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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Structural Engineering and Mechanics, An Int'l Journal Structural Engineering and Mechanics, An Int'l Journal Vol.72 No.3
- 발행연도
- 2019.1
- 수록면
- 313 - 327 (15page)
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초록· 키워드
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Damage evolution in the form of void nucleation, propagation and coalescence is the primary cause that is responsible for the ductile failure of microalloyed steels. The Gurson-Tvergaard-Needleman (GTN) damage model has proven to be extremely robust for characterizing the microscopic damage behavior of ductile metals. Nonetheless, successful applications of the model on a given metal type are limited by the correct identification of damage parameters as well as the validation of the calculated void growth rate. The purpose of this study is two-fold. First, we aim to identify the damage parameters of the GTN model for Q345 steel (Chinese code), due to its extensive application in mechanical and civil industries in China. The identification of damage parameters is facilitated by the well-suited response surface methodology, followed by a complete analysis of variance for evaluating the statistical significance of the identified model. Second, taking notched Q345 cylinders as an example, finite element simulations implemented with the identified GTN model are performed in order to analyze their microscopic damage behavior. In particular, the void growth rate predicted from the simulations is successfully correlated with experimentally measured acoustic emissions. The quantitative correlation suggests that during the yielding stage the void growth rate increases linearly with the acoustic emissions, while in the strain-hardening and softening period the dependence becomes an exponential function. The combined experimental and finite element approach provides a means for validating simulated void growth rate against experimental measurements of acoustic emissions in microalloyed steels.
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등록된 정보가 없습니다.
참고문헌 (48)
참고문헌 신청
M. Abbasi / 2012 / Application of response surface methodology to drive GTN model parameters and determine the FLD of tailor welded blank / Computational Materials Science 53(1):368~376
Fethi Abbassi / 2013 / Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming / Materials & Design 45:605~615
Marwa Abdelrahman / 2015 / Classification of alkali–silica reaction damage using acoustic emission: A proof-of-concept study / Construction and Building Materials 95:406~413
Martin Abendroth / 2003 / Determination of deformation and failure properties of ductile materials by means of the small punch test and neural networks / Computational Materials Science 28(3-4):633~644
Aggelis, D. G. / 2016 / Acoustic Emission Analysis for NDE in Concrete / Springer
Fethi Abbassi / 2013 / Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming / Materials & Design 45:605~615
Marwa Abdelrahman / 2015 / Classification of alkali–silica reaction damage using acoustic emission: A proof-of-concept study / Construction and Building Materials 95:406~413
Martin Abendroth / 2003 / Determination of deformation and failure properties of ductile materials by means of the small punch test and neural networks / Computational Materials Science 28(3-4):633~644
Aggelis, D. G. / 2016 / Acoustic Emission Analysis for NDE in Concrete / Springer
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