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Residual Stress Evaluation Using Finite Element Modeling in Turning of Ti-6Al-4V and Its Optimization Using RSM

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dc.contributor.author Kant V.
dc.contributor.author Kartheek G.
dc.contributor.author Vamsi Krishna P.
dc.contributor.author Sukjamsri C.
dc.contributor.other Srinakharinwirot University
dc.date.accessioned 2023-11-15T02:07:44Z
dc.date.available 2023-11-15T02:07:44Z
dc.date.issued 2022
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148227143&doi=10.1520%2fSSMS20220009&partnerID=40&md5=78504264fd5ff8218495c0bcd3243a9b
dc.identifier.uri https://ir.swu.ac.th/jspui/handle/123456789/29119
dc.description.abstract Residual 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.publisher ASTM International
dc.subject residual stresses
dc.subject response surface methodology
dc.subject Ti-6Al-4V alloy
dc.subject residual stresses
dc.subject response surface methodology
dc.subject Ti-6Al-4V alloy
dc.title Residual Stress Evaluation Using Finite Element Modeling in Turning of Ti-6Al-4V and Its Optimization Using RSM
dc.type Article
dc.rights.holder Scopus
dc.identifier.bibliograpycitation Smart and Sustainable Manufacturing Systems. Vol 6, No.1 (2022), p.247-261
dc.identifier.doi 10.1520/SSMS20220009


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