Inverse parameter identification of n-segmented multilinear cohesive laws using parametric finite element modeling

被引:32
作者
Jensen, S. M. [1 ]
Martos, M. J. [1 ]
Lindgaard, E. [1 ]
Bak, B. L., V [1 ]
机构
[1] Aalborg Univ, Dept Mat & Prod, Fibigerstr 16, DK-9220 Aalborg, Denmark
关键词
Delamination; Large scale bridging; Experimental characterization of cohesive laws; Inverse finite element modelling; Gradient-based optimization; FRACTURE PROPERTIES; BRIDGING TRACTIONS; ZONE MODEL; DELAMINATION; COMPOSITES; BEHAVIOR; FAILURE;
D O I
10.1016/j.compstruct.2019.111074
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
Delamination in laminated composites are efficiently modelled with the cohesive zone model (CZM). The shape of the cohesive law becomes important when simulating delamination in material systems experiencing large scale fiber bridging, as several competing damage mechanisms occurs in the fracture process zone at multiple length scales. For this purpose a multilinear cohesive law has recently been developed in [1], which readily can be adapted to a variety of shapes. However, a key challenge in applying such cohesive laws is their model calibration, i.e. identification of parameters that define the shape of the cohesive law. In this work, a new methodology for experimental characterization of multilinear cohesive laws is proposed. The methodology is an inverse approach, which identifies cohesive laws by iteratively varying cohesive zone parameters using a gradient-based optimization scheme to minimize the error in structural response between a fracture mechanical experiment and a parametric finite element model. The method is demonstrated on a moment loaded double cantilever beam (DCB) specimen made of unidirectional glass-epoxy showing large-scale fiber bridging. Multilinear cohesive laws are characterized which result in an excellent agreement between the finite element simulation and the experiment. The results and sensitivity studies demonstrate the accuracy and robustness of the proposed methodology, even for a large number of design variables in the optimization problem.
引用
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页数:12
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