Experimental evaluation of effective stress intensity factor using thermoelastic stress analysis and digital image correlation
Source abstract
Abstract
During the last decades, the debate over the mechanisms governing fatigue crack shielding has been mainly focused on demonstrating the existence of fatigue crack closure and the difficulties on quantifying the induced stress during crack propagation. Hence, most adopted experimental methods have been based on the direct or indirect measurement of contact loads between crack surfaces as the crack starts closing. Nevertheless, these methods depend on many factors sometime difficult to control, which has contributed to question their reliability by many authors. For this reason, two modern well established, full-field, non-contact experimental techniques, namely Thermoelastic Stress Analysis (TSA) and 2D Digital Image Correlation (2D-DIC), have been analysed to evaluate the influence of crack shielding during fatigue experiments conducted on two aluminium alloys (Al2024-T3 and Al7050) tested at different stress ratios. In the particular case of TSA, the technique appears to have a great potential in the evaluation of fatigue crack shielding since crack tip events are inferred directly from the temperature changes occurring at the crack tip rather than from remote data. Experimental data from both techniques have been employed in combination with two different mathematical models based on Muskhelishvili’s complex potentials to infer the effective range of stress intensity factor. Results from both techniques agree quite well, showing a variation in the stress intensity factor range as the R-ratio changes from 0.1 to 0.5 and illustrating the potential ability of both techniques to account for the shielding effect due to crack closure.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Fatigue and fracture mechanics · Structural Health Monitoring Techniques · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (28)
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