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USAF Characteristic K Approach: A Robust Tool for Predicting Fatigue Crack Growth under Various Underload Spectra
Kushagra Tiwari1,2,3, Alankar Alankar1, R K Singh Raman2
1Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India.
This study shows the USAF Characteristic K method and Hartman-Schijve equation accurately predict small crack growth under varying underloads in aluminum alloys. This aids in evaluating component durability for limited life replacement.
Area of Science:
- Materials Science
- Mechanical Engineering
- Fracture Mechanics
Background:
- Evaluating component durability for limited life replacement requires accurate fracture mechanics tools.
- Understanding the impact of underloads on crack propagation is crucial for predicting component lifespan.
Purpose of the Study:
- To assess the predictive accuracy of the USAF 'Characteristic K' method combined with the Hartman-Schijve adaptation for small crack growth under various underload spectra.
- To validate this combined approach using experimental data for aluminum alloy AA7050-T7451.
Main Methods:
- Utilized the USAF 'Characteristic K' method and the Hartman-Schijve adaptation of the NASGRO crack growth formula.
- Analyzed published da/dN versus ΔK small crack growth data for five distinct underload spectra.
- Compared predicted crack growth curves with experimentally measured curves.
Main Results:
- The combined USAF 'Characteristic K' and Hartman-Schijve method demonstrated reasonable accuracy in predicting the impact of underloads on small crack propagation.
- Good agreement was observed between predicted and measured crack growth curves across all five examined underload spectra.
- This study is the first to highlight the efficacy of this specific combination for predicting small crack growth under diverse underload conditions.
Conclusions:
- The USAF 'Characteristic K' approach, when integrated with the Hartman-Schijve equation, provides a reliable tool for predicting small crack growth under various underload conditions.
- This predictive capability is valuable for assessing the durability and optimizing the design of components intended for limited life replacement.
- Further research can build upon these findings to refine predictive models in linear elastic fracture mechanics.
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