Finite Element Analysis of Residual Stress Distribution Patterns of Prestressed Composites Considering Interphases

Abstract

New finite element analysis procedures are developed in this study to obtain the precise stress distribution patterns of prestressed composites. Within the FEM procedures, an equivalent thermal method is modified to realize the prestress application, and a multi-step methodology is developed to consider coupling effects of polymer curing and prestress application. Thereafter, the effects of interphases’ properties, including the elastic modulus and coefficient of thermal expansion (CTE), on the stress distribution patterns are revealed. Analytical methods for residual stress prediction are modified in this study to demonstrate the finite element analysis procedures. From the residual stress results, it is found that the increase in the prestress level tends to contribute to the initiation of interphase debonding. The increase in the elastic modulus or CTE of the interphase results in very large circumferential and axial stress values appearing in the interphase. When the elastic modulus in the interphase is heterogeneous, the predicted stress values in the fiber and matrix are similar to the results predicted with the equivalent elastic modulus of the interphases. However, the heterogeneous elastic modulus results in serious circumferential and axial stress gradients in the interphase.

Comprehensive Composite Mould Filling Pattern Dataset for Process Modelling and Prediction

“…However, the high costs associated with experimental investigations promote the development of alternative approaches. In the literature, efforts in process evaluation and modelling predominantly fall into the following three main fields of research: theoretical modelling (e.g., Density Functional Theory [45] and Darcy's Law [46]), physics-based modelling (e.g., Finite Element method [47,48]), and data-driven modelling (e.g., Artificial Neural Network [49,50]). While theoretical and physics-based modelling approaches are renowned for their accuracy, these methods often demand high computational resources to evaluate.…”