Progressive damage behavior of composite L-stiffened structures with initial delamination defects under uniaxial compression: Experimental and numerical investigations

L-shaped stiffened composite panels inevitably produce delamination defects during manufacturing and service, which seriously affect the load-bearing performance of the structure. In the present work, a combination of experimental and numerical simulations was used to investigate the effect of initial delamination defects with three different shapes (lossless, elliptical, and circular) in the skin on the axial compression properties of carbon fiber L-shaped stiffened composite panels. Furthermore, to predict the damage initiation and evolution process of carbon fiber-reinforced polymer (CFRP) L-stiffened plates under compression loading, a progressive damage model (PDM) that combines the Hashin failure criterion and the cohesive zone model (CZM) is proposed. Also, a user-defined field variable (USDFLD) subroutine based on the Hashin failure criteria is developed in Abaqus software to study the initiation and progression of intra-laminar damages L-stiffened panels. Damage initiation and evolution process of skin delamination defects are predicted based on CZM. Finally, the finite element simulation results are in good agreement with the experimental measurements, which confirms the reliability of the finite element models (FEMs).

Automated summary

In this study, the effect of initial delamination defects on the axial compression properties of carbon fiber L-shaped stiffened composite panels was investigated using a combination of experimental and numerical simulations. A progressive damage model (PDM) was proposed to predict the damage initiation and evolution process, and a user-defined field variable (USDFLD) subroutine based on the Hashin failure criteria was developed to study the initiation and progression of intra-laminar damages. The results of finite element simulations were in good agreement with experimental measurements, confirming the reliability of the models.