Advertisement

Friction

, Volume 5, Issue 2, pp 183–193 | Cite as

Experimental investigation and prediction of wear behavior of cotton fiber polyester composites

  • Hiral H. Parikh
  • Piyush P. Gohil
Open Access
Research Article

Abstract

The cotton fiber reinforced polyester composites were fabricated with varying amount of graphite fillers (0, 3, 5 wt.%) with a hand lay-up technique. Wear tests were planned by using a response surface (Box Behnken method) design of experiments and conducted on a pin-on-disc machine (POD) test setup. The effect of the weight percentage of graphite content on the dry sliding wear behavior of cotton fiber polyester composite (CFPC) was examined by considering the effect of operating parameters like load, speed, and sliding distance. The wear test results showed the inclusion of 5 wt.% of graphite as fillers in CFPC increase wear resistance compared to 3 wt.% of graphite fillers. The graphite fillers were recommended for CFPC to increase the wear resistance of the material. A scanning electron microscope (SEM) was used to study the wear mechanism. To predict the wear behavior of the composite material, comparisons were made between the general regression technique and an artificial neural network (ANN). The conformation test results revealed the predicted wear with the ANN was acceptable when compared with the actual experimental results and the regression mathematical models.

Keywords

wear composites cotton fiber reinforced polyester composites artificial neural network pin-on-disc 

References

  1. [1]
    Mohammad L, Ansari M N M, Grace P, Jawaid M, Islam M S. A review on natural fiber reinforced polymer composite and its applications. International Journal of Polymer Science 2015: 243947 (2015)Google Scholar
  2. [2]
    Chittaranjan D, Acharya S K. Effect of fiber content on abrasive wear of Lantana Camara fiber reinforced polymer matrix composite. Indian Journal of Engineering and Materials Sciences 17: 219–223 (2010)Google Scholar
  3. [3]
    Chin C W, Yousif B F. Potential of kenaf fibers as reinforcement for tribological applications. Wear 267: 1550–1557 (2009)CrossRefGoogle Scholar
  4. [4]
    Kranthi G, Nayak R, Biswas S, Satapapathy A. Wear performance evaluation of pine wood dust filled epoxy composites. In Proceeding of the International conference on advancements in Polymeric Materials APM, 2010.Google Scholar
  5. [5]
    Yousif B F, Lau S T W, McWilliam S. Polyester composite based on betelnut fiber for tribological application. Tribology International 43: 503–511 (2010)CrossRefGoogle Scholar
  6. [6]
    Mishra P, Acharya S K. Anisotropy abrasive wear behavior of bagasse fiber reinforced polymer composite. International Journal of Engineering Science and Technology 2(11): 104–112 (2010)Google Scholar
  7. [7]
    Sayed E L, El-Sherbiny MG, Abo-El-Ezz A S, Aggag G A. Friction and wear properties of polymeric composite materials for bearing applications. Wear 184: 45–53 (1995)CrossRefGoogle Scholar
  8. [8]
    Yousif Belal F, Leong O B, Ong L K, Jye W K. The effect of treatment on tribo performance of CFRP composites. Recent Patents on Materials Science 2: 67–74 (2009)CrossRefGoogle Scholar
  9. [9]
    Bhoopathi L, Sampath P S, Mylsamy K. Influence of fiber length in the wear behaviour of borassus fruit fiber reinforced epoxy composites. International Journal of Engineering Science and Technology 4(9): 4119–4129 (2009)Google Scholar
  10. [10]
    Yousif B F, El-Tayeb N S M. Wet adhesive wear characteristics of untreated oil palm fiber reinforced polyester and treated oil palm fiber reinforced polyester composites using the pin on disc and block on ring techniques. Journal of Engineering Tribology 224: 123–131 (2009)Google Scholar
  11. [11]
    Umar N, Jamil H, Low K O. Adhesive wear and frictional performance of bamboo fibers reinforced epoxy. International Journal of Tribology 47: 122–133 (2012)CrossRefGoogle Scholar
  12. [12]
    Majhi S, Samantarai S P, Acharya S K. Tribological behavior of modified rice husk filled epoxy composite. International Journal of Science & Engineering Research 3(6): 1–5 (2012)Google Scholar
  13. [13]
    Dwivedi U K, Ghosh A, Navin C. Abrasive wear behavior of bamboo powder filled polyester composites. Journal of Bio Resources 2(4): 693–698 (2007)Google Scholar
  14. [14]
    Chandra C K R, Madhusudan S, Raghavendra G, Venkateswara R E. Investigation into wear behaviour of coir fiber reinforced epoxy composites with taguchi method. Journal of Engineering Research and Applications 2(5): 371–374 (2012)Google Scholar
  15. [15]
    Narish S B F Y, Dirk R. Tribological characteristics of sustainable fiber-reinforced thermoplastic composites under wet adhesive wear. Tribology Transactions 54: 736–748 (2011)CrossRefGoogle Scholar
  16. [16]
    Bijwe J, Indumathi J, John Rajesh J, Fahim M. Friction and wear behavior of polyetherimide composites in various wear modes. Wear 249: 715–726 (2001)CrossRefGoogle Scholar
  17. [17]
    Kolluri D K, Satapathy B K, Bijwe J, Ghosh A K. Analysis of load and temperature dependence of tribo-performance of graphite filled phenolic composites. Materials Science and Engineering A 456: 162–169 (2007)CrossRefGoogle Scholar
  18. [18]
    Tu J V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology 49(11): 1225–1231 (1996)CrossRefGoogle Scholar
  19. [19]
    He W, Bao G H, Ge T J, Luyt A S, Jian X G. Artificial neural networks in prediction of mechanical behavior of high performance plastic composites. Polymer Processing 501: 27–31 (2012)Google Scholar
  20. [20]
    Aymerich F, Serra M. Prediction of fatigue strength of composite laminates by means of neural networks. Key Engineering Materials 144: 231–240 (1998)CrossRefGoogle Scholar
  21. [21]
    Velten K, Reinicke R, Friedrich K. Wear volume prediction with artificial neural networks. Tribology International 33: 731–736 (2000)CrossRefGoogle Scholar
  22. [22]
    Pleune T T, Chopra O K. Using artificial neural networks to predictthe fatigue life of carbon and low-alloy steels. Nuclear Engineering and Design 197: 1–12 (2000)CrossRefGoogle Scholar
  23. [23]
    Haque M, Sudhakar K V. Prediction of corrosion-fatigue behavior of DP steel through artificial neural network. International Journal of Fatigue 23: 1–4 (2001)CrossRefGoogle Scholar
  24. [24]
    Allan G, Yang R, Fotheringham A, Mather R. Neural modeling of polypropylene fiber processing: predicting the structure and properties and identifying the control parameters for specified fibers. Journal of Materials Science 36: 3113–3118 (2001)CrossRefGoogle Scholar
  25. [25]
    EI Kadi H, AI Asaaf Y. Prediction of the fatigue life of unidirectional glass fiber/epoxy composite laminate using different neural network paradigms. Composite Structures 55(2): 239–246 (2002)CrossRefGoogle Scholar
  26. [26]
    Jia J, Davalos J F. An artificial neural network for the fatigue study of bonded FRP-wood interfaces. Composite Structures 74(1): 106–114 (2006)CrossRefGoogle Scholar
  27. [27]
    EI Kadi H. Modeling the mechanical behavior of fiberreinforced polymeric composite materials using artificial neural networks—A review. Composite Structures 73(1): 1–23 (2006)CrossRefGoogle Scholar
  28. [28]
    Sha W. Some comments on ‘investigative study on machinability aspects of unreinforced and reinforced peek composite machining using ann model. Journal of Reinforced Plastics and Composites 30: 641–642 (2011)CrossRefGoogle Scholar
  29. [29]
    Shalwan A, Yousif B F. Influence of date palm fibre and graphite filler on mechanical and wear characteristics of epoxy composites. Materials and Design 59: 264–273 (2014)CrossRefGoogle Scholar
  30. [30]
    Shivamurthy B S, Prabhuswamy M S. Influence of SiO2 fillers on sliding Wear resistance and mechanical properties of compression molded glass epoxycomposites. Journal of Minerals & Materials Characterization & Engineering 8(7): 513–530 (2009)CrossRefGoogle Scholar
  31. [31]
    Rajesh S, Rajakarunakaran S, Sudhakara P. Modelling and optimization of sliding specific wear and coefficient of friction of Al based red mud, metal matrix composite using Taguchi method and RSM. Journal of Materials Physics and Mechanics 15: 150–166 (2012)Google Scholar
  32. [32]
    Basavarajappa S, Arun K V, Davim J. P. Effect of filler material on dry sliding wear behavior of polymer matrix composites─A Taguchi approach. Journal of Minerals & Materials Characterization & Engineering 8(5): 379–391 (2009)CrossRefGoogle Scholar

Copyright information

© The author(s) 2017

Open Access: The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, School of Science and EngineeringNavrachana UniversityVadodaraIndia
  2. 2.Department of Mechanical Engineering, Faculty of Technology & Engineeringthe M S University of BarodaKalabhavan, VadodaraIndia

Personalised recommendations