Structural Reliability Prediction Using Acoustic Emission-Based Modeling of Fatigue Crack Growth
| Autor: | Christine M. Sauerbrunn, Azadeh Keshtgar, Mohammad Modarres |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Materials science
Alloy 02 engineering and technology engineering.material lcsh:Technology lcsh:Chemistry structural integrity 0203 mechanical engineering Nondestructive testing General Materials Science Instrumentation uncertainty analysis lcsh:QH301-705.5 Uncertainty analysis Fluid Flow and Transfer Processes reliability nondestructive testing business.industry lcsh:T Process Chemistry and Technology General Engineering Linear model Titanium alloy Structural engineering Paris' law 021001 nanoscience & nanotechnology lcsh:QC1-999 Computer Science Applications fatigue life prediction 020303 mechanical engineering & transports Acoustic emission lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 engineering 0210 nano-technology Bayesian linear regression business acoustic emission crack growth lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 8, Iss 8, p 1225 (2018) Applied Sciences Volume 8 Issue 8 |
| ISSN: | 2076-3417 |
| Popis: | In this paper, AE signals collected during fatigue crack-growth of aluminum and titanium alloys (Al7075-T6 and Ti-6Al-4V) were analyzed and compared. Both the aluminum and titanium alloys used in this study are prevalent materials in aerospace structures, which prompted this current investigation. The effect of different loading conditions and loading frequencies on a proposed AE-based crack-growth model were studied. The results suggest that the linear model used to relate AE and crack growth is independent of the loading condition and loading frequency. Also, the model initially developed for the aluminum alloy proves to hold true for the titanium alloy while, as expected, the model parameters are material dependent. The model parameters and their distributions were estimated using a Bayesian regression technique. The proposed model was developed and validated based on post processing and Bayesian analysis of experimental data. |
| Databáze: | OpenAIRE |
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