
Author(s):  
Xi-Shu Wang ◽  
Yan-Ling Wang ◽  
Sheng-Chuan Wu ◽  
Hui-Hui Yang ◽  
Zhi-Hao Zhang
Keyword(s):  
Crack Growth ◽  
Corrosion Fatigue ◽  
Life Prediction ◽  
Fracture Behavior ◽  
Fatigue Behavior ◽  
Fatigue Cracks ◽  
Environmental Media ◽  
Railway Axle ◽  
The Media ◽  
Fatigue Fracture Behavior

The effects of environmental media on the corrosion fatigue fracture behavior of 25CrMo steel were investigated. The media include air, and a 3.5 wt.% and a 5.0 wt.% NaCl solutions. Experimental results indicate that the media induces the initiation of corrosion fatigue cracks at multiple sites. The multi-cracking sites cause the changes in the crack growth directions, the crack growth rate during the coupling action of the media and the stress amplitude. The coupling effects are important for engineering applications and research. The probability and predictions of the corrosion fatigue characteristic life can be estimated using the 3-parameter Weibull distribution function.

HIGH TEMPERATURE CREEP AND FATIGUE BEHAVIOR AND LIFE PREDICTION METHOD OF A TiAl ALLOY

2013 ◽  
Vol 49 (11) ◽  
pp. 1311 ◽  
Author(s):  
Huichen YU ◽  
Chengli DONG ◽  
Zehui JIAO ◽  
Fantao KONG ◽  
Yuyong CHEN ◽  
...  
Keyword(s):  
High Temperature ◽  
Life Prediction ◽  
Tial Alloy ◽  
Fatigue Behavior ◽  
Prediction Method ◽  
High Temperature Creep ◽  
Temperature Creep

Dynamic fatigue reliability analysis of turbine blades under combined high and low cycle loadings

2021 ◽  
pp. 105678952098685
Author(s):  
Peng Yue ◽  
Juan Ma ◽  
Changhu Zhou ◽  
Jean W Zu ◽  
Baoquan Shi
Keyword(s):  
Life Prediction ◽  
Low Cycle Fatigue ◽  
Fatigue Behavior ◽  
Turbine Blades ◽  
Prediction Method ◽  
Damage Mechanism ◽  
Operational Reliability ◽  
Reliability Model ◽  
Fatigue Reliability ◽  
Dynamic Reliability

Establishment of damage accumulation models for reflecting the combined damage mechanism on the fatigue behavior of aero-engine turbine blades is crucial for their safety. In this work, a novel combined high and low cycle fatigue (CCF) life prediction methodology is presented as a basis of that to consider the interaction between low and high cycle fatigues. Accordingly, a dynamic reliability model is proposed to study the operational reliability of turbine blades under CCF loadings. Moreover, experimental data of materials along with the collected field data from the actual turbine blades are applied to validate the CCF life prediction model and the dynamic reliability model. The validation of the results is conducted by a comparison analysis, which indicates that the proposed life prediction method yields better accuracy, while the dynamic reliability model is proved to be more in line with the outcomes derived by the Monte Carlo simulation.

Estimation of Static Fatigue Behavior of Ceramic Materials as Candidate Overpack Materials

1993 ◽  
Vol 333 ◽  
Author(s):  
Masamichi Obata ◽  
Akira Honda ◽  
Hirohisa Ishikawa ◽  
Tadashi Mano
Keyword(s):  
Titanium Oxide ◽  
Life Prediction ◽  
Growth Parameters ◽  
Fatigue Behavior ◽  
Slow Crack Growth ◽  
Prediction Method ◽  
Ceramic Materials ◽  
Localized Corrosion ◽  
Static Fatigue ◽  
Stabilized Zirconia

ABSTRACTStatic fatigue behavior is one of the important factors for life prediciton of ceramic materials. In this study, SCG (slow crack growth) parameters were measured under atmosphere conditions, and the static fatigue behavior of alumina, PSZ (partially stabilized zirconia), and titanium oxide was examined.According to the results of the evaluatin of the static fatigue behavior, the destruction probability after 1,000 years would be less than 1/40,000 when tensile stresses occurring in the material were less than 79.4, 241.3, 8.0 MPa for alumina, PSZ, and titanium oxide, respectively. However titanium oxide could not be used because of the wall thickness that would be needed to accomplish this stress. The life prediction method includes only tht effect of preexisting flaws so the method to estimate the effect of localized corrosion is required for the future examination of the application of ceramic materials.

Hybrid remaining useful life prediction method. A case study on railway D-cables

2021 ◽  
pp. 107746
Author(s):  
Yu Zang ◽  
Wei Shangguan ◽  
Baigen Cai ◽  
Huasheng Wang ◽  
Michael. G. Pecht
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life

Remaining useful life prediction method for machine tools based on meta-action theory

2021 ◽  
pp. 1748006X2110025
Author(s):  
Zongyi Mu ◽  
Yan Ran ◽  
Genbao Zhang ◽  
Hongwei Wang ◽  
Xin Yang
Keyword(s):  
Machine Tools ◽  
Life Prediction ◽  
Action Theory ◽  
Prediction Method ◽  
Operational Reliability ◽  
Remaining Useful Life ◽  
Practical Application ◽  
Dynamic Prediction ◽  
Degradation Model ◽  
Useful Life

Remaining useful life (RUL) is a crucial indictor to measure the performance degradation of machine tools. It directly affects the accuracy of maintenance decision-making, thus affecting operational reliability of machine tools. Currently, most RUL prediction methods are for the parts. However, due to the interaction among the parts, even RUL of all the parts cannot reflect the real RUL of the whole machine. Therefore, an RUL prediction method for the whole machine is needed. To predict RUL of the whole machine, this paper proposes an RUL prediction method with dynamic prediction objects based on meta-action theory. Firstly, machine tools are decomposed into the meta-action unit chains (MUCs) to obtain suitable prediction objects. Secondly, the machining precision unqualified rate (MPUR) control chart is used to conduct an out of control early warning for machine tools’ performance. At last, the Markov model is introduced to determine the prediction objects in next prediction and the Wiener degradation model is established to predict RUL of machine tools. According to the practical application, feasibility and effectiveness of the method is proved.

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