Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/29119
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dc.contributor.authorKant V.
dc.contributor.authorKartheek G.
dc.contributor.authorVamsi Krishna P.
dc.contributor.authorSukjamsri C.
dc.contributor.otherSrinakharinwirot University
dc.date.accessioned2023-11-15T02:07:44Z-
dc.date.available2023-11-15T02:07:44Z-
dc.date.issued2022
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85148227143&doi=10.1520%2fSSMS20220009&partnerID=40&md5=78504264fd5ff8218495c0bcd3243a9b
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/29119-
dc.description.abstractResidual stresses play a crucial role in the functional life of the component. Stresses develop because of mechanical and thermal loads during machining. Usually, compressive residual stresses are desirable for an improved fatigue life of the component. This study focuses on the effects of machining parameters on residual stresses while machining Ti-6Al-4V. A finite element model was developed and validated with experimental results. Different levels of cutting speed, feed rate, and depth of cut are used for evaluating residual stress, cutting force, and cutting temperature. Statistical modeling and optimization are carried out using response surface methodology. From the results it is observed that a machining speed of 45 m/min, a feed of 0.1 mm/rev, and a depth of cut of 1.84 mm help attain better responses. The feed and speed directly influence the residual stresses developed in machining. However, feed was found to be a more dominating parameter than speed. Copyright © 2022 by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959.
dc.publisherASTM International
dc.subjectresidual stresses
dc.subjectresponse surface methodology
dc.subjectTi-6Al-4V alloy
dc.subjectresidual stresses
dc.subjectresponse surface methodology
dc.subjectTi-6Al-4V alloy
dc.titleResidual Stress Evaluation Using Finite Element Modeling in Turning of Ti-6Al-4V and Its Optimization Using RSM
dc.typeArticle
dc.rights.holderScopus
dc.identifier.bibliograpycitationSmart and Sustainable Manufacturing Systems. Vol 6, No.1 (2022), p.247-261
dc.identifier.doi10.1520/SSMS20220009
Appears in Collections:Scopus 2022

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