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Experimental and numerical modeling approach for thermomechanical low cycle fatigue analysis of cyclically non-stabilized steels
The widely used fatigue life prediction models, such as the Coffin–Manson model or S–N curve related models are based on the assumption that the response of a material experiencing low cycle fatigue loading is stabilized during some period. However, for many materials such a stabilized state is hard...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374158/ https://www.ncbi.nlm.nih.gov/pubmed/34434736 http://dx.doi.org/10.1016/j.mex.2021.101213 |
Sumario: | The widely used fatigue life prediction models, such as the Coffin–Manson model or S–N curve related models are based on the assumption that the response of a material experiencing low cycle fatigue loading is stabilized during some period. However, for many materials such a stabilized state is hardly observed, and the activated mechanisms for cyclic hardening or softening depend on test conditions. In general, the selected test conditions (stress or strain control) should depend on the intended use of the obtained material data. If testing conditions do not correspond to the operation mode of the considered mechanical facilities, the above mentioned life prediction models will produce inaccurate results. Hence, selecting and identifying proper fatigue parameters, which would represent the state of a material during the whole fatigue life, is extremely important in reliability evaluation of structures. In the case of non-stabilizing steels, the common challenges are: • Selecting and performing a suitable set of experimental tests to recognize various aspects of the material behavior under low-cycle thermomechanical fatigue; • Adjusting a proper constitutive modelling, reflecting the real physical phenomena taking place in the material microstructure; • Effective numerical implementation and optimal parameter identification. |
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