Computational Prediction Of Property Parameters For Composite Materials

Since composite appearing, due to having many excellent properties comparing with other general materials, it has abroad application in many fields. Thereinto, the most distinct characteristic -designable, make composite become the emphasis in the research of material science. Within those properties, effective stiffness coefficient and effective thermal expansion coefficient as key parameters in representing mechanics and physics property of composite have considerable influence for advanced composite material design and development. At the present time, research on predicting composite property through simulated microstructures in domestic is lacked. Basing on this status, the paper employed developed software-ProDesign to simulating composite microstructures of short-fiber reinforced composite samples and predicting effective stiffness coefficient and thermal expansion coefficient of the samples. The samples are supposed to be subjected to coupled boundary traction due to mechanical loading and thermal cycling. By applying a regression analysis method, the relationship between composite microstructures and effective stiffness coefficient and thermal expansion coefficient is explored.Author has found that the stiffness coefficient C11, C12, C44 of each composite sample increase as the volume fraction Vf increases and there is a remarkable relationship between them. Besides, for different samples, there are a group of remarkable and quantified relationships between stiffness coefficient of composites and the elastic properties and anisotropy ratio of constituents. But the average grain size D has little effect on the stiffness coefficient of the composites.For effective thermal expansion coefficient, author has found that the relationship between the effective thermal expansion coefficient a11, 22 ,33 of each composite sample and volume fraction Vf is not identical. There isn't remarkable relationship between them. But, for different samples, the same as the prediction of stiffness coefficient, there are a group of remarkable relationships between thermal expansion coefficient of composites and the thermal expansion coefficient, stiffness coefficient, anisotropy ratio of constituents. But the average grain size D also has little effect on the thermal expansion coefficient of the composites.