Data-Driven Exploration of Polymer Processing Effects on the Mechanical Properties in Carbon Black-Reinforced Rubber Composites

Zi-Long Wan; Wan-Chen Zhao; Hao-Ke Qiu; Shu-Shuai Zhou; Si-Yuan Chen; Cui-Liu Fu; Xue-Yang Feng; Li-Jia Pan; Ke Wang; Tian-Cheng He; Yu-Ge Wang; Zhao-Yan Sun

doi:10.1007/s10118-024-3216-3

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Data-Driven Exploration of Polymer Processing Effects on the Mechanical Properties in Carbon Black-Reinforced Rubber Composites

RESEARCH ARTICLE | Updated：2024-12-03

- Data-Driven Exploration of Polymer Processing Effects on the Mechanical Properties in Carbon Black-Reinforced Rubber Composites
- Chinese Journal of Polymer Science Vol. 42, Issue 12, Pages: 2038-2047(2024)
- Affiliations：
  
  a.State Key Laboratory of Polymer Physics and Chemistry & Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
  b.School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, China
  c.College of Chemistry, Jilin University, Changchun 130012, China
  d.Xinjiang Laboratory of Phase Transitions and Microstructures in Condensed Matters, College of Physical Science and Technology, Yili Normal University, Yining 835000, China
- Author bio：
  
  zysun@ciac.ac.cn
- Funds：
- DOI：10.1007/s10118-024-3216-3
  CLC：
- Received：18 June 2024，
  
  Revised：2024-07-21，
  
  Accepted：03 August 2024，
  
  Online First：21 October 2024，
  
  Published：01 December 2024
- Accepted：
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Wan, Z. L.; Zhao, W. C.; Qiu, H. K.; Zhou, S. S.; Chen, S. Y.; Fu, C. L.; Feng, X. Y.; Pan, L. J.; Wang K.; He, T. C.; Wang, Y. G.; Sun, Z. Y. Data-driven exploration of polymer processing effects on the mechanical properties in carbon black-reinforced rubber composites. Chinese J. Polym. Sci. 2024, 42, 2038–2047

Zi-Long Wan, Wan-Chen Zhao, Hao-Ke Qiu, et al. Data-Driven Exploration of Polymer Processing Effects on the Mechanical Properties in Carbon Black-Reinforced Rubber Composites[J]. Chinese Journal of Polymer Science, 2024, 42(12): 2038-2047.
Wan, Z. L.; Zhao, W. C.; Qiu, H. K.; Zhou, S. S.; Chen, S. Y.; Fu, C. L.; Feng, X. Y.; Pan, L. J.; Wang K.; He, T. C.; Wang, Y. G.; Sun, Z. Y. Data-driven exploration of polymer processing effects on the mechanical properties in carbon black-reinforced rubber composites. Chinese J. Polym. Sci. 2024, 42, 2038–2047 DOI： 10.1007/s10118-024-3216-3.

Zi-Long Wan, Wan-Chen Zhao, Hao-Ke Qiu, et al. Data-Driven Exploration of Polymer Processing Effects on the Mechanical Properties in Carbon Black-Reinforced Rubber Composites[J]. Chinese Journal of Polymer Science, 2024, 42(12): 2038-2047. DOI： 10.1007/s10118-024-3216-3.

摘要

Schematic diagram of the Study. (1) Data Collecting: Conducting experiments and processing raw data for a comprehensive dataset. (2) Modeling: Training and comparing to obtain optimal DNN models

which are then interpreted with Shapley additive explanations (SHAP). (3) Predicting: Predicting the sample space to facilitate the design of composite processes.

Abstract

The performance and corresponding applications of polymer nanocomposites are highly dominated by the choice of base material

type of fillers

and the processing ways. Carbon black-filled rubber composites (CRC) exemplify this

playing a crucial role in various industries. However

due to the complex interplay between these factors and the resulting properties

a simple yet accurate model to predict the mechanical properties of CRC

considering different rubbers

fillers

and processing techniques

is highly desired. This study aims to predict the dispersion of fillers in CRC and forecast the resultant mechanical properties of CRC by leveraging machine learning. We selected various rubbers and carbon black fillers

conducted mixing and vulcanizing

and subsequently measured filler dispersion and tensile performance. Based on 215 experimental data points

we evaluated the performance of different machine learning models. Our findings indicate that the manually designed deep neural network (DNN) models achieved superior results

exhibiting the highest coefficient of determination (

) values (

0.95). Shapley additive explanations (SHAP) analysis of the DNN models revealed the intricate relationship between the properties of CRC and process parameters. Moreover

based on the robust predictive capabilities of the DNN models

we can recommend or optimize CRC fabrication process. This work provides valuable insights for employing machine learning in predicting polymer composite material properties and optimiz

ing the fabrication of high-performance CRC.

关键词

Keywords

references

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