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| Naji, M. |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Kočí, Jan | Prague |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Rančić, M. |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Goncalves, A.
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Publications (4/4 displayed)
- 2022Fatigue crack growth modelling by means of the strain energy density-based Huffman model considering the residual stress effectcitations
- 2021Signal amplification of fiber integrated X-ray detector and energy independencecitations
- 2021Low-cycle fatigue modelling supported by strain energy density-based Huffman model considering the variability of dislocation densitycitations
- 2013Thermal stability and thermoelectric properties of CuxAs40−xTe60−ySey semiconducting glassescitations
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article
Low-cycle fatigue modelling supported by strain energy density-based Huffman model considering the variability of dislocation density
Abstract
The fatigue crack initiation and propagation phases have been widely studied by the scientific community. There are several models to describe low-cycle fatigue behaviour based on strain damage criteria, but the most widely used is the Coffin-Manson-Morrow relationship, normally used for the fatigue crack initiation modelling. In addition, strain-life models based on hardness measurements and monotonic properties of metals have also been suggested. There are also integrated fatigue models that describe both the fatigue crack initiation and propagation phases, such as the UniGrow, Huffman, Peeker, among others, where the concept of successive crack re initializations (increments) based on local approaches is adopted. In this paper, the low-cycle fatigue modelling based on Huffman approach using the strain energy density and considering dislocations density is investigated and discussed. For this, various methodologies to evaluating low-cycle fatigue strength based on Huffman approach and exploring different dislocation density parameters are suggested: (i) critical dislocation density driven by the highest strain amplitude; (ii) the mean value of the dislocation density of the available experimental fatigue data and, (iii) Monte Carlo (MC) stochastic prediction considering the variability of dislocation density and the cyclic strain hardening coefficient. Besides, the Monte Carlo stochastic simulations for obtaining the strain-life parameters, fatigue strength and ductility coefficients, it allows the generation of probabilistic fields for the low-cycle fatigue behaviour of metals. In this research, the experimental fatigue data of 1050, 6061-T651, and AlMgSi0.8 aluminium alloys are used to apply the suggested methodologies. A comparison between the experimental fatigue data and strain-life curves based on various suggested methodologies is made.