Transfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0

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dc.contributor.authorTANYILDIZ, Hayriye
dc.contributor.authorŞAHİN, CANAN BATUR
dc.date.accessioned2025-10-24T18:04:05Z
dc.date.available2025-10-24T18:04:05Z
dc.date.issued2023
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractCasting represents a production process where a liquid material is poured into a mold with a hollow cavity, usually of the intended shape, following which its solidification is allowed. Numerous defect types are available, including blow holes, pin holes, burrs, mold material defects, shrinkage defects, metallurgical defects, casting metal defects, etc. All industries have quality control departments to eliminate the occurrence of this defective product. But the main problem is that this inspection process is done manually. This is a very time consuming process and due to human sensitivity this is not 100% accurate. In this study, we will verify whether the \"manual inspection\" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, we will verify whether the \"manual inspection\" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, the casting images were divided into two separate classes, and the classification process was carried out by applying deep learning architectures. The benefits of this proposed approach are discussed and proposed as a more efficient way to control the quality of final products under Industry 4.0.
dc.identifier.doi10.46810/tdfd.1236584
dc.identifier.endpage51
dc.identifier.issn2149-6366
dc.identifier.issue3
dc.identifier.startpage45
dc.identifier.trdizinid1198668
dc.identifier.urihttps://doi.org/10.46810/tdfd.1236584
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1198668
dc.identifier.urihttps://hdl.handle.net/20.500.12899/2617
dc.identifier.volume12
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofTürk Doğa ve Fen Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzTR-Dizin_20251023
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectMühendislik
dc.subjectMakine
dc.subjectNanobilim ve Nanoteknoloji
dc.subjectMalzeme Bilimleri
dc.subjectÖzellik ve Test
dc.subjectBilgisayar Bilimleri
dc.subjectDonanım ve Mimari
dc.subjectBilgisayar Bilimleri
dc.subjectYapay Zeka
dc.subjectPrediction
dc.subjectDeep Learning
dc.subjectTransfer Learning
dc.subjectCasting Defects
dc.subjectIndustrial 4.0
dc.titleTransfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0
dc.typeArticle

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