Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55259
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dc.contributor.authorKandavel, Thanjavur Krishnamoorthy-
dc.contributor.authorKumar, Thangaiyan Ashok-
dc.contributor.authorVaramban, Emaya-
dc.date.accessioned2020-09-23T09:56:38Z-
dc.date.available2020-09-23T09:56:38Z-
dc.date.issued2020-06-
dc.identifier.issn0975-1017 (Online); 0971-4588 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/55259-
dc.description503-517en_US
dc.description.abstractIn the present research work, the effects of Titanium (Ti) and Tungsten (W) addition on tribological behavior of powder metallurgy (P/M) Fe-1%C steel have been investigated. The test specimens of plain carbon steel and 1%Ti, 1%W and 1%Ti+1%W added plain carbon steels were used to conduct the wear tests and wear behavior analyses. The optical and SEM images of wear tracks and microstructures of the alloys were obtained and analysed with wear behavior of the alloy steels. Artificial Neural Network (ANN) software was used to check the degree of agreement of test results with predicted values. The experimental results show that Ti and W added alloy steel exhibits excellent wear resistance. The carbides formation due to alloying elements pronounces the wear resistance of the alloy steel. It has been proven that ANN could be used as a tool to predict the wear behavior of the P/M alloy steels by agreement between the predicted and experimental values.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJEMS Vol.27(3) [June 2020]en_US
dc.subjectWearen_US
dc.subjectMass lossen_US
dc.subjectP/M alloy steelsen_US
dc.subjectFriction co-efficienten_US
dc.subjectArtificial neural networken_US
dc.titlePrediction of tribological characteristics of powder metallurgy Ti and W added low alloy steels using artificial neural networken_US
dc.typeArticleen_US
Appears in Collections:IJEMS Vol.27(3) [June 2020]

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