Skip to main content
SpringerLink
Log in

Application of artificial neural networks to predict mechanical behaviour of cork-rubber composites

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Cork-rubber composites are materials that can be used in different applications ranging from industry to construction sectors. One of its applications is building vibration isolation. One of the principal requirements in the design of these materials is the capacity of supporting static compressive loads. In this study, based on the shape factor and hardness Shore A of a cork-rubber block, the application of artificial neural networks methodology, for the prediction of apparent compression modulus, was evaluated. A large database with compression test results was used for the training and testing of neural networks. Using the early stopping method, several studies were performed regarding artificial neural networks architecture and training: the number of neurons on the hidden layer, change of training parameters and dataset division method. Neural networks with three neurons on the hidden layer proved to be more efficient without overfitting and presented a good correspondence between predicted and measured results. The use of different methods to divide the dataset through different development stages also influences the model performance. A comparison of performance with other models available in the literature revealed that the chosen ANN model proved, in most cases, to have a better performance regarding these materials.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Mestre A, Vogtlander J (2013) Eco-efficient value creation of cork products: an LCA-based method for design intervention. J Clean Prod 57:101–114. https://doi.org/10.1016/j.jclepro.2013.04.023

    Article  Google Scholar 

  2. Gil L (2009) Cork composites: a review. Materials 2:776–789. https://doi.org/10.3390/ma2030776

    Article  Google Scholar 

  3. Silva SP, Sabino MA, Fernandes EM et al (2005) Cork: properties, capabilities and applications. Int Mater Rev 50:345–365. https://doi.org/10.1179/174328005X41168

    Article  Google Scholar 

  4. Gent AN (1958) On the relation between indentation hardness and Young’s modulus. Rubber Chem Technol 31:896–906

    Article  Google Scholar 

  5. BS 903 Methods of testing vulcanised rubber Part 19 (1950) and Part A7 (1957)

  6. Qi HJ, Joyce K, Boyce MC (2003) Durometer hardness and the stress-strain behavior of elastomeric materials. Rubber Chem Technol 76:419–435. https://doi.org/10.5254/1.3547752

    Article  Google Scholar 

  7. Kunz J, Studer M (2006) Determining the modulus of elasticity in compression via the shore a hardness. Kunststoffe Int 96:92–94

    Google Scholar 

  8. Gent AN (2012) Engineering with rubber: How to design rubber components. Carl Hanser Verlag GmbH & Co, KG

    Book  Google Scholar 

  9. Fediuc DO, Budescu M, Fediuc V, Venghiac V-M (2013) Compression modulus of elastomers. Bul Inst Politeh Lasi Sect Constructii Arhitectura 59:157–166

    Google Scholar 

  10. Gent AN, Lindley PB (1959) The compression of bonded rubber blocks. Proc Inst Mech Eng 173:111–122. https://doi.org/10.1243/PIME

    Article  Google Scholar 

  11. Lindley PB (1979) Compression moduli for blocks of soft elastic material bonded to rigid end plates. Anal Eng Design 14:11–16

    Article  Google Scholar 

  12. Horton JM, Tupholme GE, Gover MJC (2002) Axial loading of bonded rubber blocks. J Appl Mech 69:836–843. https://doi.org/10.1115/1.1507769

    Article  MATH  Google Scholar 

  13. Williams JG, Gamonpilas C (2008) Using the simple compression test to determine Young’s modulus, Poisson’s ratio and the Coulomb friction coefficient. Int J Solids Struct 45:4448–4459. https://doi.org/10.1016/j.ijsolstr.2008.03.023

    Article  MATH  Google Scholar 

  14. Khanam PN, AlMaadeed MA, AlMaadeed S et al (2016) Optimization and prediction of mechanical and thermal properties of graphene/LLDPE nanocomposites by using artificial neural networks. Int J Polymer Sci 2016:15

    Article  Google Scholar 

  15. Altarazi S, Ammouri M, Hijazi A (2018) Artificial neural network modeling to evaluate polyvinylchloride composites’ properties. Comput Mater Sci 153:1–9. https://doi.org/10.1016/j.commatsci.2018.06.003

    Article  Google Scholar 

  16. Bustillo A, Urbikain G, Perez JM et al (2018) Smart optimization of a friction-drilling process based on boosting ensembles. J Manuf Syst 48:108–121. https://doi.org/10.1016/j.jmsy.2018.06.004

    Article  Google Scholar 

  17. Van DD, Ly H-B, Trinh SH et al (2019) Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials 12:983. https://doi.org/10.3390/ma12060983

    Article  Google Scholar 

  18. Ling Y, Wang K, Wang X, Li W (2021) Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks. Neural Comput Appl 33:85–105. https://doi.org/10.1007/s00521-019-04662-3

    Article  Google Scholar 

  19. Wang Y, Wu X, Li X et al (2020) Prediction and analysis of tensile properties of austenitic stainless steel using artificial neural network. Metals 10:234. https://doi.org/10.3390/met10020234

    Article  Google Scholar 

  20. Zhang Z, Friedrich K (2003) Artificial neural networks applied to polymer composites: a review. Compos Sci Technol 63:2029–2044. https://doi.org/10.1016/S0266-3538(03)00106-4

    Article  Google Scholar 

  21. Sha W, Edwards KL (2007) The use of artificial neural networks in materials science based research. Mater Des 28:1747–1752. https://doi.org/10.1016/j.matdes.2007.02.009

    Article  Google Scholar 

  22. Xiang KL, Xiang PY, Wu YP (2014) Prediction of the fatigue life of natural rubber composites by artificial neural network approaches. Mater Des 57:180–185. https://doi.org/10.1016/j.matdes.2013.12.044

    Article  Google Scholar 

  23. Sankar HR, Srikant RR, Krishna PV et al (2013) Estimation of the dynamic properties of epoxy glass fabric composites with natural rubber particle inclusions. Int J Automotive Mech Eng 7:968–980

    Article  Google Scholar 

  24. Karaaǧaç B, Inal M, Deniz V (2009) Artificial neural network approach for predicting optimum cure time of rubber compounds. Mater Des 30:1685–1690. https://doi.org/10.1016/j.matdes.2008.07.010

    Article  Google Scholar 

  25. Iglesias C, Anjos O, Martínez J et al (2015) Prediction of tension properties of cork from its physical properties using neural networks. Eur J Wood Wood Prod 73:347–356. https://doi.org/10.1007/s00107-015-0885-1

    Article  Google Scholar 

  26. García Á, Anjos O, Iglesias C et al (2015) Prediction of mechanical strength of cork under compression using machine learning techniques. Mater Des 82:304–311. https://doi.org/10.1016/j.matdes.2015.03.038

    Article  Google Scholar 

  27. Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: A tutorial. Computer 29:31–44. https://doi.org/10.1109/2.485891

    Article  Google Scholar 

  28. Beale M, Hagan M, Demuth H (2019) MATLAB Deep Learning ToolboxTM User’s Guide R2019b. The MathWorks, Inc.

  29. Plaut D, Nowlan S, Hinton G (1986) Experiments on learning by back propagation. Technical Report CMU-CS-86–126

  30. Prechelt L (1998) Early stopping– but when? In: Orr GB, Müller K-R (eds) Neural networks: tricks of the trade. Springer, Berlin, pp 55–69

    Chapter  Google Scholar 

  31. Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2:164–168. https://doi.org/10.1090/qam/10666

    Article  MathSciNet  MATH  Google Scholar 

  32. Marquardt DW (1963) An Algorithm for Least-Squares Estimation of Nonlinear Parameters. J Soc Ind Appl Math 11:431–441. https://doi.org/10.1137/0111030

    Article  MathSciNet  MATH  Google Scholar 

  33. Yu H, Wilamowski BM (2011) Levenberg-Marquardt Training. In: Wilamowski BM, Irwin JD (eds) Intelligent Systems: The Industrial Electronics Handbook, 2nd edn. CRC Press, Boca Raton

    Google Scholar 

  34. Lopes H, Silva S, Machado J (2020) Analysis of the effect of shape factor on cork-rubber composites under small strain compression. Appl Sci 10:7177. https://doi.org/10.3390/App10207177

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to FCT—Fundação para a Ciência e Tecnologia who financially supported this work through scholarship SFRH/BD/136700/2018 and to Amorim Cork Composites. This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDP/04077/2020 and UIDB/04077/2020.

Funding

This study was funded by Fundação para a Ciência e Tecnologia (grant number SFRH/BD/136700/2018), by company Amorim Cok Composites and, also, funded within the R&D Units Project Scope: UIDP/04077/2020 and UIDB/04077/2020, by Fundação para a Ciência e Tecnologia.

Author information

Authors and Affiliations

  1. MEtRICs Research Center, University of Minho, Campus of Azurém, 4800-058, Guimarães, Portugal

    Helena Lopes & José Machado

  2. Amorim Cork Composites, Rua mérico Ferreira Amorim, Comendador A260, 4535-186, Mozelos VFR, Portugal

    Susana P. Silva

Authors
  1. Helena Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Susana P. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Helena Lopes.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lopes, H., Silva, S.P. & Machado, J. Application of artificial neural networks to predict mechanical behaviour of cork-rubber composites. Neural Comput & Applic 33, 14069–14078 (2021). https://doi.org/10.1007/s00521-021-06048-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06048-w

Keywords

Search

Navigation