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research-article

Analyses of tribological properties of castor oil with various carbonaceous micro- and nano- friction modifiers

[+] Author and Article Information
Shubrajit Bhaumik

Department of Mechanical Engineering, SRM University Kattankulathur, Chennai, 603203, India
shubrajit.b@ktr.srmuniv.ac.in

Shubhabrata Dutta

Department of Mechanical Engineering, SRM University Kattankulathur, Chennai, 603203, India
shubhabrata.p@ktr.srmuniv.ac.in

Sateesh Pathak

Department of Mechanical Engineering, SRM University Kattankulathur, Chennai, 603203, India
sdpa12@gmail.com

1Corresponding author.

ASME doi:10.1115/1.4036379 History: Received December 31, 2016; Revised March 24, 2017

Abstract

The present work investigates the tribological properties of castor oil with various carbonaceous friction modifiers (nano and micro size additives) assessed using four ball tester as per ASTM D 4172 and ASTM D 2783. Castor oil has been chosen because of its high viscosity and ease of availability. Graphite (GRT), multi-walled carbon nanotube (MWCNT) and multi layered graphene (GRPHN) are used as friction modifiers in castor oil on weight percentage basis. Significant enhancements of tribological properties with a certain level of concentration of friction modifiers have been observed. The surface features of the tested balls were also analyzed using a three-dimensional non-contact type profilometer, scanning electron microscope (SEM) and energy dispersive system (EDS). Decrease in surface roughness indicated better anti-wear properties in case of friction modifiers based castor oil as compared to micro (GRT) based and neat castor oil. The formation of tribo film and mending effect as detected by SEM and EDS are most likely mechanisms for enhancing the tribological properties. In order to assess the suitability of castor oil as a replacement for mineral oil, the results of castor oil samples are also compared with commercially available mineral oil. The tribological properties of castor oil are found to be competitive and generally superior to the mineral gear oil. The data generated are used to develop a neural network model to map the input-output correlation.

Copyright (c) 2017 by ASME
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