화학공학소재연구정보센터
Polymer(Korea), Vol.46, No.5, 614-620, September, 2022
HDPE 기반의 나노다이아몬드 복합소재의 물성 예측을 위한 인공신경망 연구
Artificial Neural Network for Prediction of Mechanical Properties of HDPE Based Nanodiamond Nanocomposite
E-mail:,
The mechanical performance of the nanocomposite depends on the processing conditions of the samples. Therefore a predictive model is essential to proceed the combination of processing conditions into account, for accurately predicting the mechanical properties is a critical requirement in manufacturing industries. The current investigation explores the prediction of mechanical properties of high-density polyethylene (HDPE)-based nano-diamond nanocomposite (i.e., HDPE/0.1 ND) using an artificial neural network (ANN) model under various processing conditions of temperature and pressure. A 2-10-2 (2 input, 10 hidden and 2 output layer) neural network model with Levenberg–Marquardt algorithm is developed to predict Young's modulus and Hardness of HDPE/0.1 ND nanocomposite. The model accurately predicted Young's modulus and hardness with a correlation coefficient of more than 0.99. The root means square error (r.m.s) of experimental vs. predicted value is minimal, confirming the proposed ANN model's high reliability and accuracy.
  1. Sahu SK, Badgayan ND, Samanta S, Sreekanth PR, Mater. Today. Proc., 24, 415 (2020)
  2. Badgayan ND, Samanta S, Sahu SK, Siva SV, Sadasivuni KK, Sahu D, Sreekanth PR, Wear, 376, 1379 (2017)
  3. Yu L, Wei D, Zheng A, Xu X, Guan Y, Polym. Bull. (2022)
  4. Sahu SK, Badgayan ND, Sreekanth PR, Appl. Chem., 12, 5709 (2022)
  5. Obeid A, Roumie M, Badawi MS, Awad R, J. Inorg. Organomet. Polym., 32, 1506 (2022)
  6. Honaker K, Vautard F, Drzal LT, Adv. Compos. Hybrid Mater., 4, 492 (2021)
  7. Olesik P, Godzierz M, Kozioł M, Jała J, Szeluga U, Myalski J, Mater., 14, 4024 (2021)
  8. Khan AA, Khan UA, Hassan R, Effects on Mechanical Properties of High-Density Polyethylene (HDPE) Reinforced with Walnut Shell Powder, Springer: Singapore, pp 323-330, 2022
  9. Morimune‐Moriya S, Hashimoto T, Haga R, Tanahashi H, J. Appl. Polym. Sci., 138, 50929 (2021)
  10. Badgayan ND, Sahu SK, Samanta S, Sreekanth PS, Int. J. Thermophys., 40, 93 (2019)
  11. Sahu SK, Sreekanth RPS, Adv. Mater. Proc. Technol. (2022)
  12. Wang K, Zhang K, Jiang Z, Qiu Z, J. Polym. Environ., 30, 555 (2022)
  13. Kapoor S, Goyal M, Jindal P, J. Therm. Compos. Mater., 35, 216 (2022)
  14. Ahmad A, Mansor N, Mahmood H, Iqbal T, Moniruzzaman M, J. Appl. Polym. Sci., 139, 51788 (2022)
  15. Manjunatha CM, Srihari S, Trans. Indian Nat. Acad. Eng., 7, 501 (2022)
  16. Namdev A, Telang A, Purohit R, Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci., 236, 7984 (2022)
  17. Bilisik K, Akter M, J. Reinf. Plast. Compos., 41, 99 (2022)
  18. Yarahmadi A, Hashemian M, Toghraie D, Abedinzadeh R, Eftekhari SA, J. Mol. Liq., 347, 118392 (2022)
  19. Lodhi RS, Kumar P, Achuthanunni A, Rahaman M, Das P, Woodhead Publishing: Duxford, pp 75-105, 2022
  20. Zhang X, Chen J, Liu T, Macromol. Theory Simul., 31, 2100044 (2022)
  21. Sahu SK, Badgayan ND, Samanta S, Sreekanth PSR, Mater. Sci. Forum, 917, 27 (2018)
  22. Maitra U, Prasad KE, Ramamurty U, Rao CNR, Solid State Commun., 149, 1693 (2009)
  23. Sahu SK, Badgayan ND, Sreekanth PR, Wear, 438-439 (2019)
  24. Ornaghi HL Jr, Monticeli FM, Neves RM, Zattera AJ, Amico SC, Polym. Polym. Compos., 29, S1033 (2021)
  25. Adesina OT, Jamiru T, Daniyan IA, Sadiku ER, Ogunbiyi OF, Adesina OS, Beneke LW, Cogent Eng., 7, 1720894 (2020)
  26. Lingaraju D, Ramji K, Rao NM, Procedia Eng., 10, 9 (2011)
  27. Zazoum B, Triki E, Bachri A, Mater., 13, 4266 (2020)