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Augmenting epoxy toughness by combination of both thermoplastic and nanolayered materials and using artificial intelligence techniques for modeling and optimization

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Abstract

Epoxy resins are brittle because of their tight three-dimensional molecular network structures. In an attempt to overcome this issue, epoxies incorporating a combination of nanoclay (used for nano-reinforcement) and high-impact polystyrene (HIPS, used as the thermoplastic phase) were synthesized and tested in this work. The tensile, flexural, compressive, and impact strengths of these materials were evaluated. Various factors can influence these properties of hybrid nanocomposites during the preparation of such materials, so an artificial neural network (ANN) was employed to determine the effects of the clay, HIPS, and hardener loadings on the mechanical properties of the epoxy/HIPS/nanoclay nanocomposites and to develop models for predicting their mechanical behavior. A genetic algorithm (GA), a powerful optimization method, was employed to determine a fitness function that could calculate the optimum values of these mechanical properties. The results obtained indicated that the new ternary nanocomposites possess tensile, compressive, and impact strengths were improved up to 60 %, 64 %, and 402 %, respectively higher than those of the neat resin, although they did not show enhanced flexural strength. The tensile, flexural, and copmressive elongations at break were improved up to 53%, 38%, and 27% greater than those of neat epoxy, respectively. In addition, the fracture surface morphologies of the ternary nanocomposites were investigated by energy-dispersive X-ray spectroscopy (EDX) and scanning electron microscopy (SEM). The mechanical properties of the new ternary nanocomposites showed that they possess enhanced toughness compared to neat epoxy resin.

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References

  1. LeBaron PC, Wang Z, Pinnavaia TJ (1999) Appl Clay Sci 15:11–29

    Article  CAS  Google Scholar 

  2. Becker O, Varley RJ, Simon GP (2004) Eur Polym J 40:187–195

    Google Scholar 

  3. Becker O, Varley R, Simon G (2002) Polym 43:4365–4373

    Article  CAS  Google Scholar 

  4. Liu W, Hoa SV, Pugh M (2005) Compos Sci Technol 65:307–316

    Article  CAS  Google Scholar 

  5. Yasmin A, Luo J-J, Daniel IM (2006) Compos Sci Technol 66:1182–1189

    Article  CAS  Google Scholar 

  6. Mirmohseni A, Zavareh S (2010) Mater Des 31:2699–2706

    Article  CAS  Google Scholar 

  7. Mirmohseni A, Zavareh S (2011) J Polym Res 18:509–517

    Article  CAS  Google Scholar 

  8. Mohan TP, Kumar MR, Velmurugan R (2006) J Mater Sci 41:2929–2937

    Article  CAS  Google Scholar 

  9. Alsewailem FD, Gupta RK (2006) Can J Chem Eng 84:693–703

    Article  CAS  Google Scholar 

  10. Garg AC, Mai Y-W (1988) Compos Sci Technol 31:179–223

    Article  CAS  Google Scholar 

  11. Kinloch AJ, Shaw SJ, Tod DA et al (1983) Polym 24:1341–1354

    Article  CAS  Google Scholar 

  12. Brooker RD, Kinloch AJ, Taylor AC (2010) J Adhes 86:726–741

    Article  CAS  Google Scholar 

  13. Sultan JN, McGarry FJ (1973) Polym Eng Sci 13:29–34

    Article  CAS  Google Scholar 

  14. Pearson RA, Yee AF (1991) J Mater Sci 26:3828–3844

    Article  CAS  Google Scholar 

  15. Frounchi M, Mehrabzadeh M, Parvary M (2000) Polym Int 49:163–169

    Article  CAS  Google Scholar 

  16. Chen TK, Jan YH (1992) J Mater Sci 27:111–121

    Article  CAS  Google Scholar 

  17. Ramakrishna HV, Priya SP, Rai SK (2007) J Appl Polym Sci 104:171–177

    Article  CAS  Google Scholar 

  18. Torres A, López-de-Ullibarri I, Abad MJ et al (2004) J Appl Polym Sci 92:461–467

    Article  CAS  Google Scholar 

  19. Yun NG, Won YG, Kim SC (2004) Polym Bull 52:365–372

    Article  CAS  Google Scholar 

  20. Kimoto M, Mizutani K (1997) J Mater Sci 32:2479–2483

    Article  CAS  Google Scholar 

  21. Mimura K, Ito H, Fujioka H (2000) Polym 41:4451–4459

    Article  CAS  Google Scholar 

  22. Rose LRF (1987) Mech Mater 6:11–15

    Article  Google Scholar 

  23. Faber KT, Evans AG (1983) Acta Metall 31:577–584

    Article  Google Scholar 

  24. Zaryabi A, Ben Hamza A (2012) Neural Comput Appl 21:1–9

    Google Scholar 

  25. Lim Y, Kang S (2012) Neural Comput Appl 21:1931–1936

    Article  Google Scholar 

  26. Masri SF, Chassiakos AG, Caughey TK (1993) J Appl Mech 60:123–133

    Article  Google Scholar 

  27. Ghaboussi J, Garrett J Jr, Wu X (1991) J Eng Mech 117:132–153

    Article  Google Scholar 

  28. Rhim J, Lee SW (1995) Comput Mech 16:437–443

    Article  Google Scholar 

  29. Su C-T, Wang F-F (2012) Neural Comput Appl 21:2127–2135

    Google Scholar 

  30. Song RG, Zhang QZ (2001) Mater Sci Eng C 17:133–137

    Google Scholar 

  31. Chung JS, Hwang SM (1997) J Mater Process Technol 72:69–77

    Article  Google Scholar 

  32. Mousavi Anijdan SH, Madaah-Hosseini HR, Bahrami A (2007) Mater Des 28:609–615

    Article  CAS  Google Scholar 

  33. Funahashi K-I (1989) Neural Netw 2:183–192

    Article  Google Scholar 

  34. Hartman EJ, Keeler JD, Kowalski JM (1990) Neural Comput 2:210–215

    Article  Google Scholar 

  35. Khandetsky V, Antonyuk I (2002) NDT&E Int 35:483–488

    Google Scholar 

  36. Asif A, Leena K, Lakshmana Rao V et al (2007) J Appl Polym Sci 106:2936–2946

    Article  CAS  Google Scholar 

  37. Bakar M, Wojtania I, Legocka I et al (2007) Adv Polym Technol 26:223–231

    Article  CAS  Google Scholar 

  38. Mirmohseni A, Zavareh S (2010) J Polym Res 17:191–201

    Article  CAS  Google Scholar 

  39. López J, Ramírez C, Abad MJ et al (2002) J Appl Polym Sci 85:1277–1286

    Article  Google Scholar 

Download references

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Correspondence to A. Hamed Mashhadzadeh.

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Rostamiyan, Y., Fereidoon, A.B., Hamed Mashhadzadeh, A. et al. Augmenting epoxy toughness by combination of both thermoplastic and nanolayered materials and using artificial intelligence techniques for modeling and optimization. J Polym Res 20, 135 (2013). https://doi.org/10.1007/s10965-013-0135-3

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  • DOI: https://doi.org/10.1007/s10965-013-0135-3

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