Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16893
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dc.contributor.authorChenrayan, Venkatesh-
dc.contributor.authorShahapurkar, Kiran-
dc.contributor.authorManivannan, Chandru-
dc.contributor.authorRajeshkumar, L-
dc.contributor.authorSivakumar, N-
dc.contributor.authorRajesh sharma, R-
dc.contributor.authorVenkatesan, R-
dc.date.accessioned2024-12-12T09:38:20Z-
dc.date.available2024-12-12T09:38:20Z-
dc.date.issued2024-
dc.identifier.citationVol. 10, No. 16en_US
dc.identifier.issn2405-8440-
dc.identifier.urihttps://doi.org/10.1016/j.heliyon.2024.e36087-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16893-
dc.description.abstractThe implementation of hard-facing alloy on the existing materials caters the need for high-performance surfaces in terms of wear and high temperatures. The present research explore the effect of Plasma Transferred Arc Welding (PTAW) parameters and powder composition on dilution, microstructure and hardness of the commonly used hard-facing alloy Ni–Cr–Si–B powder. The hard-facing alloy was deposited with three weight proportions of boron (2.5 %, 3 % and 3.5 %). The statistical-based Grey Relational Analysis (GRA) followed by a Machine Learning Algorithm (MLA) was implemented to identify the ideal parameters and degree of significance of each parameter and for the prediction of the responses. The dilution percentage, microstructure analysis, and phase detection were estimated through elemental analysis, Scanning electron Microscopy (SEM) and X-ray Diffraction Analysis (XRD) respectively. The experimental and modelling results revealed that 400 mm/min of scanning speed, 8 gm/min of powder delivery, 14 mm of stand-off distance, and 120 A of current were the optimal parameters along with 3.5 wt% of boron powder composition to yield a better dilution, microstructure and hardness. © 2024 The Authorsen_US
dc.language.isoenen_US
dc.publisherHeliyonen_US
dc.publisherElsevier Ltden_US
dc.subjectChromium Borideen_US
dc.subjectDilutionen_US
dc.subjectGraen_US
dc.subjectGrain Growthen_US
dc.subjectHeat Affected Zoneen_US
dc.subjectMachine Learningen_US
dc.titleEffect of Powder Composition, Ptaw Parameters on Dilution, Microstructure and Hardness of Ni–Cr–Si–B Alloy Deposition: Experimental Investigation and Prediction Using Machine Learning Techniqueen_US
dc.typeArticleen_US
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