Building Machine Learning Models on Thermosetting Resin Composite Materials toward the Prediction of Physical Characteristics

Abstract

In this study, we constructed machine learning models for predicting the relative permittivity ( ε ) and dielectric loss tangent (tanδ), which are important for the industrial application of thermosetting resin composites, using our own experimental data. We adopted a wide range of methods, including gradient boosting decision tree (GBDT) algorithms, which have been