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Please use this identifier to cite or link to this item: http://lrcdrs.bennett.edu.in:80/handle/123456789/258
Title: Neural network supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating
Authors: Singh, Simranjit
Keywords: Neural networks
Machine learning
Erosion
Wear HVOF technique
Issue Date: 2022
Publisher: Springer Science and Business Media Deutschland GmbH
Citation: Singh, J., & Singh, S. (2021). Neural network supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating. In Journal of King Saud University - Engineering Sciences. Elsevier BV.
Abstract: In this work, a study was carried out by modifying the conventional Tungsten Carbide Cobalt Chrome (WC–10Co4Cr) powder with a small addition of yttrium-oxide (Y2O3). Reinforcement was done by adding yttria (Y2O3) ceramics in WC–10Co4Cr powder by using a jar ball mill process. The surface microstructure, chemical composition, and phase compositions of coating powder and coatings were examined by using scanning electron microscopy, energy dispersive spectroscopy, and X-ray diffractometry. Silt erosion was evaluated through a pot tester by preparing equi- and multi-sized slurries at different velocities, impact angles, concentrations, and rates. Results show that the WC–10Co4Cr powder coating reinforced by Y2O3 ceramics possesses low porosity, providing higher erosive performance as compared to conventional WC–10Co4Cr coating. The present study reveals that the deposition of conventional WC–10Co4Cr coating helps improve the wear resistance of AISI 316L stainless steel (UNS S31600) by 9.98% for the variation in rotational speed. However, the erosive wear performance of conventional WC–10Co4Cr coating was improved by 45.9% by blending it with the Y2O3 ceramics.
URI: http://lrcdrs.bennett.edu.in:80/handle/123456789/258
ISSN: 1018-3639
Appears in Collections:Journal Articles_SCSET

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