Cyclic plasticity model for fatigue with softening behaviour below macroscopic yielding
Experimental observations have evidenced the insufficiency of yield surface definition via monotonic loading to guarantee adequate description of material behaviour under fatigue tests. Therefore, mechanical property characterisation through simple experimental tests such as uniaxial monotonic exten...
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Veröffentlicht in: | Materials & design 2019-03, Vol.165, p.107573, Article 107573 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Experimental observations have evidenced the insufficiency of yield surface definition via monotonic loading to guarantee adequate description of material behaviour under fatigue tests. Therefore, mechanical property characterisation through simple experimental tests such as uniaxial monotonic extension/compression is favoured, considering different responses due to cyclic loading. Herein, a phenomenological cyclic plasticity model is formulated to capture the correct generation of plastic deformation under cyclic loading conditions, even for stress states lower than the yield stress. The unconventional extended subloading surface model is modified to include an additional internal variable describing the strain-softening phenomenon typical of low- to high-cycle fatigue problems. Implicit integration strategies, in the form of the cutting-plane and closest projection point return mapping algorithms, are implemented to reduce the computation time. The solution accuracies are compared to that of a forward Euler integration scheme, and exhibit fast and accurate performance.
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•A constitutive model of the softening response of metals is developed.•This model captures behaviours under sub-macroscopic yield stress cyclic loading.•The total/net plastic strain range is accurately evaluated.•Fully/incomplete implicit constitutive-equation integration schemes are used.•The model is validated for SM490 steel under fully reversed uniaxial cyclic loading. |
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ISSN: | 0264-1275 1873-4197 |
DOI: | 10.1016/j.matdes.2018.107573 |