XGBoost Regression Analysis of Dielectric Properties of Epoxy Resin with Inorganic Hybrid Nanofillers

Abstract

Our research described in this paper investigated the influence of varying weight percentages of a hybrid nano filler composed of titanium dioxide (TiO₂) and silicon dioxide (SiO₂) in an epoxy polymer composite. The dielectric properties were analyzed across a broadband frequency range. Additionally, X-ray diffraction (XRD) was employed to examine the structural changes and crystalline phases within the composite materials. The Influence of hybrid inorganic nano-fillers in the epoxy composite on the dielectric properties are discussed thoroughly in this report. To improve material design and prediction modeling, our research utilized machine learning algorithms, such as the XGBoost regressor. The aim was to assess the effectiveness of regression techniques in evaluating the material properties. By employing performance metrics, like R² score and RMSE, the tests could achieve accuracies ranging from 30% to 60%. Simulation results demonstrated that machine learning-based methods can significantly expedite the forecasting of the dielectric properties, i.e. dielectric constant and dielectric loss at intermediate frequencies, thereby saving both time and energy.

Keywords:

• Dielectric properties
• epoxy polymer composite
• hybrid nano filler
• machine learning
• XGBoost

Disclosure statement

No potential conflict of interest was reported by the author(s).