The use of supervised artificial intelligence methods in quality determination in continuous production lines: a case study of ceramic industry

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dc.authoridŞengül, Ümran / 0000-0001-5867-863X
dc.authoridÖzcan, Serdar / 0000-0002-2136-2049
dc.contributor.authorÖzcan, Serdar
dc.contributor.authorŞengül, Ümran
dc.date.accessioned2026-02-03T12:02:59Z
dc.date.available2026-02-03T12:02:59Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractIn the modern manufacturing environment shaped by the Fourth Industrial Revolution, the critical role of data analytics and machine learning in enhancing quality management is increasingly gaining importance. The ability to predict final product quality at the early stages of the production process can aid in making manufacturing more efficient. Study underscores the significance of accurate quality predictions, which are intricately linked to the depth and scope of training data used in ML models, and presents an approach to overcome some key challenges in the ceramic manufacturing sector. Several traditional machine learning algorithms, including deep neural network algorithms, as well as multivariate analyses, were applied to develop predictive models for determining the final product quality in ceramic industries in the context of a pilot production process. The study encompasses a detailed case study that conducts an extensive comparison among machine learning models developed to predict the quality of tiles post-firing, based on an imbalanced dataset provided by the enterprise, enabling early prediction of tile quality before entering the kiln. Due to the inability to reach the desired minority class F1-Score and Cohen's Kappa values with machine learning classification models created using resampling methods, a deep neural network model capable of operating on an imbalanced dataset was developed for the first time specifically for the industry. The developed deep neural network model successfully predicted the minority class with an F1-Score of 0.75. The Cohen's Kappa value of the model was calculated to be 0.7413.
dc.identifier.doi10.1007/s12008-025-02310-w
dc.identifier.endpage8706
dc.identifier.issn1955-2513
dc.identifier.issn1955-2505
dc.identifier.issue12
dc.identifier.scopus2-s2.0-105006720440
dc.identifier.scopusqualityQ2
dc.identifier.startpage8687
dc.identifier.urihttps://doi.org/10.1007/s12008-025-02310-w
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34911
dc.identifier.volume19
dc.identifier.wosWOS:001497078100001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofInternational Journal of Interactive Design and Manufacturing - Ijidem
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260130
dc.subjectQuality control
dc.subjectPredictive quality
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectManufacturing
dc.subjectImbalanced data
dc.titleThe use of supervised artificial intelligence methods in quality determination in continuous production lines: a case study of ceramic industry
dc.typeArticle

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