Abstract:

Aging experiments conducted on polypropylene composites have long durations, and a limited number of samples can be collected in a single experiment. As a result, traditional machine-learning approaches have a low prediction accuracy. To address these issues, we present an ensemble learning prediction based on virtual sample generation (VSG). To generate valid virtual samples of aging data for polypropylene composites, we first adopted the Gaussian mixed model (GMM) method and then used the generated data set to build an ensemble-learning prediction model comprising the random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) algorithms. The LightGBM and CatBoost algorithms in the ensemble learning model demonstrate the best performance on the test data; the mean square errors are 0.001 3 and 0.000 1, respectively, which are 0.4 and 0.2 higher than those of the RF algorithm and XGBoost algorithm, respectively. This study's aging VSG and ensemble learning approach for polypropylene composites can not only successfully overcome the long experimental times and insufficient number of data samples acquired in a single experiment but outperforms a single machine-learning algorithm.

Key words: polypropylene composites, material aging, ensemble learning, Gaussian mixture model