Skip to main content

Prediction of Mechanical Properties of Polymer Materials Using Extreme Gradient Boosting on High Molecular Weight Polymers

  • Conference paper
  • First Online:
Complex, Intelligent, and Software Intensive Systems (CISIS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 772))

Included in the following conference series:

Abstract

The development of a new polymer materials depends on properties and it typically involves synthesizing a lot of compounds that finally becomes a new materials. This process is very expensive and take a long time. Therefore, the accurately prediction of the mechanical properties of polymer materials are required for understanding the rationale behind those predictions would be a valuable knowledge to the material studies. This work proposed the extreme gradient boosting to predict the mechanical properties of polymer materials. The model were developed, learn and the existing mechanical properties dataset. Then, the models were used to predict the mechanical properties for polymer materials. The predicted mechanical properties values were compared with the laboratory experimental results to validate the models. The experiment results have shown that the model could significantly enhance to predict the mechanical properties of polymer materials. In which way, this work could solve the problem of the polymer material and improve the efficiency of prediction obviously. This work evaluated the model with the logarithmic loss function, and the random forest classifier is used to test the model with all the features. Furthermore, this study enhance to reduce both time and cost of polymer material development. Even though, the accuracy of the predictions are not high enough, it has caused the insufficient number of sample in the dataset and the collection of data is widely scattered.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Roy, N.K., Potter, W.D., Landau, D.P.: Polymer property prediction and optimization using neural networks. IEEE Trans. Neural Netw. 17(4), 1001–1014 (2006)

    Article  Google Scholar 

  2. Khanam, P.N., AlMaadeed, M.A., AlMaadeed, S., Kunhoth, S., Ouederni, M., Sun, D., Hamilton, A., Jones, E.H., Mayoral, B.: Optimization and prediction of mechanical and thermal properties of graphene/LLDPE nanocomposites by using artificial neural networks. Int. J. Polym. Sci. 2016(01), 1–15 (2016)

    Article  Google Scholar 

  3. Li, M., Wan, Y., Wang, W.: Prediction of mechanical properties for defective monolayer MOS2 with single molybdenum vacancy defects using molecular dynamics simulations. In: 2017 IEEE 17th International Conference on Nanotechnology (IEEE-NANO), pp. 9–12, July 2017

    Google Scholar 

  4. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  Google Scholar 

  5. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)

    Article  Google Scholar 

  6. Mayrink, V., Hippert, H.S.: A hybrid method using exponential smoothing and gradient boosting for electrical short-term load forecasting. In: 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1–6, November 2016

    Google Scholar 

  7. Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)

    Google Scholar 

  8. Touzani, S., Granderson, J., Fernandes, S.: Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy Build. 158, 1533–1543 (2018)

    Article  Google Scholar 

  9. Breiman, L.: Arcing the edge. Technical report, Statistics Department, University of California at Berkeley (1997)

    Google Scholar 

  10. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, New York, NY, USA, pp. 785–794. ACM (2016)

    Google Scholar 

  11. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28, 2000 (1998)

    MathSciNet  MATH  Google Scholar 

  12. Czichos, H., Saito, T., Smith, L.: Springer Handbook of Materials Measurement Methods. Springer, Heidelberg (2007)

    Google Scholar 

  13. Hall, C.M.: Polymer materials : an introduction for technologists and scientists, 2nd edn. Macmillan, Basingstoke (1989)

    Google Scholar 

  14. Granderson, J., Price, P.N., Jump, D., Addy, N., Sohn, M.D.: Automated measurement and verification: performance of public domain whole-building electric baseline models. Appl. Energy 144, 106–113 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manop Phankokkruad .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Phankokkruad, M., Wacharawichanant, S. (2019). Prediction of Mechanical Properties of Polymer Materials Using Extreme Gradient Boosting on High Molecular Weight Polymers. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_33

Download citation

Publish with us

Policies and ethics