Microscopic finite element modeling and fracture properties of particulate filled polymer composites
Source abstract
Abstract
Abstract Based on the microstructure of PFPC, this article proposes a grading construction model that conforms to the normal distribution law by observing the distribution characteristics of the particles. A RVE random placement modeling program is developed using Python language, and an RVE model containing irregular polygonal particles is constructed, providing an effective new method for microfinite element modeling of PFPC. The validity of the model is proved by comparing the simulation results of the composites stress–strain behavior and the experimental results. The effects of particle volume fraction and particle size distribution on crack extension are analyzed using this microfinite element model, and their influence on the fracture properties of the composites is discussed. The results show that the initiation location and propagation direction of cracks in the material are influenced by the particle size, size distribution, and loading conditions. With the increase of particle volume content, the fracture properties of particles increase first and then decreased. When the volume fraction of particles is certain, the fracture property decreases with the increase of the mean value of particle size. The fracture properties of the composites can be improved by regulating the combination of particles with appropriate SD of particle size. Highlights Developed a program for random particle placement using Python language. Established an RVE model incorporating irregular polygonal particles. Simulated the initiation and propagation process of cracks in composites. Studied particle volume fraction's impact on PFPC fracture properties. Studied particle size distribution's effect on PFPC fracture properties.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Polymer Nanocomposites and Properties · Epoxy Resin Curing Processes · Polymer crystallization and properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Materials Engineering Jiangsu University of Technology Changzhou Jiangsu China
Jiangsu Branch of China Academy of Machinery Science and Technology Group Co., Ltd. Changzhou Jiangsu China
Funding
National Natural Science Foundation of China
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