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Journal of Shanghai University >

2022 , Vol. 28 >Issue 3: 440 - 450

DOI: https://doi.org/10.12066/j.issn.1007-2861.2382

Data Collection, Database and Data Processing

Ensemble learning of polypropylene-composite aging data

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  • 1. School of Computer Science and Engineering, Shanghai University, Shanghai 200444, China
    2. Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
    3. Research Center of Nanoscience and Nanotechnology, College of Sciences, Shanghai University, Shanghai 200444, China
    4. Center of Materials Informatics and Data Science, Materials Genome Institute, Shanghai University, Shanghai 200444, China

Received date: 2022-03-26

  Online published: 2022-05-27

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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

Cite this article

WU Xing, GAO Jin, DING Peng . Ensemble learning of polypropylene-composite aging data[J]. Journal of Shanghai University, 2022 , 28(3) : 440 -450 . DOI: 10.12066/j.issn.1007-2861.2382

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