Micro-computed tomography based experimental investigation of micro- and macro-mechanical response of particulate composites with void growth

ABSTRACT

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.

KEYWORDS:

• Micro-computed tomography
• nondestructive material testing
• in situ analysis
• void evolution
• statistical characterisation
• image-processing

Acknowledgments

We gratefully acknowledge Dr. A. Gillman for his numerous contributions to the development of the experimental procedures.

Disclosure statement

The authors have no competing interests.

Additional information

Funding

This work was supported by the Department of Energy, National Nuclear Security Administration, under the award number DE-NA0002377 as part of the Predictive Science Academic Alliance Program II.