Computational Physics Group

   In this work, we develop an image-based material testing approach using micro-computed tomography to understand the influence of microstructure and local damage phenomenon on the effective mechanical response of rubber-glass bead composites. Furthermore, a nondestructive, three-dimensional image-based analysis protocol which provides high fidelity of sample testing and data assessment has been established. An investigation was performed on various compositions of silicone rubber reinforced with silica particles. In situ compression experiments were used to study how the microscale damage (void creation from debonding) develops and evolves in the context of four primary studies: (i) effect of particle volume fraction, (ii) effect of particle diameter, (iii) local damage phenomena and its evolution (incremental loading/unloading), and (iv) effect of surface treatments on bonding characteristics. A detailed statistical analysis of the evolution of structural features through robust image processing strategies at various stages of loading was conducted. The rich data analysis collected from the experimental studies offers an understanding of the complex phenomena attributing to the material's macro and microscopic response to loading. Altogether, this framework results in the development of microstructure-statistics-property relations. Furthermore, the mechanical and morphological response of non-linear viscoelastic materials subjected to uniaxial compression is investigated.

Conclusions

    In this work, we present an image-based materials testing approach using microcomputed tomography to observe void formation and its evolution while varying the microstructure of rubber-glass bead composites. A material testing methodology is developed resulting in precise and reliable force-displacement data using the in situ material testing stage.
   Visualization and analysis software is used to determine multiscale coupling between the morphological and mechanical response of the composites when subjected to uniaxial compression loading. Microstructure-statistics-property relations are established. We test multiple samples to ensure repeatability and use large data sets for the statistical calculations representing the amount of microscale damage. The developed image processing pipeline accurately identifies particles and voids which are critical for material response assessment.
   The investigation of varied microstructure's effects on microscale damage can be summarised as follows. When varying the particle volume fraction, the void growth saturates with higher filled composites despite having different material bulk responses to loading. When varying the particle diameter a non-monotonic like trend in void content is observed, a result of chain-like clusters that reduce stress concentrations in the largest particle diameter composite. When studying void evolution, highly nonlinear void growth is observed with each incremental loading step. Lastly, comparisons of strong and weak bonding agents used for particle matrix interfacial bonding results in 5x increases in void sizes.
   In addition to the fundamental understanding, these experiments serve as validation data sets for multiscale modelling approaches. The constitutive model development, validation, and co-designed simulations and experiments are a natural next step. This analysis and framework yields new insight of particulate composite materials required for optimal material design. Furthermore, the novel, thorough, and reliable experimental and image processing pipelines are applicable to other material systems.