Theoretical, data-driven, and experimental analyses on composites with various microstructures

Abstract

Composites are materials that combine two or more materials to achieve new material properties to overcome the limitations of single materials. It can be easily found at almost all kinds of industry, bio-materials and research for advanced materials. In this paper, I conducted a study on particle-reinforced composites, biomimetic composites, and random composites that represent the trend of composite re-search. The particle-reinforced composite material has been studied since the 1980s. It has been studied in a mathematical way based on micro-mechanical theory. In this study, a new homogenization method of hyperelastic-viscoelastic materials has been developed. I applied to ABAQUS USERMATERIAL, and it was verified by comparison with the representative volume element model. The staggered platelet composite, which is a kind of bio-inspired composite, has been actively studied since the 2000s. However, although toughness is the biggest advantage of this composite, it has been difficult to analyze. so it was difficult to interpret the toughness. In this study, the effective elastic modulus prediction and failure mode were predicted by analytical method, and the composite toughness was optimized based on simu-lation and experiment using 3D printer through Gaussian process and Markov Chain Monte Carlo, one of the data driven techniques. The random composite material, which is defined by randomly placing a hard material and a soft material in the form of a checker board, has no restrictions on the shape or orientation of the composite microstructure. In this study, prediction and optimization of nonlinear property values were performed using Convolution Neural Network (CNN) using simulation data until the material was completely destroyed.