4.7 Article

LEFM crack path models evaluation under proportional and non-proportional load in low carbon steels using digital image correlation data

Journal

INTERNATIONAL JOURNAL OF FATIGUE
Volume 156, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.ijfatigue.2021.106687

Keywords

Crack path; LEFM; Non-proportional loading; Mixed-mode loading

Funding

  1. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior Brasil (CAPES)

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Crack path models were evaluated using experimentally obtained stress intensity factor (SIF) data for proportional and non-proportional loading conditions on plane and tubular ductile steel samples. While most models failed to match the experimental results for non-proportional loading, the strain energy density criterion (SED) showed a good agreement. The discrepancy can be attributed to the origins of the SIF ranges, which take into account accumulated plasticity and shielding due to crack roughness, as well as the non-proportionality of the mixed-mode applied load. The successful matching with SED is attributed to the inclusion of mode III and the specific evaluation method used. A comparison of computational cost for the different models suggests the need to consider the type of load and computing time in crack path model selection.
Crack path models were evaluated with experimentally obtained SIF for proportional and non-proportional loading for plane and tubular ductile steel samples. For non-proportional loading, most of the models had trouble matching experimental results, but the strain energ y density criterion (SED). The difference may be attributed to the origin of the SIF ranges, which account for accumulated plasticity and shielding by crack roughness, and to the non-proportionality of the mixed-mode applied load. The matching in the SED is attributed to the inclusion of mode III and the w a y it is evaluated. A comparison of computational cost for the models is made, suggesting that the type of load and the computing time may need to be included in the CP model selection.

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