TY - GEN
T1 - Hdpe/tio2 nanocomposite
T2 - 9th International Conference on Manufacturing Science and Technology, ICMST 2018
AU - Mozumder, Mohammad Sayem
AU - Mairpady, Anusha
AU - Mourad, Abdel Hamid I.
N1 - Publisher Copyright:
© 2019 Trans Tech Publications, Switzerland.
PY - 2019
Y1 - 2019
N2 - Polymeric nanocomposites have proven to be excellent candidate as biomaterials. However, materials and approaches used to improve the mechanical property of the polymer are still under scrutiny. In this study, improvement of mechanical property upon addition of nano-titanium oxide (n-TiO2 ), cellulose nanocrystal (CNC) and two different types of coupling agent was analyzed. Influence of the individual factors and its interaction with tensile strength was evaluated using analysis of variance. From the analyses of main effect and interaction effects, it could be concluded that n-TiO2 and CNC have major influence on the improving mechanical properties. Moreover, the coupling agent and compatibilizing agent did not have considerable effect on the mechanical properties. The central composite design was used to evaluate the best combination of n-TiO2 and CNC to be experimented. The responses were modeled and optimized using response surface methodology (RSM) and artificial neural network (ANN). The predicted data was in agreement with the experimental data. The modeling accuracy and efficiency is evaluated based on regression coefficient (R square value). Both the method had recommendable R square value. However, the R square value of the Artificial neural network (R2 >95%) was higher than Response surface methodology (R2 >70 %).
AB - Polymeric nanocomposites have proven to be excellent candidate as biomaterials. However, materials and approaches used to improve the mechanical property of the polymer are still under scrutiny. In this study, improvement of mechanical property upon addition of nano-titanium oxide (n-TiO2 ), cellulose nanocrystal (CNC) and two different types of coupling agent was analyzed. Influence of the individual factors and its interaction with tensile strength was evaluated using analysis of variance. From the analyses of main effect and interaction effects, it could be concluded that n-TiO2 and CNC have major influence on the improving mechanical properties. Moreover, the coupling agent and compatibilizing agent did not have considerable effect on the mechanical properties. The central composite design was used to evaluate the best combination of n-TiO2 and CNC to be experimented. The responses were modeled and optimized using response surface methodology (RSM) and artificial neural network (ANN). The predicted data was in agreement with the experimental data. The modeling accuracy and efficiency is evaluated based on regression coefficient (R square value). Both the method had recommendable R square value. However, the R square value of the Artificial neural network (R2 >95%) was higher than Response surface methodology (R2 >70 %).
KW - ANOVA
KW - And artificial neural network
KW - Cellulose nanocrystals
KW - High-density polyethylene
KW - Response surface methodology
UR - http://www.scopus.com/inward/record.url?scp=85064215530&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064215530&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/SSP.287.54
DO - 10.4028/www.scientific.net/SSP.287.54
M3 - Conference contribution
AN - SCOPUS:85064215530
T3 - Solid State Phenomena
SP - 54
EP - 58
BT - Manufacturing Sciences and Technologies IX
A2 - Debnath, Sujan
PB - Trans Tech Publications Ltd
Y2 - 11 August 2018 through 13 August 2018
ER -