Hostname: page-component-6d856f89d9-76ns8 Total loading time: 0 Render date: 2024-07-16T06:15:17.517Z Has data issue: false hasContentIssue false
  • English
  • Français

Experimental Investigation of Mechanical Properties of PMMA Nanocomposites Containing Various Contents of Prevalent Nanofillers from Multi-Criteria Decision Analysis Point of View

Published online by Cambridge University Press:  09 May 2017

R. Hasanzadeh ,
S. Rashahmadi  and
H. Memari
R. Hasanzadeh*
Affiliation:
Department of Mechanical EngineeringUrmia UniversityUrmia, Iran
S. Rashahmadi
Affiliation:
Department of Mechanical EngineeringUrmia UniversityUrmia, Iran
H. Memari
Affiliation:
Department of Mechanical EngineeringUrmia UniversityUrmia, Iran
*
*Corresponding author (re.hasanzadeh@urmia.ac.ir)
Get access

Abstract

Poly methyl methacrylate (PMMA) was melt compounded with different nanoparticles using a twin-screw extruder. Nano TiO2, SiO2 and Al2O3 in 0.5, 1 and 2 wt% were added to the polymeric matrix as reinforcements. Nine polymeric nanocomposite samples were injection molded. Impact strength, Young's modulus, Rockwell hardness-R and cost of raw materials were considered as different criteria. Two procedures of multi-criteria decision making (MCDM) methods were performed for solving material selection problem. Criteria weighting was performed using analytical hierarchy process (AHP). According to weights that obtained from AHP, the alternative ranking was implemented using TOPSIS (the technique for order preference by similarity to ideal solution) and MOORA (multi-objective optimization on the basis of ratio analysis) methods. The results indicated that the addition of nanoparticles to the polymeric matrix was significantly improved mechanical properties. The results showed a 94% and 229% improvement in impact strength for PMMA containing 1 and 2 wt% TiO2 compared to pure PMMA. The results also revealed that hardness and Young's modulus of PMMA were increased by addition of different nanoparticles. The implementation of MCDM methods illustrated that PMMA-2 wt% TiO2 is the best alternative. Also, PMMA-1 wt% TiO2 and PMMA-2 wt% SiO2 are the next alternatives, respectively.

Keywords

NanocompositeMechanical propertiesMaterial selectionPolymer
Type
Research Article
Information
Journal of Mechanics , Volume 34 , Issue 4 , August 2018 , pp. 461 - 468
Copyright
Copyright © The Society of Theoretical and Applied Mechanics 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Lu, H., Yao, Y., Huang, W. M., Leng, J. and Hui, D., “Significantly Improving Infrared Light-Induced Shape Recovery Behavior of Shape Memory Polymeric Nanocomposite via a Synergistic Effect of Carbon Nanotube and Boron Nitride,” Composites Part B: Engineering, 62, pp. 256–261 (2014).Google Scholar
2. Hashemi, R., “On the Overall Viscoelastic Behavior of Graphene/Polymer Nanocomposites with Imperfect Interface,” International Journal of Engineering Science, 105, pp. 38–55 (2016).Google Scholar
3. Cano, M. et al., “Improving the Mechanical Properties of Graphene Oxide Based Materials by Covalent Attachment of Polymer Chains,” Carbon, 52, pp. 363–371 (2013).Google Scholar
4. Mutiso, R. M. and Winey, K. I., “Electrical Properties of Polymer Nanocomposites Containing Rod-Like Nanofillers,” Progress in Polymer Science, 40, pp. 63–84 (2015).Google Scholar
5. Yuan, B. et al., “Preparation of Functionalized Graphene Oxide/Polypropylene Nanocomposite with Significantly Improved Thermal Stability and Studies on the Crystallization Behavior and Mechanical Properties,” Chemical Engineering Journal, 237, pp. 411–420 (2014).Google Scholar
6. Yousefi, N. et al., “Highly Aligned Graphene/Polymer Nanocomposites with Excellent Dielectric Properties for HighPerformance Electromagnetic Interference Shielding,” Advanced Materials, 26, pp. 5480–5487 (2014).Google Scholar
7. Hong, N. et al., “Co-Precipitation Synthesis of Reduced Graphene Oxide/NiAl-Layered Double Hydroxide Hybrid and its Application in Flame Retarding Poly (Methyl Methacrylate),” Materials Research Bulletin, 49, pp. 657–664 (2014).Google Scholar
8. Chakraborty, S., Kumar, M., Suresh, K. and Pugazhenthi, G., “Investigation of Structural, Rheological and Thermal Properties of PMMA/ONi-Al LDH Nanocomposites Synthesized via Solvent Blending Method: Effect of LDH Loading,” Chinese Journal of Polymer Science, 34, pp. 739–754 (2016).Google Scholar
9. Rajender, N. and Suresh, K. I., “SurfaceInitiated Atom Transfer Radical Polymerization (SIATRP) From Graphene Oxide: Effect of Functionalized Graphene Sheet (FGS) on the Synthesis and Material Properties of PMMA Nanocomposites,” Macromolecular Materials and Engineering, 301, pp. 81–92 (2016).Google Scholar
10. Tripathi, S. N., Saini, P., Gupta, D. and Choudhary, V., “Electrical and Mechanical Properties of PMMA/Reduced Graphene Oxide Nanocomposites Prepared via in Situ Polymerization,” Journal of Materials Science, 48, pp. 6223–6232 (2013).Google Scholar
11. Okolieocha, C. et al., “Preparation of Microcellular Low-Density PMMA Nanocomposite Foams: Influence of Different Fillers on the Mechanical, Rheological and Cell Morphological Properties,” Composites Science and Technology, 118, pp. 108–116 (2015).Google Scholar
12. Chang, M. K. and Lee, H. C., “Effects of Montmorillonite and Compatibilizer on the Mechanical and Thermal Properties of Dispersing Intercalated PMMA Nanocomposites,” International Communications in Heat and Mass Transfer, 67, pp. 21–28 (2015).Google Scholar
13. Mahato, N., Banerjee, A., Gupta, A., Omar, S. and Balani, K., “Progress in Material Selection for Solid Oxide Fuel Cell Technology: A Review,” Progress in Materials Science, 72, pp. 141–337 (2015).Google Scholar
14. Triantaphyllou, E., Multi-Criteria Decision Making Methods: a Comparative Study, Springer Science & Business Media, Berlin (2000).Google Scholar
15. Velasquez, M. and Hester, P. T., “An Analysis of Multi-Criteria Decision Making Methods,” International Journal of Operations Research, 10, pp. 56–66 (2013).Google Scholar
16. Saito, E. K., Shea, S., Jones, A., Ramos, G. and Pitesky, M., “A Cooperative Approach to Animal Disease Response Activities: Analytical Hierarchy Process (AHP) and vvIBD in California Poultry,” Preventive Veterinary Medicine, 121, pp. 123–131 (2015).Google Scholar
17. Abdollah, M. F. B., Shuhimi, F. F., Ismail, N., Amiruddin, H. and Umehara, N., “Selection and Verification of Kenaf Fibres as an Alternative Friction Material Using Weighted Decision Matrix method,” Materials & Design, 67, pp. 577–582 (2015).Google Scholar
18. Wang, Y. J., “A Fuzzy Multi-Criteria Decision-Making Model by Associating Technique for Order Preference by Similarity to Ideal Solution with Relative Preference Relation,” Information Sciences, 268, pp. 169–184 (2014).Google Scholar
19. Kluczek, A. and Gladysz, B., “Analytical Hierarchy Process/Technique for Order Preference by Similarity to Ideal Solution-Based Approach to the Generation of Environmental Improvement Options for Painting Process e Results from an Industrial Case Study,” Journal of Cleaner Production, 101, pp. 360–367 (2015).Google Scholar
20. Karande, P. and Chakraborty, S., “Application of Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Method for Materials Selection,” Materials & Design, 37, pp. 317–324 (2012).Google Scholar
21. Gadakh, V. S., “Application of MOORA Method for Parametric Optimization of Milling Process,” International Journal of Applied Engineering Research, 1, pp. 743–758 (2010).Google Scholar
22. Çalışkan, H., Kurşuncu, B., Kurbanoğlu, C. and Güven, Ş. Y., “Material Selection for the Tool Holder Working under Hard Milling Conditions Using Different Multi Criteria Decision Making Methods,” Materials & Design, 45, pp. 473–479 (2013).Google Scholar
23. Mansor, M. R., Sapuan, S. M., Zainudin, E. S., Nuraini, A. A. and Hambali, A., “Hybrid Natural and Glass Fibers Reinforced Polymer Composites Material Selection Using Analytical Hierarchy Process for Automotive Brake Lever Design,” Materials & Design, 51, pp. 484–492 (2013).Google Scholar
24. Hambali, A., Sapuan, S. M., Ismail, N. and Nukman, Y., “Application of Analytical Hierarchy Process in the Design Concept Selection of Automotive Composite Bumper Beam During the Conceptual Design Stage,” Scientific Research and Essays, 4, pp. 198–211 (2009).Google Scholar
25. Modanloo, V., Doniavi, A. and Hasanzadeh, R., “Application of Multi Criteria Decision Making Methods to Select Sheet Hydroforming Process Parameters,” Decision Science Letters, 5, pp. 349–360 (2016).Google Scholar
26. Shishavan, S. M., Azdast, T. and Ahmadi, S. R., “Investigation of the Effect of Nanoclay and Processing Parameters on the Tensile Strength and Hardness of Injection Molded Acrylonitrile Butadiene Styrene–Organoclay Nanocomposites,” Materials & Design, 58, pp. 527–534 (2014).Google Scholar
27. Navidfar, A., Azdast, T. and Karimzad Ghavidel, A., “Influence of Processing Condition and Carbon Nanotube on Mechanical Properties of Injection Molded Multi Walled Carbon Nanotube/Poly (Methyl Mmethacrylate) Nanocomposites,” Journal of Applied Polymer Science, 133 (2016).Google Scholar
28. Ghavidel, A. K., Azdast, T., Shabgard, M. R., Navidfar, A. and Shishavan, S. M., “Effect of Carbon Nanotubes on Laser Cutting of Multi-Walled Carbon Nanotubes/Poly Methyl Methacrylate Nanocomposites,” Optics & Laser Technology, 67, pp. 119–124 (2015).Google Scholar
29. Singh, R. P. and Nachtnebel, H. P., “Analytical Hierarchy Process (AHP) Application for Reinforcement of Hydropower Strategy in Nepal,” Renewable and Sustainable Energy Reviews, 55, pp. 43–58 (2016).Google Scholar
30. Saaty, T. L., “Decision Making with the Analytic Hierarchy Process,” International Journal of Services Sciences, 1, pp. 83–98 (2008).Google Scholar
31. Ariff, H., Salit, M. S., Ismail, N. and Nukman, Y., “Use of Analytical Hierarchy Process (AHP) for Selecting the Best Design Concept,” Jurnal Teknologi, 49, pp. 1–18 (2012).Google Scholar
32. Tong, L. I., Wang, C. H. and Chen, H. C., “Optimization of Multiple Responses Using Principal Component Analysis and Technique for Order Preference by Similarity to Ideal Solution,” The International Journal of Advanced Manufacturing Technology, 27, pp. 407–414 (2005).Google Scholar
33. Görener, A., Dincer, H. and Hacioglu, U., “Application of Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Method for Bank Branch Location Selection,” International Journal of Finance & Banking Studies (2147-4486), 2, pp. 41–52 (2016).Google Scholar