Stochastic Fatigue Life Prediction Based on a Reduced Data Set

2019 ◽  
Author(s):  
Dino Celli ◽  
M.-H. Herman Shen ◽  
Onome E. Scott-Emuakpor ◽  
Casey M. Holycross ◽  
Tommy J. George
Keyword(s):  
Fatigue Life ◽  
Strain Energy ◽  
Life Prediction ◽  
Prediction Method ◽  
Fatigue Life Prediction ◽  
Data Set ◽  
The Monte Carlo Method ◽  
Data Points ◽  
Reduced Data ◽  
Cycles To Failure

Abstract The aim of this paper is to provide a novel stochastic life prediction approach capable of predicting the total fatigue life of applied uniaxial stress states from a reduced data set reliably and efficiently. A previously developed strain energy-based fatigue life prediction method is integrated with the stochastic state space approach for prediction of total cycles to failure. The approach under consideration for this study is the Monte Carlo method where input is randomly generated to approximate the output of highly complex systems. The strain energy fatigue life prediction method is used to first approximate SN behavior from a set of two SN data points. This process is repeated with another unique set of SN data points to evaluate and approximate distribution of cycles to failure at a given stress amplitude. Uniform, normal, log-normal, and Weibull distributions are investigated. From the Monte Carlo Method, fatigue data is sampled from the approximated distribution and an SN curve is generated to predict HCF behavior from LCF data.

Stochastic Fatigue Life Prediction Based on a Reduced Data Set

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Dino Celli ◽  
M.-H. Herman Shen ◽  
Onome Scott-Emuakpor ◽  
Casey Holycross ◽  
Tommy George
Keyword(s):  
Fatigue Life ◽  
Strain Energy ◽  
Life Prediction ◽  
Prediction Method ◽  
Fatigue Life Prediction ◽  
Cycle Fatigue ◽  
Data Set ◽  
Data Points ◽  
Prediction Approach ◽  
Cycles To Failure

Abstract The aim of this paper is to provide a novel stochastic life prediction approach capable of predicting the total fatigue life of applied uniaxial stress states from a reduced dataset reliably and efficiently. A previously developed strain energy-based fatigue life prediction method is integrated with the stochastic state space approach for prediction of total cycles to failure. The approach under consideration for this study is the Monte Carlo method (MCM) where input is randomly generated to approximate the output of highly complex systems. The strain energy fatigue life prediction method is used to first approximate SN behavior from a set of two SN data points. This process is repeated with another unique set of SN data points to evaluate and approximate distribution of cycles to failure at a given stress amplitude. Uniform, normal, log-normal, and Weibull distributions are investigated. From the MCM, fatigue data are sampled from the approximated distribution and an SN curve is generated to predict high cycle fatigue (HCF) behavior from low cycle fatigue (LCF) data.

Hysteresis Strain Energy Behavior of Al6061-T6 With Multi-Fatigue Load Levels as Applied to an Energy-Based Fatigue Life Prediction Method

2013 ◽  
Author(s):  
Todd Letcher ◽  
Sepehr Nesaei ◽  
Cody Auen ◽  
Matt Nielsen ◽  
Fereidoon Delfanian
Keyword(s):  
Fatigue Life ◽  
Strain Energy ◽  
Life Prediction ◽  
Prediction Method ◽  
Fatigue Life Prediction ◽  
Representative Specimen ◽  
Energy Behavior ◽  
Stress Levels ◽  
Single Stress ◽  
Life Predictions

Fatigue testing is a time and resource-consuming task. Historically, SN testing was conducted at many stress levels on simple representative specimen in order to determine an SN curve, which could then be used to design a component from the same type of material. Recently, an energy-based fatigue life prediction method has been in development. The goal of this method is to quickly determine a material’s fatigue characteristics using simple test procedures. The main theory behind the energy-based fatigue life prediction method is that the strain energy in a monotonic tensile test is equal to the cumulative hysteresis energy of a cyclic test. This theory has always been tested using a single stress level on each specimen. The hysteresis loop information was then used to make fatigue life predictions at other stress levels. Further testing has been done to learn more about the hysteresis energy behavior throughout the lifetime of a specimen, but only for a single stress value. In this study, several stress levels were tested on a single specimen. This new information will help make fatigue life predictions by completely removing the difficult and inconsistent process of determining experimental curve fit coefficients traditionally used in the energy-based fatigue life prediction method.

Rapid Characterization of Fatigue Performance With Application to Additive Manufactured Components

2020 ◽  
Author(s):  
Dino A. Celli ◽  
M.-H. Herman Shen ◽  
Onome E. Scott-Emuakpor ◽  
Tommy J. George
Keyword(s):  
Experimental Data ◽  
Fatigue Life ◽  
Life Prediction ◽  
Prediction Method ◽  
Experimental Tests ◽  
Parameter Selection ◽  
Process Parameter ◽  
Fatigue Life Prediction ◽  
Fatigue Performance ◽  
Cycles To Failure

Abstract The aim of this paper is to provide a fatigue life prediction method which can concurrently approximate both SN behavior as well as the inherent variability of fatigue efficiently with a limited number of experimental tests. The purpose of such a tool is for the quality assessment and verification of components using Additive Manufacturing (AM) processes and other materials with a limited knowledgebase. Interest in AM technology is continually growing in many industries, such as aerospace, automotive, or biomedical. But components often result in highly variable fatigue performance. The determination of optimal process parameters for the build process can be an extensive and costly endeavor due to either a limited knowledgebase or proprietary restrictions. Quantifying the significant variability of fatigue performance in AM components is a challenging task as there are many causes including machine to machine differences, recycles of powder, and process parameter selection. Therefore, a life prediction method which can rapidly determine the fatigue performance of a material with little or no prior information of the material and a limited number of experimental tests is developed as an aid in process parameter selection and fatigue performance qualification. This is performed by using a previously developed and simplistic energy based fatigue life prediction method, or Two Point method, to predict the inherent variability associated with fatigue performance. The proposed approach is verified by using predicted distributions of stress and cycles to failure and comparing with experimental data at 104 and 106 cycles to failure. SN life prediction is modeled via a modified Random Fatigue Limit (RFL) model where the two RFL model parameters are evaluated using Bayesian statistical inference and stochastic sampling techniques for distribution estimation. This is performed in a dynamic way such that the life prediction model is continually updated with the generation of experimental data.

scholarly journals An Energy-Based Experimental-Analytical Torsional Fatigue Life-Prediction Method

2010 ◽  
Author(s):  
John N. Wertz ◽  
M.-H. Herman Shen ◽  
Tommy George ◽  
Charles Cross ◽  
Onome Scott-Emuakpor
Keyword(s):  
Fatigue Life ◽  
Strain Energy ◽  
Life Prediction ◽  
Fatigue Testing ◽  
Pure Shear ◽  
Prediction Method ◽  
Strain Curve ◽  
Fatigue Life Prediction ◽  
Torsional Fatigue ◽  
Shear Fatigue

An energy-based fatigue life prediction framework for calculation of torsional fatigue life and remaining life has been developed. The framework for this fatigue prediction method is developed in accordance with our previously developed energy-based axial and bending fatigue life prediction approaches, which state: the total strain energy dissipated during a monotonic fracture and cyclic processes is the same material property, where each can be determined by measuring the area underneath the monotonic true stress-strain curve and the area within a hysteresis loop, respectively. The energy-based fatigue life prediction framework is composed of the following entities: (1) development of a shear fatigue testing procedure capable of assessing strain energy density per cycle in a pure shear stress state and (2) incorporation of an energy-based fatigue life calculation scheme to determine the remaining fatigue life of in-service gas turbine materials subjected to pure shear fatigue.

An Energy Based Critical Fatigue Life Prediction Method

2011 ◽  
Author(s):  
Todd Letcher ◽  
M.-H. Herman Shen ◽  
Onome Scott-Emuakpor ◽  
Tommy George ◽  
Charles Cross
Keyword(s):  
Fatigue Life ◽  
Energy Density ◽  
Strain Energy Density ◽  
Strain Energy ◽  
Life Prediction ◽  
Failure Process ◽  
Prediction Method ◽  
Fatigue Life Prediction ◽  
Stress Strain ◽  
Energy Behavior

The capability of a critical life, energy-based fatigue prediction method is analyzed in this study. The theory behind the prediction method states that the strain energy accumulated during monotonic fracture and fatigue are equal. Therefore, a precise understanding of the strain energy density behavior in each failure process is necessary. The initial understanding of energy behavior shows that the accumulated strain energy density during monotonic fracture is the area underneath the experimental stress-strain curve, whereas the sum of the constant area within every stress-strain hysteresis loop of the cyclic loading process is the total strain energy density accumulated during fatigue; meaning, fatigue life is determined by dividing monotonic strain energy density by the strain energy density in one cycle. Further observation of the energy trend during fatigue shows that strain energy density per cycle is not constant throughout the process as initially assumed. This finding led to the incorporation of a critical life effect into the energy-based fatigue prediction method. The analysis of the method’s capability was conducted on Al 6061-T6 ASTM standard specimens. The results of the analysis provide further improvement to the characterization of strain energy density for both monotonic fracture and fatigue; thus improving the capability of the energy-based fatigue life prediction method.

The Effect of Heat Generation on Low Cycle Fatigue Life Prediction

2011 ◽  
Author(s):  
Onome Scott-Emuakpor ◽  
Tommy George ◽  
Charles Cross ◽  
Todd Letcher ◽  
John Wertz ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Heat Generation ◽  
Strain Energy ◽  
Life Prediction ◽  
Low Cycle Fatigue ◽  
Prediction Method ◽  
Fatigue Life Prediction ◽  
Cycle Fatigue ◽  
Physical Damage ◽  
Stress Ratios

An energy-based life prediction method is used in this study to determine the fatigue life of tension-compression loaded components in the very low cycle regime between 102 and 104. The theoretical model for the energy-based prediction method was developed from the concept that the strain energy accumulated during both monotonic failure and an entire fatigue process are equal; In other words, the scalar quantity of strain energy accumulated during monotonic failure is a physical damage quantity that correlates to fatigue as well. The energy-based method has been successfully applied to fatigue life prediction of components failing in the fatigue regime between 104 and 107 cycles. To assess Low Cycle Fatigue (LCF) with the prediction method, a clearer understanding of energy dissipation through heat, system vibration, damping, surface defects and acoustics were necessary. The first of these topics analyzed is heat. The analysis conducted studies the effect of heat generated during cyclic loading and heat loss from slipping at the interface of the grip wedges of the servo-hydraulic load frame and the test specimen. The reason for the latter is to address the notion that slippage in the experimental setup may be the cause of the reduction in the accuracy of the energy-based prediction method for LCF, which was seen in previous research. These analyses were conducted on Titanium 6Al-4V, where LCF experimental data for stress ratios R = −1 and R = −0.813 were compared with the energy-based life prediction method. The results show negligible effect on both total and cyclic energy from heat generation at the interface of the grip wedges and heat generation in the fatigue zone of the specimen.

scholarly journals Strain energy-based rubber fatigue life prediction under the influence of temperature

2018 ◽  
Vol 5 (10) ◽  
pp. 180951 ◽  
Author(s):  
Jingnan Zhang ◽  
Fengxian Xue ◽  
Yue Wang ◽  
Xin Zhang ◽  
Shanling Han
Keyword(s):  
Fatigue Life ◽  
Prediction Model ◽  
Fatigue Damage ◽  
Strain Energy ◽  
Life Prediction ◽  
Least Square ◽  
Fatigue Life Prediction ◽  
Uniaxial Tensile ◽  
Influence Of Temperature ◽  
Different Temperatures

Aiming at the problem of the fatigue life prediction of rubber under the influence of temperature, the effects of thermal ageing and fatigue damage on the fatigue life of rubber under the influence of temperature are analysed and a fatigue life prediction model is established by selecting strain energy as a fatigue damage parameter based on the uniaxial tensile data of dumbbell rubber specimens at different temperatures. Firstly, the strain energy of rubber specimens at different temperatures is obtained by the Yeoh model, and the relationship between it and rubber fatigue life at different temperatures is fitted by the least-square method. Secondly, the function formula of temperature and model parameters is obtained by the least-square polynomial fitting. Finally, another group of rubber specimens is tested at different temperatures and the fatigue characteristics are predicted by using the proposed prediction model under the influence of temperature, and the results are compared with the measured results. The results show that the predicted value of the model is consistent with the measured value and the average relative error is less than 22.26%, which indicates that the model can predict the fatigue life of this kind of rubber specimen at different temperatures. What's more, the model proposed in this study has a high practical value in engineering practice of rubber fatigue life prediction at different temperatures.

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