ABSTRACT
ABSTRACT: The rubber materials of tires contain nano-fillers i.e. carbon black and silica for the improvement of tire performances. The mechanical properties of filled rubber depend on the morphology of fillers. In this work Multi-Objective Design Exploration (MODE) is applied to material design of filled rubbers to get the information between mechanical properties and morphological design variables. A multi-scale random model based on a Poisson point process is used to generate a simulation model of filled rubber, and FFT (Fast Fourier Transform) based scheme is applied to solve large-scale dynamic viscoelastic simulation. To get big data including Pareto solution for data mining, multi-objective genetic algorithm are conducted on TSUBAME, supercomputer at Tokyo Institute of Technology. Data mining is employed to highlight properties-sensitive features in the microstructure. First, the volume fraction of bound rubber plays a major role in the material design of filled rubbers. Second, the radius of aggregates contributes to mechanical properties of filled rubbers.
