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 Fatigue life prediction and influencing factors analysis of mesh flexible spoke non-pneumatic tire

2021 ◽  
Vol 13 (10) ◽  
pp. 168781402110524
Author(s):  
Hongxun Fu ◽  
Xiaoxia Chen ◽  
Qiang Zhao ◽  
Zhen Xiao ◽  
Xuemeng Liang
Keyword(s):  
Fatigue Life ◽  
Life Prediction ◽  
Optimization Design ◽  
Structural Parameters ◽  
Prediction Method ◽  
Simulation Method ◽  
Fatigue Life Prediction ◽  
Fatigue Performance ◽  
Driving Safety ◽  
Pneumatic Tire

A mesh flexible spoke non-pneumatic tire is designed to avoid tire burst and other hidden dangers in the traditional pneumatic tires, and improve driving safety. The purpose of this study is to explore the fatigue performance and fatigue life prediction method of the non-pneumatic tire and analyze the influence of structural parameters on the fatigue life of non-pneumatic tire. Based on the crack propagation method of energy release rate by J-integral, the fatigue life of the meshed flexible spoke non-pneumatic tire is predicted. Using numerical simulation method, the influence of key structural parameters, such as the curvature, unit angle and thickness of the lateral spoke and the tread thickness, on tire fatigue life is studied. The results show that the fatigue life prediction method proposed can be used to predict the fatigue life of flexible spoke non-pneumatic tire, and the fatigue life of non-pneumatic tire with flexible spoke can be improved by selecting appropriate structural parameters, which could provide some reference for the structural optimization design of the fatigue performance of the non-pneumatic tire.

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.

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.

scholarly journals Fatigue life prediction method for notched 3D woven composites based on progressive damage and field intensity

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaming Xue ◽  
Weidong Wen ◽  
Haitao Cui
Keyword(s):  
Experimental Data ◽  
Fatigue Life ◽  
Fatigue Damage ◽  
Field Intensity ◽  
Life Prediction ◽  
Prediction Method ◽  
Fatigue Life Prediction ◽  
Woven Composites ◽  
Progressive Damage ◽  
3D Woven Composites

To solve the problem of fatigue life prediction of notched 3D woven composites, a novel fatigue life prediction method based on progressive damage and field intensity theory is proposed. By analyzing the initial loading process of 3D woven composite structure based on progressive damage method, the stress field when the fatigue damage develops gently and the dangerous area where the fatigue damage occurs first are obtained. Then combining with the concept of notch field intensity function, the failure criteria of composites and the three-dimensional space vector stress field intensity method, the fatigue damage degree field intensity of the fiber yarns in the dangerous area is analyzed and the most damaged fiber yarn is found. The fatigue life of the structure is calculated with the field intensity equivalent stress and the S-N curve of this fiber yarn. In order to verify the effectiveness of this method, the fatigue life of notched 3D woven composites under several stress levels is predicted and compared with the existing experimental data. The compared results show that the fatigue life prediction values are all within the two-fold error bounds of the experimental data, which are in good agreement with experimental results. Compared with the progressive damage fatigue life prediction method of woven composites, the prediction accuracy of the method proposed in this paper is improved by 31.5% on average, and the calculation efficiency is also greatly improved.

Fatigue Life Prediction of Buckling String with Cracks in Horizontal Wells of Mining Engineering

2012 ◽  
Vol 577 ◽  
pp. 127-131 ◽  
Author(s):  
Peng Wang ◽  
Tie Yan ◽  
Xue Liang Bi ◽  
Shi Hui Sun
Keyword(s):  
Fatigue Life ◽  
Prediction Model ◽  
Life Prediction ◽  
Horizontal Well ◽  
Drill Pipe ◽  
Prediction Method ◽  
Drill String ◽  
Fatigue Life Prediction ◽  
Mining Engineering ◽  
Life Prediction Model

Fatigue damage in the rotating drill pipe in the horizontal well of mining engineering is usually resulted from cyclic bending stresses caused by the rotation of the pipe especially when it is passing through curved sections or horizontal sections. This paper studies fatigue life prediction method of rotating drill pipe which is considering initial crack in horizontal well of mining engineering. Forman fatigue life prediction model which considering stress ratio is used to predict drill string fatigue life and the corresponding software has been written. The program can be used to calculate the stress of down hole assembly, can predict stress and alternating load in the process of rotating-on bottom. Therefore, establishing buckling string fatigue life prediction model with cracks can be a good reference to both operation and monitor of the drill pipe for mining engineering.

scholarly journals A New Multiparameter Model for Multiaxial Fatigue Life Prediction of Rubber Materials

Polymers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1194
Author(s):  
Rafael Tobajas ◽  
Daniel Elduque ◽  
Elena Ibarz ◽  
Carlos Javierre ◽  
Luis Gracia
Keyword(s):  
Fatigue Life ◽  
Life Prediction ◽  
Experimental Testing ◽  
Experimental Tests ◽  
Relevant Information ◽  
Fatigue Tests ◽  
Multiaxial Fatigue ◽  
Fatigue Life Prediction ◽  
Proposed Model ◽  
Predicted Values

Most of the mechanical components manufactured in rubber materials experience fluctuating loads, which cause material fatigue, significantly reducing their life. Different models have been used to approach this problem. However, most of them just provide life prediction only valid for each of the specific studied material and type of specimen used for the experimental testing. This work focuses on the development of a new generalized model of multiaxial fatigue for rubber materials, introducing a multiparameter variable to improve fatigue life prediction by considering simultaneously relevant information concerning stresses, strains, and strain energies. The model is verified through its correlation with several published fatigue tests for different rubber materials. The proposed model has been compared with more than 20 different parameters used in the specialized literature, calculating the value of the R2 coefficient by comparing the predicted values of every model, with the experimental ones. The obtained results show a significant improvement in the fatigue life prediction. The proposed model does not aim to be a universal and definitive approach for elastomer fatigue, but it provides a reliable general tool that can be used for processing data obtained from experimental tests carried out under different conditions.

Adaptive Neural Network Modelling in Fatigue life Prediction under Load History effects

2011 ◽  
Vol 284-286 ◽  
pp. 1266-1270
Author(s):  
M. Abdul Razzaq ◽  
Kamal A. Ariffin ◽  
Ahmed El Shafie ◽  
Shahrum Abdullah ◽  
Z. Sajuri ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Crack Growth ◽  
Life Prediction ◽  
Experimental Tests ◽  
Fatigue Life Prediction ◽  
Load History ◽  
Adaptive Neural Network ◽  
Network Modelling ◽  
History Effects ◽  
Point Bend

Artificial intelligence (AI) techniques and in particular, adaptive neural networks (ANN) have been commonly used in order to Fatigue life prediction. The aim of this paper is to consider a new crack propagation principle based on simulating experimental tests on three point-bend (TPB) specimens, which allow predicting the fatigue life and fatigue crack growth rate (FCGR). An important part of this paper is estimation of FCG rate related to different load histories. The effects of different load histories on the crack growth life are obtained in different representative simulation and experiments.

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