Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating
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 microstruct...
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Elsevier
2024-12-01
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| Series: | Journal of King Saud University: Engineering Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018363921001768 |
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| author | Jashanpreet Singh Simranjit Singh |
| author_facet | Jashanpreet Singh Simranjit Singh |
| author_sort | Jashanpreet Singh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-078e596f8bdd41ca9fa37bccdaadeb6d |
| institution | Kabale University |
| issn | 1018-3639 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Engineering Sciences |
| spelling | doaj-art-078e596f8bdd41ca9fa37bccdaadeb6d2024-12-22T05:27:29ZengElsevierJournal of King Saud University: Engineering Sciences1018-36392024-12-01368662676Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coatingJashanpreet Singh0Simranjit Singh1Department of Engineering, Punjab State Aeronautical Engineering College, Patiala 147001, Punjab, India; Corresponding author.School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, Uttar Pradesh, IndiaIn 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.http://www.sciencedirect.com/science/article/pii/S1018363921001768Neural networksMachine learningErosionWearHVOF technique |
| spellingShingle | Jashanpreet Singh Simranjit Singh Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating Journal of King Saud University: Engineering Sciences Neural networks Machine learning Erosion Wear HVOF technique |
| title | Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating |
| title_full | Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating |
| title_fullStr | Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating |
| title_full_unstemmed | Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating |
| title_short | Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating |
| title_sort | neural network supported study on erosive wear performance analysis of y2o3 wc 10co4cr hvof coating |
| topic | Neural networks Machine learning Erosion Wear HVOF technique |
| url | http://www.sciencedirect.com/science/article/pii/S1018363921001768 |
| work_keys_str_mv | AT jashanpreetsingh neuralnetworksupportedstudyonerosivewearperformanceanalysisofy2o3wc10co4crhvofcoating AT simranjitsingh neuralnetworksupportedstudyonerosivewearperformanceanalysisofy2o3wc10co4crhvofcoating |