Research On Prediction Of Residual Strength For Notched Composite Laminates

With the advanced composite materials being widely used in aeronautic structures, there are various notches in structures. It is important to investigate on the structure and the damage theory of notched composite laminates.It is more practical and important to research on residual strength for notched composite laminates after low cycle fatigue load.In this paper, the research methods of residual strength of notched laminates prediction are represented. Based on the macroscopical phenomenological theory, a hole-edge stress method of predicting the residual strength of notched laminates is established. The theory contains two parts, i.e. analysis of the stress distribution of notched laminates and fatigue stress fracture criterion. The method is simple and easy to apply. However, because some parameters in the model need to be fitted on the base of experiment, it can not be wildely used.In order to improve predicted capacity of residual strength, a progressive damage method of predicting the residual strength notched laminates is established, which is based on the theory of unidirectional ply. The equations of virtual work are deduced to analyze stress-strain states under static and fatigue loading conditions. The three-dimensional failure criteria are introduced into the method to detect damage for diverse damage modes. The composite material degradation models are introduced into the method to degrade the material property. Based on the software of ANSYS, additional program code is written using APDL language, which provides a better technique platform for the prediction the residual strength of notched laminates.Finally, the residual strength of notched composite laminates under fatigue loading conditions is predicted by using the fatigue progressive damage method and the hole-edge stress method. The results show that the residual strength increases after fatigue loading for the notched laminates. A good agreement is found between the predict results and the experiment.