Numerical Simulation of Local Electromechanical Response of Polymer Composites based on Image Processing

The random distribution, aggregation, and orientation characteristics of conductive fillers in polymer composite materials lead to biases in traditional numerical simulations based on the assumption of uniform distribution when predicting local electromechanical responses. This article aims to establish a digital model of real microstructures based on image processing, breaking through the empirical limitations of traditional methods. Firstly, using FE-SEM(Field emission-Scanning electron microscope) images, a three-dimensional realistic microstructure model is constructed through non local mean denoising, CLAHE(Contrast Limited Adaptive Histogram Equalization) enhancement, and SIFT-RANSAC(Scale-Invariant Feature Transform and Random Sample Consensus) stitching. Then, a strain conductivity model was established by combining DIC(Digital Image Correlation) four probe measurement, and the interfacial binding energy and critical displacement were extracted through molecular dynamics. Based on Kriging, construct a multi-scale response surface and develop ABAQUS UEL (User Element) electromechanical coupling unit to achieve staggered iterative strong coupling solution. The experimental results showed that the Pearson correlation coefficient between strain and conductivity mapping reached 0.87, and the Hausdorff distance predicted for crack propagation path was less than 5μm, verifying the model's ability to accurately predict local electromechanical response. Moreover, the parallel computing framework reduces simulation time from 58 minutes to 4.2 minutes, and improves convergence speed by 27%.In summary, this article provides a high-precision and efficient paradigm for the study of local electromechanical response of complex microstructure composite materials based on image processing numerical simulation methods.