文献类型：期刊文献

英文题名：Identification of material parameters of a shear modified GTN damage model by small punch test

作者：Sun, Quan[1];Lu, Yebo[1];Chen, Jianjun[2]

机构：[1]Jiaxing Univ, Sch Mech & Elect Engn, Jiaxing 314001, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：222

期号：1-2

起止页码：25

外文期刊名：INTERNATIONAL JOURNAL OF FRACTURE

收录：;EI(收录号：20200608129482);WOS:【SCI-EXPANDED(收录号:WOS:000510349400001)】；

基金：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.

语种：英文

外文关键词：GTN model; Shear damage; Small punch test; Artificial neural networks

摘要：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.