Stochastic Finite Element Analysis Framework for Modelling Mechanical Properties of Particulate Modified Polymer...

Area of Science:

• Materials Science
• Polymer Engineering
• Computational Mechanics

Background:

• Polymers offer advantages like low density and corrosion resistance but often require enhanced mechanical, thermal, and electrical properties for demanding engineering uses.
• Particulate fillers are commonly incorporated into polymer matrices to improve material characteristics.
• Accurate prediction of composite properties is crucial for effective material design and application.

Purpose of the Study:

• To develop and validate a numerical modeling framework for predicting the properties of polymer composites with randomly distributed particulate fillers.
• To investigate the mechanical properties (elastic modulus, Poisson's ratio) and thermal expansion coefficient of composites with size-distributed spherical fillers.
• To compare the accuracy and efficiency of the proposed modeling approach against experimental data and analytical methods.

Main Methods:

• Stochastic finite element analysis was employed to create a numerical modeling framework.
• The framework was used to predict elastic modulus, Poisson's ratio, and thermal expansion coefficient for polymer composites.
• Model predictions were systematically compared with existing experimental data from the literature.

Main Results:

• The numerical model successfully predicted non-linear trends in composite properties, with elastic modulus predictions aligning well with experimental data scatter.
• Deviations between numerical and experimental Poisson's ratio were observed at filler volume fractions exceeding 0.15.
• The model demonstrated reduced effort and increased accuracy compared to traditional experimental and analytical techniques.

Conclusions:

• The developed stochastic finite element analysis framework provides a reliable tool for predicting the properties of polymer composites.
• The model's accuracy is promising for material design, though limitations exist at higher filler concentrations due to potential morphology changes.
• This computational approach offers a valuable alternative to extensive experimental testing for optimizing polymer composite performance.