Fatigue Life Prediction Using Simplified Endurance Function Model
Joint Authors
Source
Advances in Mechanical Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-09
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
A methodology is proposed to apply an endurance function model with a genetic algorithm to estimate the fatigue life of notched or smooth components.
The endurance function model is based on stress tensor invariants and deviatoric stress invariants.
In the proposed methodology, FEA is used to simplify the application of the endurance function model.
Experimental results from published literature are considered for the case studies to evaluate the proposed methodology.
The results show that the proposed methodology simplified the application of the endurance function model, particularly by reducing the need for notch sensitivity factors, and the stress invariants can be calculated directly from the stresses at the critical point.
The comparison with experimental results shows that, with proper calibration, the model can predict fatigue life accurately.
American Psychological Association (APA)
Kamal, M.& Rahman, M. M.& Sani, M. S. M.. 2013. Fatigue Life Prediction Using Simplified Endurance Function Model. Advances in Mechanical Engineering،Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-482533
Modern Language Association (MLA)
Rahman, M. M.…[et al.]. Fatigue Life Prediction Using Simplified Endurance Function Model. Advances in Mechanical Engineering No. 2013 (2013), pp.1-12.
https://search.emarefa.net/detail/BIM-482533
American Medical Association (AMA)
Kamal, M.& Rahman, M. M.& Sani, M. S. M.. Fatigue Life Prediction Using Simplified Endurance Function Model. Advances in Mechanical Engineering. 2013. Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-482533
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-482533