Study And Application Of Computational Model For Elasticity And Electrical Conductivity Of Particulate Composite Materials

Particle-reinforced composite materials have been widely used in various industrial fields due to their advantages of high strength,high wear resistance,and excellent conductivity.Developing high-performance particle-reinforced composite materials is of great significance for meeting the requirements of harsh environments.The properties of particle-reinforced composite materials are influenced by various microscopic factors such as inclusion shape,size,and distribution.In order to reconstruct the microscopic geometry model of particle-reinforced composite materials,a Fourier descriptor characterization method was proposed to study the relationship between different particle shape feature parameters and Fourier descriptors.Based on the microscopic structure,a prediction model for the macroscopic effective properties of particle-reinforced composite materials was proposed,which predicts the elastic and electrical properties（hereafter referred to as macroscopic effective properties）of the materials.The theoretical model was verified by experiments and finite element simulations.The following research results and innovations were achieved in this paper:（1）A reconstruction method of the microscopic geometry model of particle-reinforced composite materials was proposed,which considers the shape feature parameters of the inclusion phase in the geometric image reconstruction process.The Fourier descriptor method was used to characterize the microscopic particle contour,and the correlation between the shape feature parameters（particle aspect ratio and roundness）and the key Fourier descriptors（Fd₂、Fd₃和Fd₈）was established.Meanwhile,the contact determination method of particle contour boundary was studied.（2）A prediction model for the macroscopic effective properties of particle-reinforced composite materials based on the incremental inclusion method was proposed.The Eshelby tensor calculation expression of the inclusion particle was derived through the Green’s function method,and the theoretical method for exploring the relationship between the real microscopic structure and the macroscopic effective properties was established by introducing microscopic geometric feature variables.（3）A copper-chromium alloy particle-reinforced composite material was prepared,and its original microscopic image was obtained using scanning electron microscopy.By using the proposed microscopic geometry image reconstruction method and the theoretical method for calculating macroscopic effective properties,the relationship between macroscopic effective properties and microscopic structure of copper-chromium alloy was established,and the effectiveness of the prediction model was verified by experiments and finite element simulations.（4）A prediction system for the macroscopic effective properties of particle-reinforced composite materials was developed.The theoretical model wass introduced into the system as a theoretical basis.The prediction system provides a user-friendly interface and data visualization tools,which makes it easy for users to perform model building,parameter setting,and result analysis.Therefore,the system can be used to quickly and accurately predict material properties,and achieve the goal of designing and optimizing high-performance particle-reinforced composite materials.