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Friction 2017, 5(2): 183-193 https://doi.org/10.1007/s40544-017-0145-y
Research Article | Open Access | Issue | Published: 12 May 2017

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

Show Author's Information Hiral H. PARIKH1( ),  Piyush P. GOHIL2
 Department of Mechanical Engineering, School of Science and Engineering, Navrachana University, Vadodara 391410, India
 Department of Mechanical Engineering, Faculty of Technology & Engineering, the MS University of Baroda, Kalabhavan, Vadodara 390001, India
Keywords:
pin-on-disc, wear, artificial neural network, composites, cotton fiber reinforced polyester composites
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PARIKH HH, GOHIL PP. Experimental investigation and prediction of wear behavior of cotton fiber polyester composites. Friction, 2017, 5(2): 183-193. https://doi.org/10.1007/s40544-017-0145-y
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Abstract Full text About this article

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.


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About this article

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

Show Author's information Hiral H. PARIKH1( ), Piyush P. GOHIL2
 Department of Mechanical Engineering, School of Science and Engineering, Navrachana University, Vadodara 391410, India
 Department of Mechanical Engineering, Faculty of Technology & Engineering, the MS University of Baroda, Kalabhavan, Vadodara 390001, India

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

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Publication history

Received: 20 June 2016
Revised: 27 October 2016
Accepted: 11 January 2017
Published: 12 May 2017
Issue date: June 2017

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© The author(s) 2017

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This article is published with open access at Springerlink.com

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