Application of digital image correlation and Williams series approximation to characterize mode I stress intensity factor; Acta Mechanica; Vol. 233, iss. 12
| Parent link: | Acta Mechanica.— .— New York: Springer Science+Business Media LLC. Vol. 233, iss. 12.— 2022.— P. 5089–5104 |
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| Other Authors: | , , , |
| Summary: | Title screen Existing methods of stress intensity factor (SIF) estimation are commonly based on the contact measurements of crack length with subsequent computation at taking into account the specimen geometry and loading process. The paper aims at the enhancement and verification of the non-contact and easily implemented technique for the assessment of mode I SIF during mechanical tests. Displacement fields constructed by digital image correlation (DIC) were approximated using Williams series in order to determine precise crack tip coordinates and proceed to the computation of stress intensity factor (SIF). The approximation algorithm was tested on linear-elastic and elastic–plastic ABAQUS models under various stress levels and the different Williams series term number. Besides numerical modeling of SIF, the algorithm was tested through assessment of crack propagation parameters under fatigue crack growth with the use of coarse- and fine-grained titanium samples. For the crack length range a/W ≤ 0.3, the approximation algorithm using the Williams series may be employed for estimating crack tip coordinates (crack length) and mode I stress intensity factor with satisfactory level of accuracy. The results of the study appeared to hold the promise of automated non-contact measurements of the crack length as well as stress intensity factor under fatigue crack propagation by employing DIC and appropriate high-performance real-time computing Текстовый файл AM_Agreement |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/s00707-022-03374-5 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=685022 |