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Abstract
This work studies the Voce–Chaboche (V–C) material model parameter optimization for high-strength steel welded joints subjected to cyclic loading. The model parameters of each material zone in a S690 steel butt-welded joint were determined using an optimization algorithm based on the Newton trust region (NTR) method and an accumulated true strain parameter. The model parameters were fitted to stress–strain histories from uniaxial strain-controlled cyclic tests. To validate the model, fully-reversed variable amplitude fatigue experiments were performed under load control. The experimental results were then compared to numerical results from a finite element analysis. When the elastic modulus is optimized as a V–C parameter, the results indicate that the V–C model slightly underestimates the strain range, leading to conservative fatigue life estimates. However, the results can be improved by using an elastic modulus obtained experimentally. In this case, the resulting material model slightly overestimates the strain range, leading to a non-conservative, but more accurate, fatigue life estimation. It can be concluded that the NTR-based accumulated true strain approach successfully determined the V–C model parameters for different material zones in the welded joint, and closely estimated the strain range and the fatigue life for a variable amplitude load history.
Original language | English |
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Pages (from-to) | 818 |
Number of pages | 20 |
Journal | Journal of Marine Science and Engineering |
Volume | 10 |
Issue number | 6 |
DOIs | |
Publication status | Published - 14 Jun 2022 |
MoE publication type | A1 Journal article-refereed |
Keywords
- voce-chaboche
- welded joints
- high-strength steels
- fatigue life
- marine structures
- cyclic plasticity
Fingerprint
Dive into the research topics of 'Optimizing the Voce–Chaboche Model Parameters for Fatigue Life Estimation of Welded Joints in High-Strength Marine Structures'. Together they form a unique fingerprint.Projects
- 1 Finished
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-: Multiscale models for the fatigue assessment of engineering applications: experimental investigation of fatigue mechanisms at micro and nanoscale.
Gallo, P.
01/09/2019 → 31/12/2022
Project: Academy of Finland: Other research funding
Equipment
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i3 – Industry Innovation Infrastructure
Panu Sainio (Manager)
School of EngineeringFacility/equipment: Facility
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Solid Mechanics Laboratory (i3)
Pauli Lehto (Manager)
Department of Mechanical EngineeringFacility/equipment: Facility