Development and Optimization of Hybrid Friction Materials Consisting of Nanoclay and Carbon Nanotubes by using Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under Fuzzy Atmosphere

Authors

  • Tej SINGH Department of Mechanical Engineering, National Institute of Technology, Hamirpur
  • Amar PATNAIK Department of Mechanical Engineering, National Institute of Technology, Hamirpur
  • Bhabani SATAPATHY Centre for Polymer Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi
  • Bharat TOMAR Allied Nippon Industries Limited-Sahibabad, Uttar Pradesh

Keywords:

Friction composites, FAHP, FTOPSIS

Abstract

The tribo-performance of nanoclay and multi-walled carbon nanotube (MWNT) filled and graphite lubricated phenolic composites, reinforced with a combination of lapinus and kevlar fibers, have been evaluated on a Kraus friction testing machine. The combined fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) approach, taking into account performance defining attributes (PDAs) such as friction performance, wear, friction-fade, friction-recovery, stability coefficient, variability coefficient, friction fluctuations and temperature rise of the disc, was used for the performance assessment of fabricated friction composite materials. The weight of different PDAs were evaluated by FAHP; μ-performance (0.144, 0.255, 0.435), wear (0.144, 0.255, 0.435), fade-% (0.073, 0.15, 0.307), recovery-% (0.063, 0.126, 0.268), stability coefficient (0.037, 0.075, 0.156), variability coefficient (0.032, 0.063, 0.136), frictional fluctuations (0.023, 0.037, 0.069), and DTR (0.023, 0.037, 0.069) respectively.  FTOPSIS was employed to determine the optimal ranking of the friction composite materials as NC-7>NC-8>NC-6>NC-5>NC-3>NC-4>NC-2>NC-1. The alternative with kevlar: lapinus, 2.5:27.5 wt-% and graphite: nanoclay: carbon nanotube, 2.25:2.75 wt-% exhibits the optimal properties.

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Author Biographies

Tej SINGH, Department of Mechanical Engineering, National Institute of Technology, Hamirpur

Mechanical Engineering Department

Amar PATNAIK, Department of Mechanical Engineering, National Institute of Technology, Hamirpur

Asst. Professor

Mechanical Engineering Department
N.I.T.Hamirpur-177005 (H.P), India

Bhabani SATAPATHY, Centre for Polymer Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi

Centre for polymer science and engineering

References

J Bijwe. Composites as friction materials: recent developments in non-asbestos fiber reinforced friction materials- A review. Polym. Compos. 1997; 18, 378-96.

D Chan and GW Stachowiak. Review of automotive brake friction materials. Proc. Ins. Mech. Eng. D: J. Automobile Eng. 2004; 218, 953-66.

M Elzey, R Vancheeswaran, S Myers and R McLellan. Automotive Braking-technologies for the 21st Century. In: Proceeding of the International Conference on Brakes 2000, Leeds, UK, 2000, p. 197-205.

BK Satapathy and J Bijwe. Performance of friction materials based on variation in nature of organic fibers: Part II. Optimization by balancing and ranking using multiple criteria decision model (MCDM). Wear 2004; 257, 585-9.

BK Satapathy, J Bijwe and DK Kolluri. Assessment of fiber contribution to friction material performance using grey relational analysis (GRA). J. Compos. Mater. 2006; 40, 483-501.

DK Kolluri, BK Satapathy, J Bijwe and AK Ghosh. Analysis of load and temperature dependence of tribo-performance of graphite filled phenolic composites. Mater. Sci. Eng. A 2007; 456, 162-9.

BK Satapathy, A Majumdar and BS Tomar. Optimal design of fly ash filled composite friction materials using combined analytical hierarchy process and technique for order preference by similarity to ideal solutions approach. Mater. Des. 2010; 31, 1937-44.

Z Zhu, L Xu, G Chen and Y Li. Optimization on tribological properties of aramid fiber and CaSO4 whisker reinforced non-metallic friction material with analytic hierarchy process and preference ranking organization method for enrichment evaluations. Mater. Des. 2010; 31, 551-5.

HJ Hwang, SL Jung, KH Cho, YJ Kim and H Jang. Tribological performance of brake friction materials containing carbon nanotubes. Wear 2010; 268, 519-25.

T Singh, A Patnaik and BK Satapathy. Effect of carbon nanotubes on tribo performance of brake friction materials. AIP Conf. Proc. 2011; 1393, p. 223.

MG Jacko, PHS Tsang and SK Rhee. Wear debris compactions and friction film formation of polymer composites. Wear 1989; 133, 23-38.

SK Rhee, MG Jacko and PHS Tsang. The role of friction film in friction, wear and noise of automotive brakes. Wear 1991; 146, 89-97.

S Bahadur. The development of transfer layers and their role in polymer tribology. Wear 2000; 245, 92-9.

W Österle and I Urban. Friction layers and friction films on PMC brake pads. Wear 2004; 257, 215-26.

P Filip, Z Weiss and Z Rafaja. On friction layer formation in polymer matrix composite materials for brake applications. Wear 2002; 252, 189-98.

W Österle, AI Dmitriev and H Klob. Possible impacts of third body nanostructure on friction performance during dry sliding determined by computer simulation based on the method of movable cellular automata. Tribol. Int. 2012; 48, 128-36

SJ Kim, KS Kim and H Jang. Optimization of manufacturing parameters for a brake lining using Taguchi method. J. Mater. Process. Tech. 2003; 136, 202-8.

I Chamodrakas, D Batis and D Martakos. Supplier selection in electronic market places using satisfying and fuzzy AHP. Expert Syst. Appl. 2010; 37, 490-8.

O Duran and J Aguilo. Computer-aided machine-tool selection based on a Fuzzy-AHP approach. Expert Syst. Appl. 2008; 34, 1787-94.

H Amy, I Lee, WC Chen and CJ Chang. A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Syst. Appl. 2008; 34, 96-107.

Z Gungor, G Serhadlioglu and SE Kesen. A fuzzy AHP approach to personnel selection problem. Appl. Soft Comput. 2009; 9, 641-6.

Z Ayag and RG Ozdemir. A fuzzy AHP approach to evaluating machine tool alternatives. J. Intell. Manuf. 2006; 17, 179-90.

SJ Chen and CL Hwang. Fuzzy Multi Attribute Decision Making, Lecture Notes in Economics and Mathematical System Series. Vol 375. Springer-Verlag New York, 1992.

E Triantaphyllou and CL Lin. Development and evaluation of five fuzzy multi-attribute decision making methods. Int. J. Approx. Reason. 1996; 14, 281-310.

CT Chen. Extension of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Set. Syst. 2000; 114, 1-9.

TC Chu. Selecting plant location via a fuzzy TOPSIS approach. Int. J. Adv. Manuf. Tech. 2002; 20, 859-64.

TC Chu and YC Lin. A fuzzy topsis method for robot selection. Int. J. Adv. Manuf. Technol. 2003; 21, 284-90.

TC Wang and TH Chang. Application of TOPSIS in evaluating initial training aircraft under fuzzy environment. Expert Syst. Appl. 2007; 33, 870-80.

M Yurdakula and Y Tansel. Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. J. Mater. Process. Tech. 2009; 209, 310-7.

RK Singh and L Benyoucef. A fuzzy TOPSIS based approach for e-sourcing. Eng. Appl. Artif. Intell. 2011; 24, 437-48.

R Rostamzadeh and S Sofian. Prioritizing effective 7Ms to improve production systems performance using fuzzy AHP and fuzzy TOPSIS (case study). Expert Syst. Appl. 2011; 38, 5166-77.

A Zouggari and L Benyoucef. Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem. Eng. Appl. Artif. Intell. 2012; 25, 507-19.

CC Sun. A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst. Appl. 2010; 37, 7745-54.

F Torfi, RZ Farahani and S Rezapour. Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives. Appl. Soft Comput. 2010; 10, 520-8.

G Büyüközkan and G Çifci. A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in health care industry. Expert Syst. Appl. 2012; 39, 2341-54.

A Zouggari and L Benyoucef. Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem. Eng. Appl. Artif. Intell. 2012; 25, 507-19.

A Patnaik, M Kumar, BK Satapathy and BS Tomar. Performance sensitivity of hybrid phenolic composites in friction braking: Effect of ceramic and aramid fiber combination. Wear 2010; 269, 891-9.

CL Hwang and K Yoon. Multiple attribute decision making: methods and applications, Lect. Notes Econ. Math. Syst. 1981; 186, 1-259.

TL Saaty. The analytic hierarchy process. New York, McGraw-Hill, 1980.

M Kamal and AS Al-Harbi. Application of the AHP in project management. Int. J. Proj. Manag. 2001; 19, 19-27.

E Albayrak and YC Erensal. Using analytic hierarchy process (AHP) to improve human performance. An application of multiple criteria decision making problem. J. Intell. Manuf. 2004; 15, 491-503.

M Hajeeh and A Al-Othman. Application of analytical hierarchy process in the selection of desalination plants. Desalination 2005; 174, 97-108.

JJ Buckley. Fuzzy hierarchical analysis. Fuzzy Set. Syst. 1985; 17, 233-47.

LA Zadeh. Fuzzy set. Inform. Contr. 1965; 8, 338-53.

HJ Zimmermann. Fuzzy set theory and its applications. Boston, MA, Kluwer, 1985.

A Kaufmann and MM Gupta. Introduction to fuzzy arithmetic: Theory and applications. New York, Van Nostrand Reinhold, 1985.

LA Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Inform. Sci. 1998; 249, 301-57.

HJ Zimmermann. Fuzzy Set Theory and Its Applications. In: 2nd (ed.). London, Kluwer Academic Publishers, 1991.

JJ Buckley. Ranking alternatives using fuzzy numbers. Fuzzy Set. Syst. 1985; 15, 21-31.

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Published

2013-07-08

How to Cite

SINGH, T., PATNAIK, A., SATAPATHY, B., & TOMAR, B. (2013). Development and Optimization of Hybrid Friction Materials Consisting of Nanoclay and Carbon Nanotubes by using Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under Fuzzy Atmosphere. Walailak Journal of Science and Technology (WJST), 10(4), 343–362. Retrieved from https://wjst.wu.ac.th/index.php/wjst/article/view/357

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Research Article