Multi-response optimization and modeling of machinability indicators in the turning of duplex stainless steel

ArticleAçık Erişim

Özet

Duplex stainless steels have a two-phase microstructure containing both ferritic and austenitic phases and have high strength, hardness, and corrosion resistance. However, these properties complicate chip formation during machining and make chip control difficult. Lack of chip control during the turning process negatively affects machining efficiency, quality, and safety. Inadequate chip control results in an inability to manage unwanted chip formation, especially long and tangled chips. This affects both machine operations and workpiece quality. Therefore, machining parameters need to be optimized and carefully examined to ensure chip control. The objective of this study was to detect the optimal combination of processing parameters for decreasing cutting force and surface roughness throughout the turning of AISI 2205 duplex stainless steel. To attain this goal, the experiments used the Taguchi L18 orthogonal array experimental design. Two distinct chip breaker forms of cemented carbide cutting inserts (NR4, NM4), three distinct feed rates (0.1–0.2–0.3 mm/rev), and three distinct cutting speed (180–210–240 m/min) values were used as machining parameters. As a consequence of the study, the lowest cutting force and surface roughness were obtained at NR4 insert, 0.1 mm/rev feed rate and, 210 m/min cutting speed. Using the entropy weighted-TOPSIS approach, a multi-objective optimization process was carried out for surface roughness and cutting force. As a result of the optimization approach, the sequence of experiments in which the most suitable processing parameters were obtained was determined. Anova study shows that feed rate is the most efficient parameter for both surface roughness and cutting force. To forecast processing outputs, mathematical models were constructed utilizing the response surface method. In summary, the findings from this study can contribute to the improvement of the productivity required by the industry in turning AISI 2205 duplex stainless steel. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024.

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RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/ebyu/11144Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1007/s40430-024-05227-9Yayıncı resmi DOI adresi
Tam Kayıt
AlanDeğerDil
dc.contributor.authorSiyambaş, Yusuf
dc.contributor.authorMemiş, Fatih
dc.contributor.authorTurgut, Yakup
dc.date.accessioned2026-10-18T20:26:14.771525456Z
dc.date.available2026-10-18T20:26:14.771525456Z
dc.date.issued2024
dc.description.abstractDuplex stainless steels have a two-phase microstructure containing both ferritic and austenitic phases and have high strength, hardness, and corrosion resistance. However, these properties complicate chip formation during machining and make chip control difficult. Lack of chip control during the turning process negatively affects machining efficiency, quality, and safety. Inadequate chip control results in an inability to manage unwanted chip formation, especially long and tangled chips. This affects both machine operations and workpiece quality. Therefore, machining parameters need to be optimized and carefully examined to ensure chip control. The objective of this study was to detect the optimal combination of processing parameters for decreasing cutting force and surface roughness throughout the turning of AISI 2205 duplex stainless steel. To attain this goal, the experiments used the Taguchi L18 orthogonal array experimental design. Two distinct chip breaker forms of cemented carbide cutting inserts (NR4, NM4), three distinct feed rates (0.1–0.2–0.3 mm/rev), and three distinct cutting speed (180–210–240 m/min) values were used as machining parameters. As a consequence of the study, the lowest cutting force and surface roughness were obtained at NR4 insert, 0.1 mm/rev feed rate and, 210 m/min cutting speed. Using the entropy weighted-TOPSIS approach, a multi-objective optimization process was carried out for surface roughness and cutting force. As a result of the optimization approach, the sequence of experiments in which the most suitable processing parameters were obtained was determined. Anova study shows that feed rate is the most efficient parameter for both surface roughness and cutting force. To forecast processing outputs, mathematical models were constructed utilizing the response surface method. In summary, the findings from this study can contribute to the improvement of the productivity required by the industry in turning AISI 2205 duplex stainless steel. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024.
dc.identifier.doi10.1007/s40430-024-05227-9
dc.identifier.eissn1806-3691
dc.identifier.issn1678-5878
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85206394817
dc.identifier.startpage651
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/11144
dc.identifier.volume46
dc.identifier.wosWOS:001331228600004
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofJournal of the Brazilian Society of Mechanical Sciences and Engineering
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCutting force
dc.subjectDuplex stainless steels
dc.subjectMachinability
dc.subjectModeling
dc.subjectSurface roughness
dc.subjectTOPSIS
dc.subjectAustenitic stainless steel
dc.subjectCarbide cutting tools
dc.subjectCorrosion resistance
dc.subjectCutting
dc.subjectSteel corrosion
dc.subject2205 duplex stainless steel
dc.subjectChip control
dc.subjectChip formations
dc.subjectCutting forces
dc.subjectDuplex stainless
dc.subjectFeedrate
dc.subjectMachining parameters
dc.subjectProcessing parameters
dc.subjectDuplex stainless steel
dc.titleMulti-response optimization and modeling of machinability indicators in the turning of duplex stainless steel
dc.typeArticle
dspace.entity.typePublication
rdl.citation.scopus9
rdl.citation.wos10
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)