Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks
The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP)...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
|
| Series: | International Journal of Polymer Science |
| Online Access: | http://dx.doi.org/10.1155/2016/5340252 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832554154613014528 |
|---|---|
| author | P. Noorunnisa Khanam MA AlMaadeed Sumaaya AlMaadeed Suchithra Kunhoth M. Ouederni D. Sun A. Hamilton Eileen Harkin Jones Beatriz Mayoral |
| author_facet | P. Noorunnisa Khanam MA AlMaadeed Sumaaya AlMaadeed Suchithra Kunhoth M. Ouederni D. Sun A. Hamilton Eileen Harkin Jones Beatriz Mayoral |
| author_sort | P. Noorunnisa Khanam |
| collection | DOAJ |
| description | The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort. |
| format | Article |
| id | doaj-art-65d9102f2987416a8a2af5e89b3c00d1 |
| institution | Kabale University |
| issn | 1687-9422 1687-9430 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Polymer Science |
| spelling | doaj-art-65d9102f2987416a8a2af5e89b3c00d12025-02-03T05:52:15ZengWileyInternational Journal of Polymer Science1687-94221687-94302016-01-01201610.1155/2016/53402525340252Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural NetworksP. Noorunnisa Khanam0MA AlMaadeed1Sumaaya AlMaadeed2Suchithra Kunhoth3M. Ouederni4D. Sun5A. Hamilton6Eileen Harkin Jones7Beatriz Mayoral8Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, QatarCenter for Advanced Materials, Qatar University, P.O. Box 2713, Doha, QatarDepartment of Computer Science & Engineering, Qatar University, P.O. Box 2713, Doha, QatarDepartment of Computer Science & Engineering, Qatar University, P.O. Box 2713, Doha, QatarQatar Petrochemical Company (QAPCO), Doha, QatarSchool of Mechanical & Aerospace Engineering, Queen’s University Belfast, Belfast BT9 5AH, UKSchool of Mechanical & Aerospace Engineering, Queen’s University Belfast, Belfast BT9 5AH, UKSchool of Engineering, University of Ulster, Newtownabbey BT37 0QB, UKSchool of Mechanical & Aerospace Engineering, Queen’s University Belfast, Belfast BT9 5AH, UKThe focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.http://dx.doi.org/10.1155/2016/5340252 |
| spellingShingle | P. Noorunnisa Khanam MA AlMaadeed Sumaaya AlMaadeed Suchithra Kunhoth M. Ouederni D. Sun A. Hamilton Eileen Harkin Jones Beatriz Mayoral Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks International Journal of Polymer Science |
| title | Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks |
| title_full | Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks |
| title_fullStr | Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks |
| title_full_unstemmed | Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks |
| title_short | Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks |
| title_sort | optimization and prediction of mechanical and thermal properties of graphene lldpe nanocomposites by using artificial neural networks |
| url | http://dx.doi.org/10.1155/2016/5340252 |
| work_keys_str_mv | AT pnoorunnisakhanam optimizationandpredictionofmechanicalandthermalpropertiesofgraphenelldpenanocompositesbyusingartificialneuralnetworks AT maalmaadeed optimizationandpredictionofmechanicalandthermalpropertiesofgraphenelldpenanocompositesbyusingartificialneuralnetworks AT sumaayaalmaadeed optimizationandpredictionofmechanicalandthermalpropertiesofgraphenelldpenanocompositesbyusingartificialneuralnetworks AT suchithrakunhoth optimizationandpredictionofmechanicalandthermalpropertiesofgraphenelldpenanocompositesbyusingartificialneuralnetworks AT mouederni optimizationandpredictionofmechanicalandthermalpropertiesofgraphenelldpenanocompositesbyusingartificialneuralnetworks AT dsun optimizationandpredictionofmechanicalandthermalpropertiesofgraphenelldpenanocompositesbyusingartificialneuralnetworks AT ahamilton optimizationandpredictionofmechanicalandthermalpropertiesofgraphenelldpenanocompositesbyusingartificialneuralnetworks AT eileenharkinjones optimizationandpredictionofmechanicalandthermalpropertiesofgraphenelldpenanocompositesbyusingartificialneuralnetworks AT beatrizmayoral optimizationandpredictionofmechanicalandthermalpropertiesofgraphenelldpenanocompositesbyusingartificialneuralnetworks |