Modeling and multi-response optimization of milling characteristics based on Taguchi and gray relational analysis

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dc.authoridSarikaya, Murat/0000-0001-6100-0731
dc.authoridDilipak, Hakan/0000-0003-3796-8181
dc.contributor.authorSarikaya, Murat
dc.contributor.authorYilmaz, Volkan
dc.contributor.authorDilipak, Hakan
dc.date.accessioned2025-03-23T19:30:43Z
dc.date.available2025-03-23T19:30:43Z
dc.date.issued2016
dc.departmentSinop Üniversitesi
dc.description.abstractThis article focuses on experimental investigation and effective approach to optimize the milling characteristics with mono and multiple response outputs such as vibration signals, cutting force, and surface roughness. To achieve this goal, experiments were designed based on Taguchi's L-18 (2(1)x3(3)) orthogonal array. During the milling of AISI 1050 steel, process performance indicators such as vibration signals (RMS), cutting force (Fx), and surface roughness (Ra) were measured. The effect of process parameters such as depth of cut, feed rate, cutting speed, and number of insert on RMS, Fx, and Ra were investigated and parameters were simultaneously optimized by taking into consideration the multi-response outputs using Taguchi-based gray relational analysis. Taguchi's signal-to-noise ratio was employed to obtain the best combination with smaller-the-better and larger-the-better approaches for mono- and multi-optimization, respectively. Analysis of variance was conducted to determine the importance of process parameters on responses. Mathematical models were created, namely, RMSpre, Ra-pre, and Fx(pre), using regression analysis. According to the multi-response optimization results, which were obtained from the largest signal-to-noise ratio of the gray relational grade, it was found out that the optimum combination was depth of cut of 1mm, feed rate of 0.05mm/rev, cutting speed of 308m/min, and number of insert of 1 to minimize simultaneously RMS, Fx, and Ra. It was obtained that the percentage improvement in gray relational grade with the multiple responses is 42.9%. It is clearly shown that the performance indicators are significantly improved using this approach in milling of AISI 1050 steel. Moreover, analysis of variance for gray relational grade proved that the feed rate is the most influential factor as the minimization of all responses is concurrently considered.
dc.identifier.doi10.1177/0954405414565136
dc.identifier.endpage1065
dc.identifier.issn0954-4054
dc.identifier.issn2041-2975
dc.identifier.issue6
dc.identifier.scopus2-s2.0-84983239002
dc.identifier.scopusqualityQ2
dc.identifier.startpage1049
dc.identifier.urihttps://doi.org/10.1177/0954405414565136
dc.identifier.urihttps://hdl.handle.net/11486/5151
dc.identifier.volume230
dc.identifier.wosWOS:000378421700006
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofProceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectCutting
dc.subjectoptimization
dc.subjectsurfaces
dc.subjectvibration
dc.subjectmodeling
dc.subjectforce
dc.titleModeling and multi-response optimization of milling characteristics based on Taguchi and gray relational analysis
dc.typeArticle

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