Stochastic Progressive Damage Process in Thick Composites: DIC-Based Experimental Characterization

Abstract

This study is focused on development of experimental capabilities to (a) understand and (b) quantify progressive damage processes (PDP) in thick composite structures, as well as (c) generate outputs sufficient for confident validation of corresponding existing or future modeling solutions. It is proposed to apply quasi-2D statements of analysis to monitor detailed through-thickness PDP of considered materials and/or structural elements, especially, with thick lay-ups and relatively complex patterns of inter- and cross-laminar damage. Thus, the Digital Image Correlation (DIC) technique is fully applicable to quantify processes of crack initiation, growth and networking. An additional key focus of this study is the assessment of the inevitable randomness of PDP, which is often not captured in more traditional experimental implementations. It aims to understand the actual multi-step nature of stochastic PDP and develop efficient methods for their statistical characterization. Demonstration of the capabilities is considered on representative examples of generic laminated carbon fiber polymer matrix composites with different lay-ups and thicknesses. It is shown that the proposed DIC-based implementation provides convenient yet accurate assessment of the stochastic nature of PDP. Details of the implementation and statistical post-processing are discussed in detail.