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Image Texture Description, Surface-Grains Structure Morphology and Prediction of Wear Parameters for Mg/B4C Composites Using Response Surface Methodology

Publication Type : Journal Article

Publisher : Springer Science and Business Media LLC

Source : Metals and Materials International

Url : https://doi.org/10.1007/s12540-021-01042-2

Campus : Nagercoil

School : School of Computing

Year : 2021

Abstract : The different percentages of boron carbide (B4C) reinforced Magnesium metal matrix composites were processed using Powder Metallurgy method. The characterizations are performed by Scanning Electronic Microscope (SEM), X-ray Diffraction pattern, Electron Backscattered Diffraction, and Energy Dispersive Spectrum. The texture analysis of the composites is conducted via an image processing method. Wear tests are executed in the dry sliding state under ambient room conditions. The three levels Box-Behnken design of Response Surface Methodology has been selected to create the mathematical models for the three variables (weight percentage of B4C, sliding velocity, and load) to predict the responses namely specific wear rate (SWR) and coefficient of friction (COF). The developed model is validated and analyzed through the Analysis of variance (ANOVA). This model has a 95% efficient confidence to forecast the COF and SWR. The worn surface features are extracted and analyzed through an image processing technique. The roughness measurement of the worn surfaces is executed via Atomic Force Microscopy analysis.

Cite this Research Publication : M. Navaneetha Krishnan, S. Suresh, S. C. Vettivel, C. Emmy Prema, J. Arun, P. Thirunamakodi, Image Texture Description, Surface-Grains Structure Morphology and Prediction of Wear Parameters for Mg/B4C Composites Using Response Surface Methodology, Metals and Materials International, Springer Science and Business Media LLC, 2021, https://doi.org/10.1007/s12540-021-01042-2

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