Estimation of the Dominant Process Parameters on Coating Thickness in a Continuous Galvanizing Line With Computational Fluid Dynamics and Machine Learning Approaches

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dc.contributor.authorSimsir, Cansu
dc.contributor.authorBilgin, Metin
dc.contributor.authorTuran, Osman
dc.date.accessioned2026-02-08T15:14:47Z
dc.date.available2026-02-08T15:14:47Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, the dominant process parameters in the air-jet continuous galvanizing line on coating thickness were estimated by computational fluid dynamics and machine learning approaches. First, 128 different cases consisting of different levels of process parameters were created with the Taguchi method. Then, numerical analyses were performed for each case, calculating the maximum pressure gradient and maximum shear stress values on the strip, which were then used in the analytical model developed based on one-dimensional lubrication theory to obtain coating thickness values. Lastly, artificial intelligence techniques based on different machine learning algorithms such as K-Nearest Neighbors, linear regression, random forest and Adaboost, the relative effects of the process parameters influencing the coating thickness were compared through the feature importance values. It was observed that the dominant process parameters differ in low and high jet pressure cases. Accordingly, in the case of low jet pressure, air jet pressure, nozzle slot opening and velocity of the steel strip stand out as the dominant parameters, while in the case of high jet pressure, the most effective parameters influencing the coating thickness are air jet pressure and nozzle slot opening. In addition to this, the effect of the distance between the nozzle and the zinc pot influencing the coating thickness can also be neglected in both low and high pressure cases. Moreover, it was also noticed that the effects of nozzle angle and the distance between the nozzle and the steel strip influencing the coating thickness increase with increasing jet pressure.
dc.description.sponsorshipTUBITAK [119C074]; Borcelik Steel Industry Trade Inc.; Bursa Technical University; Scientific and Technological Research Council of Turkiye (TUEBITAK)
dc.description.sponsorshipThis work is supported by the TUBITAK-2244 project numbered 119C074 in partnership with Borcelik Steel Industry Trade Inc., Bursa Technical University and The Scientific and Technological Research Council of Turkiye (TUEBITAK). We thank the relevant institutionsfor their valuable support.
dc.identifier.doi10.1002/nme.70213
dc.identifier.issn0029-5981
dc.identifier.issn1097-0207
dc.identifier.issue24
dc.identifier.scopus2-s2.0-105024685265
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/nme.70213
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5438
dc.identifier.volume126
dc.identifier.wosWOS:001651547500006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal For Numerical Methods in Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectartificial intelligence
dc.subjectcomputational fluid dynamics
dc.subjectgalvanized coating
dc.subjectmachine learning
dc.subjectrandom forest
dc.subjectTaguchi method
dc.titleEstimation of the Dominant Process Parameters on Coating Thickness in a Continuous Galvanizing Line With Computational Fluid Dynamics and Machine Learning Approaches
dc.typeArticle

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