Modeling the Internal Architecture of Composites. (December 2020) - Conde-Rodríguez, F.; García-Fernández, Á.L.; Torres, J.C.
Abstract: This paper introduces a flexible and easy to use method for designing complex composite heterogeneous materials. These materials feature two distinct phases called c o r e and m a t r i x that remain separate and distinct. Moreover, composite materials have an internal microarchitecture that have to be precisely modeled. All the microarchitecture examples that are shown in this paper have been modeled in the same way, without any particular case nor having to use different implementation strategies or changing the source code depending on the microarchitecture. The microarchitecture is modeled with a function that combines a material distribution function, which models the proportion of each phase at each point of the solid and determines the thickness of the core phase, and a cellular noise function based on distance fields that determines the shape, size, and distribution of the microarchitecture. Modeling the microarchitecture using two components gives our model great flexibility. In addition, it also allows to vary the size or thickness of the microarchitecture continuously inside the solid. With this method, it is possible to model complex composite materials in which the phases (core and matrix) are in turn other composites with two distinct phases. Another important advantage of this method is that a complex object consisting of several different parts made of different materials can be modeled as a single computational object, which is very suitable for editing or computing simulations Highlights: A framework for modeling the microarchitecture of material composites is described. The model and algorithms remain always the same, no matter the microarchitecture. Complex microarchitectures can be modeled (f.e. multi-layered or reinforced). Complex composite materials, made in turn of other composites, are allowed. The microarchitecture remains stable under modeling operations.