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Experimental Analysis and Modeling for Reciprocating Wear Behavior of Nanocomposite Coatings.

Nazir, M.H., Khan, Z. A., Saeed, A., Bakolas, V., Braun, W. and Bajwa, R., 2018. Experimental Analysis and Modeling for Reciprocating Wear Behavior of Nanocomposite Coatings. Wear, 416- (December), 89-102.

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DOI: /10.1016/j.wear.2018.09.011


This paper presents the study of wear responses of nanocomposite coatings with a steel ball under oscillatingreciprocating state. Nanocomposite coatings for this study include: Nickel-Alumina (Ni/Al2O3), Nickel-Silicon Carbide (Ni/SiC), Nickel-Zirconia (Ni/ZrO2) and Ni/Graphene. Ni/ZrO2 exhibited maximum wear rate followed by Ni/SiC, Ni/Al2O3 and Ni/Graphene respectively which was also assured by Scanning Electron Microscopy (SEM) micrographs, grain sizes, hardness, porosity, surface stresses, frictional coefficients behaviours and “Ushaped" wear depth profiles. The “U-shaped” profiles were utilised to calculate the energy distribution (Archard factor density) along the interface. A novel mechano-wear model incorporating the energy distribution equations with the mechanics equations was developed for analysing the effects of intrinsic mechanical properties (such as grain sizes, hardness, porosity, surface stresses of the nanocomposite coatings) on the wear response. The predictions showed close agreement with the experimental results. In conclusion Ni/Graphene exhibited better anti-wear properties compared to other nanocomposite coatings. The high anti-wear behaviour of Ni/Graphene composite is due to enhanced strengthening effects in the presence of graphene. The importance of this work is evident from various industrial applications which require reliable modelling techniques to predict coatings failures due to wear. This work will bring significant impact to precision manufacturing, wind turbine industries, automotive, locomotive and aerospace in overcoming critical wear failures.

Item Type:Article
Uncontrolled Keywords:Reciprocating wear; Nanocomposite coatings; Modelling; Simulation
Group:Faculty of Science & Technology
ID Code:31289
Deposited By: Symplectic RT2
Deposited On:01 Oct 2018 09:11
Last Modified:14 Mar 2022 14:12


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