Understanding Damage Mechanics in Advanced Fiber-Reinforced Composite Materials via Digital Image Correlation and Discontinuous Finite Element Method

University Of South Carolina At Columbia, Columbia SC

Investigators

Abstract

Though advanced fiber-reinforced polymer matrix composites have been under development for decades, their use in load-bearing structures is far more recent. The reason for such reticence in their use is readily seen in news stories: structural failure of composite components, catastrophic collapse of deep-sea submersibles, including the recent Titan disaster, etc. Composite components are subject to damage that affects strength, fatigue resistance, and durability— issues that continue to this day. As a first step in addressing these challenges, the primary research objective of this award is to develop an experimental-computational framework for a methodic characterization of damage magnitude as well as mechanisms in the emerging field of mesostructured composites. Knowledge developed from this effort would look to enable the development of more accurate damage evolution models while reducing the time-intensive design and certification timeline of lightweight, damage-tolerant, and energy-efficient composites for transportation, defense, and energy applications. In addition, an educational module based on this research will be incorporated into the courses for undergraduate and graduate students to prepare them with critical skills in the mechanics of composite materials. In this research project, a combined digital image correlation (DIC) and discontinuous finite element method (DFEM) approach will be developed to determine damage and fracture parameters from experiments for a range of mesostructured composites. A DFEM will be formulated to solve an inverted form of the boundary value problem with spatially varying material properties and damage as the unknown, where full-field displacements and strains obtained using DIC will be used as input, to reconstruct the full-field evolution of material properties and damage. Deep neural network models will be used to assess the influence of material mesostructure on damage evolution by using the datasets produced by the DIC-DFEM approach to predict full-field damage evolution. Experimental validation will be performed on additively manufactured mesostructured composites. This research has the potential to significantly advance the field of experimental/computational mechanics for design and characterization of spatially heterogeneous materials and, in the process, provide a capability to drastically reduce the number of experiments required to characterize damage evolution and determine damage laws. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.