A New Nonproportional Low Cycle Fatigue Life Prediction Method for Type 304 Stainless Steel

2019 ◽  
Vol 141 (2) ◽  
Author(s):  
Bowen Liu ◽  
Xiangqiao Yan
Keyword(s):  
Stainless Steel ◽  
Fatigue Life ◽  
Life Prediction ◽  
Low Cycle Fatigue ◽  
Prediction Method ◽  
Fatigue Life Prediction ◽  
New Method ◽  
304 Stainless Steel ◽  
Loading Path ◽  
Prediction Approach

A new method is put forward to predict fatigue life for low cycle nonproportional loading based on the Itoh criterion. The proposed method considers the multi-axiality influence on the reference maximum principal strain path and the calculation of nonproportionality factor Fnp by utilizing a multi-axial fatigue life prediction approach based on the modified Wöhler curve method. Different from the hypothesis of previous integral models for computing factor Fnp where the loading path is considered uniform, a new model using an inhomogeneous integral is presented and a path-dependent weight factor is defined to describe this inhomogeneity. The experimental tests of Itoh on 304 stainless steel with 14 different loading cases are referenced to examine the validity of the new method.

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.

scholarly journals Multiaxial Non-proportional Low Cycle Fatigue Properties of HIPped A319 Cast Aluminum Alloy

2021 ◽  
Author(s):  
Bin Ge ◽  
Xiaoshan Liu ◽  
Guoqiu He ◽  
Peiwen Le ◽  
Zhiqiang Zhou ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Prediction Model ◽  
Life Prediction ◽  
Low Cycle Fatigue ◽  
Fatigue Life Prediction ◽  
Fatigue Properties ◽  
Loading Path ◽  
Cast Aluminum Alloy ◽  
Cast Aluminum ◽  
Life Prediction Model

This paper describes a microstructure-based multiaxial non-proportional fatigue life prediction model with maximum shear strain and non-proportionality as damage parameters applied to A319 alloy. The materials made with different casting cooling rates and Sr modification are characterized and quantified in terms of secondary dendrite arm spacing (SDAS), size and aspect ratio of eutectic Si particles. Multiaxial non-proportional fatigue tests have been performed on six groups of A319 alloys to systematically analyze the effect of microstructure and loading path on the fatigue properties of Al-Si cast alloy. The first part of the paper is focused on microstructure quantitative characterization to determine the influence of different casting conditions, followed by stress response behavior and fatigue fracture analysis. Finally, quantitative relationship between six fatigue life parameters and microstructure characteristics is established and a new fatigue life prediction model is proposed to predict fatigue life of Al-Si alloy under multiaxial non-proportional loading condition.

scholarly journals Research on low cycle fatigue life prediction considering average strain

2022 ◽  
Author(s):  
Yan Peng ◽  
Yang Liu ◽  
Haoran Li ◽  
Jiankang Xing
Keyword(s):  
Fatigue Life ◽  
Life Prediction ◽  
Damage Mechanics ◽  
Prediction Models ◽  
Low Cycle Fatigue ◽  
Prediction Method ◽  
Fatigue Life Prediction ◽  
Model Parameters ◽  
Cycle Fatigue ◽  
Average Strain

Abstract To address the difficult problems in the study of the effect of average strain on fatigue life under low-cycle fatigue loads, the effect of average strain on the low-cycle fatigue life of materials under different strain cycle ratios was discussed based on the framework of damage mechanics and its irreversible thermodynamics. By introducing the Ramberg-Osgood cyclic constitutive equation, a new low-cycle fatigue life prediction method based on the intrinsic damage dissipation theory considering average strain was proposed, which revealed the correlation between low-cycle fatigue strain life , material properties, and average strain. Through the analysis of the low-cycle fatigue test data of five different metal materials, the model parameters of the corresponding materials were obtained. The calculation results indicate that the proposed life prediction method is in good agreement with the test, and a reasonable characterization of the low-cycle fatigue life under the influence of average strain is realized. Comparing calculations with three typical low-cycle fatigue life prediction models, the new method is within two times the error band, and the prediction effect is significantly better than the existing models, which is more suitable for low-cycle fatigue life prediction. The low-cycle fatigue life prediction of different cyclic strain ratios based on the critical region intrinsic damage dissipation power method provides a new idea for the research of low-cycle fatigue life prediction of metallic materials.

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.

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