Experimental study of lubricating wear phenomena in Ag-Mg alloy systems

Citation

Venkatasudhahar, M. and Muthukumar, S. and Kumar, N. Mathan and Kumar, P. Manoj and Al Awadh, Mohammed and Rajendran, Parvathy and Lee, It Ee (2025) Experimental study of lubricating wear phenomena in Ag-Mg alloy systems. Scientific Reports, 15 (1). ISSN 2045-2322

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Abstract

This investigation presents a comprehensive investigation into the lubricating wear properties of Ag-Mg alloys, focusing on pin-on-disc wear tests conducted under various lubrication conditions, including gear oil, filtered water, and SAE 80w, as well as Vickers hardness testing for material hardness characterization. The researchers employed cutting-edge techniques such as SEM and XRD to meticulously analyze the size, shape, morphology, and composition of the alloys. To establish connections between the coefficient of friction and wear rate data, the study further examines the underlying wear mechanisms using SEM and XRD. An in-depth examination of surface wear characteristics and the identification of tribo-layers provides crucial insights into the material changes during the wear process. In addition to microstructural investigation, tribo-layer thickness evaluation, and phase identification within the layers, the researchers also explored cross-sectional wear aspects. Furthermore, surface profile metry methods were utilized to create detailed 3D surface maps that accurately capture the post-wear test surface changes. To gain a comprehensive understanding of the tribological behavior of Ag-Mg alloys, this research also undertakes a critical comparative analysis of the acquired data with existing literature on Mg alloys. The findings from this study offer valuable knowledge for enhancing the performance of Ag-Mg alloys in engineering applications by addressing wear-related issues.

Item Type: Article
Uncontrolled Keywords: Lubricant effects, wear mechanism
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1-484 Motor vehicles. Cycles
Divisions: Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 07 Jan 2026 02:18
Last Modified: 07 Jan 2026 08:14
URII: http://shdl.mmu.edu.my/id/eprint/15160

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