Abstract
A new approach was put forward to identify the damage parameters of the shear modified GTN damage model proposed by Nahshon and Hutchinson (Eur J Mech Solid 27:10–17, 2008) by combining the artificial neural networks algorithm and small punch test. The factorial design method was used to analyze the influence of the parameters on the shape of load-displacement curve of small punch test. The less important parameters were set as empirical value and the significant factors were determined by an artificial neural networks model which was build up based on large amount of simulations of small punch tests with different levels of damage parameters values. The identified parameters were validated by small punch test simulations with different specimen thickness. The results show that the identified parameters of the shear modified GTN damage model are effective to characterize the mechanical behavior as well as the damage evolution and ductile failure of material during the process of small punch test. In addition, the applicability of the identified parameters in the tests with different stress condition were verified.
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Acknowledgements
The present research was supported by the National Natural Science Foundation of China under Grant No. 51105143 and Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ19E050008.
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Sun, Q., Lu, Y. & Chen, J. Identification of material parameters of a shear modified GTN damage model by small punch test. Int J Fract 222, 25–35 (2020). https://doi.org/10.1007/s10704-020-00428-4
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DOI: https://doi.org/10.1007/s10704-020-00428-4