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Research Papers: Lubricants

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
e-mail: shubrajit.b@ktr.srmuniv.ac.in

Shubhabrata Datta

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

S. D. Pathak

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

1Corresponding author.

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received December 31, 2016; final manuscript received March 24, 2017; published online June 30, 2017. Assoc. Editor: Ning Ren.

J. Tribol 139(6), 061802 (Jun 30, 2017) (13 pages) Paper No: TRIB-16-1408; doi: 10.1115/1.4036379 History: Received December 31, 2016; Revised March 24, 2017

The present work investigates the tribological properties of castor oil with various carbonaceous friction modifiers (nano and microsize 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, multiwalled carbon nanotube (MWCNT), and multilayered graphene are used as friction modifiers (FMs) 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 analyzed using a three-dimensional noncontact type profilometer, scanning electron microscope (SEM), and energy dispersive system (EDS). Decrease in surface roughness indicated better antiwear properties in case of nanofriction modifiers-based castor oil as compared to micrographite-based and neat castor oil (NCO). 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.

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Figures

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Fig. 1

Scanning electron microscope images of (a) graphite microparticles, (b) multiwall carbon nanotubes, and (c) graphene

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Fig. 2

Schematic diagram of four-ball test rig

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Fig. 3

Schematic diagram of perceptron type ANN used in this work

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Fig. 4

Coefficient of friction for various oil samples

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Fig. 5

Variation of (a) coefficient of friction, (b) scar diameter versus contact pressure, and (c) scar diameter with FTP for CMO, NCO, and optimum FM concentrations

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Fig. 6

Three-dimensional images of wear scar on balls before and after the tests: (a) untested ball, (b) CMO, (c) NCO, (d) NCO + 2 wt. % graphite, (e) NCO + 1 wt. % MWCNT, and (f) NCO + 0.5 wt. % graphene

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Fig. 7

Optical microscope images for surfaces of balls at LNSL: (a) and (b) CMO, (c) and (d) NCO, (e) and (f) NCO + 2 wt. % graphite, (g) and (h) NCO + 1 wt. % MWCNT, and (i) and (j) NCO + 0.5 wt. % graphene

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Fig. 8

Optical microscope images for surfaces of balls at ISL: (a) and (b) CMO, (c) and (d) NCO, (e) and (f) NCO + 2 wt. % graphite, (g) and (h) NCO + 1 wt. % MWCNT, and (i) and (j) NCO + 0.5 wt. % graphene

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Fig. 9

Wear scar diameter versus load curve for (a) CMO, (b) NCO, (c) NCO + 2.0 wt. % graphite, (d) NCO + 1.0 wt. % MWCNT, (e) NCO + 0.5 wt. % graphene, and (f) LNSL, ISL, and WL of oil samples

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Fig. 10

(a) LNSL and (b) ISL versus wear scar diameter for different oil samples

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Fig. 11

SEM images of wear of (a) and (b) graphite-based NCO, (c) and (d) MWCNT-based NCO, and (e) and (f) graphene-based NCO samples

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Fig. 12

EDS spectrum of (a) CMO, (b) NCO, (c) NCO + 2.0 wt. % graphite, (d) NCO + 1.0 wt. % MWCNT, and (e) NCO + 0.5 wt. % graphene

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Fig. 13

Scatter plot showing the predictivity and sensitivity analysis of the ANN model

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Fig. 14

Surface views showing the combined effects of (a) E and FMC, (b) SA and FMC, (c) G and SA, and (d) E and ILS on CoF as predicted by the ANN model

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