scholarly journals Study of pooling method for Bi-linear type fatigue life data based on control chart.

1986 ◽  
Vol 35 (399) ◽  
pp. 1378-1384
Author(s):  
Hidetoshi NAKAYASU
Keyword(s):  
Fatigue Life ◽  
Control Chart ◽  
Linear Type ◽  
Life Data

scholarly journals Bayesian estimation of the lethargy coefficient for probabilistic fatigue life model

2017 ◽  
Vol 5 (2) ◽  
pp. 191-197 ◽  
Author(s):  
Jaehyeok Doh ◽  
Jongsoo Lee
Keyword(s):  
Fatigue Life ◽  
Sampling Method ◽  
Bayesian Updating ◽  
Unknown Parameters ◽  
Life Data ◽  
Mcmc Sampling ◽  
Fatigue Model ◽  
Relative Stress ◽  
Life Model ◽  
Static Tensile

Abstract In this study, a model for probabilistic fatigue life that is based on the Zhurkov model is suggested using stochastically and statistically estimated lethargy coefficients. The fatigue life model was derived using the Zhurkov life model, and it was deterministically validated using real fatigue life data as a reference. For this process, firstly, a lethargy coefficient that is related to the failure of materials must be obtained with rupture time and stress from a quasi-static tensile test. These experiments are performed using HS40R steel. However, the lethargy coefficient has discrepancies due to the inherent uncertainty and the variation of material properties in the experiments. The Bayesian approach was employed for estimating the lethargy coefficient of the fatigue life model using the Markov Chain Monte Carlo (MCMC) sampling method and considering its uncertainties. Once the samples are obtained, one can proceed to the posterior predictive inference of the fatigue life. This life model was shown to be reasonable when compared with experimental fatigue life data. As a result, predicted fatigue life was observed to significantly decrease in accordance with increasing relative stress conditions. Highlights Zhurkov fatigue life model is deterministically validated with experiments. Prediction of the S-N curve using Zhurkov fatigue model and lethargy coefficients. Lethargy coefficients of Zhurkov fatigue model are estimated by Bayesian updating. Bayesian updating is useful for quantifying the uncertainty of unknown parameters.

Capturing uncertainty in fatigue life data

2015 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Ravi C. Penmetsa ◽  
Raymond R. Hill ◽  
Darryl K. Ahner ◽  
Brent D. Russell
Keyword(s):  
Fatigue Life ◽  
Life Data

Lower Tail Estimation of Fatigue Life

2019 ◽  
Author(s):  
D. Gary Harlow
Keyword(s):  
Fatigue Life ◽  
Extreme Value ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Tail Behavior ◽  
Confidence Bounds ◽  
Lower Tail ◽  
Life Data ◽  
Order Of Magnitude ◽  
Value Estimation

Abstract Uncertainty in the prediction of lower tail fatigue life behavior is a combination of many causes, some of which are aleatoric and some of which are systemic. The error cannot be entirely eliminated or quantified due to microstructural variability, manufacturing processing, approximate scientific modeling, and experimental inconsistencies. The effect of uncertainty is exacerbated for extreme value estimation for fatigue life distributions because by necessity those events are rare. In addition, typically, there is a sparsity of data in the region of smaller stress levels in stress–life testing where the lives are considerably longer, extending to giga cycles for some applications. Furthermore, there is often over an order of magnitude difference in the fatigue lives in that region of the stress–life graph. Consequently, extreme value estimation is problematic using traditional analyses. Thus, uncertainty must be statistically characterized and appropriately managed. The primary purpose of this paper is to propose an empirically based methodology for estimating the lower tail behavior of fatigue life cumulative distribution functions, given the applied stress. The methodology incorporates available fatigue life data using a statistical transformation to estimate lower tail behavior at much smaller probabilities than can be estimated by traditional approaches. To assess the validity of the proposed methodology confidence bounds will be estimated for the stress–life data. The development of the methodology and its subsequent validation will be illustrated using extensive fatigue life data for 2024–T4 aluminum alloy specimens readily available in the open literature.

RELIABILITY ASSESSMENT FOR AN AUTOMOBILE CRANKSHAFT UNDER RANDOM LOADING

2016 ◽  
Vol 78 (6-10) ◽  
Author(s):  
S.S.K. Singh ◽  
S. Abdullah ◽  
N.A.N. Mohamed
Keyword(s):  
Fatigue Life ◽  
Structural Health Monitoring ◽  
Markov Process ◽  
Health Monitoring ◽  
Reliability Assessment ◽  
Life Assessment ◽  
Loading History ◽  
Random Loading ◽  
Structural Health ◽  
Life Data

This paper presents the stochastic process for reliability  assessment based on the fatigue life data under random loading for structural health monitoring of an automobile crankshaft due tofatigue failure. This is based on reported failure of the component due to the effect of the random loads that acts on the component during its operating condition over a given period of time. Since there are significant limitations of the experimental analysis in terms of actual loading history, therefore, the reliability assessment is considered to be less accurate. Hence, the reliability assessment based on fatigue life data using the Markov process by incorporating loading data to synthetically generate loading history has been proposed in this study. The Markov process has the capability of continuously updating the loading history data to reduce the intervals between each data point for reliability assessment based on the fatigue life data. The accuracy of the proposed monitoring system for reliability assessment was validated through its statistical method. The reliability assessment from the Markov process corresponded well by providing an accuracy of more than 95% when compared towards the actual sampling data. The reliability of the crankshaft based on the fatigue life assessment provides a highly accurate  for the improvement and control of risk factors in terms of structural health monitoring by overcoming the extensive time and cost required for fatigue testing

scholarly journals Structural Stress Method to Evaluate Fatigue Properties of Similar and Dissimilar Self-Piercing Riveted Joints

Metals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 359 ◽  
Author(s):  
Harish Rao ◽  
Jidong Kang ◽  
Garret Huff ◽  
Katherine Avery
Keyword(s):  
Fatigue Life ◽  
Analytical Model ◽  
Fiber Reinforced Polymer ◽  
Fatigue Properties ◽  
Structural Stress ◽  
Life Data ◽  
Reinforced Polymer ◽  
Riveted Joints ◽  
Cfrp Composite ◽  
Head Height

In this paper, we discuss the application of a simple Battelle structural stress model to evaluate the fatigue life of a self-piercing riveted (SPR) carbon-fiber-reinforced polymer (CFRP) composite to aluminum AA6111. The analytical model accounts for the forces and moments acting on the rivets to determine the structural stresses which were then plotted against the laboratory-generated fatigue life data. The master S-N curve determined in this study thus accounts for various factors such as the stacking configuration, rivet head height, and fatigue load ratios. The analytical model used in this study was able to collapse a large number of fatigue life data into one master S-N curve irrespective of stack-ups, rivet head height, and load ratios. Thus, the master S-N curve derived from the model can be used to predict the fatigue life of the SPR joints.

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