Neural network supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating

被引:0
|
作者
Singh J. [1 ]
Singh S. [2 ]
机构
[1] Department of Engineering, Punjab State Aeronautical Engineering College, Punjab, Patiala
[2] School of Computer Science Engineering and Technology, Bennett University, Uttar Pradesh, Greater Noida
关键词
Erosion; HVOF technique; Machine learning; Neural networks; Wear;
D O I
10.1016/j.jksues.2021.12.005
中图分类号
学科分类号
摘要
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. © 2021 The Authors
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页码:662 / 676
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