Stress-Based Fatigue Performance and Fatigue Life Prediction of Engineered Cementitious Composites

2021 ◽  
Vol 33 (2) ◽  
pp. 04020470
Author(s):  
Burhan Alam ◽  
Ismail Ozgur Yaman
Keyword(s):  
Fatigue Life ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Fatigue Performance ◽  
Cementitious Composites ◽  
Engineered Cementitious Composites

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.

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.

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