A comparative assessment of artificial neural network and regression models to predict mechanical properties of continuously cooled low carbon steels: an external data analysis approach

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dc.contributor.authorAlan, Emre
dc.contributor.authorAyhan, Ismail Irfan
dc.contributor.authorOgel, Bilgehan
dc.contributor.authorUzunsoy, Deniz
dc.date.accessioned2026-02-08T15:08:34Z
dc.date.available2026-02-08T15:08:34Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, mechanical properties of continuously cooled low carbon steels were predicted via Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models. Unlike the previous studies, laboratory scaled self-generated data that consists of chemical compositions and cooling rates were used as input while yield strength (YS), ultimate tensile strength (UTS) and total elongation (TE) were served as target data. The prediction performances of the models were compared by applying new data set extracted from external sources like previously studied research papers, thesis or dissertations. A better agreement between predicted and actual data was achieved with ANN model. Additionally, the response of ANN model to new external data resulted in lower prediction errors even the data has one or more input value that is not included in the range of training data set. Unlike ANN model, MLR model shows a significant decrease in prediction accuracy when input data has non-uniform distribution or target data takes place in relatively narrow range. In general, it was shown that ANN model trained with self-generated data can be used as an efficient tool to estimate mechanical properties of continuously cooled low carbon steels that are produced with various conditions, even for the phenomena between input and output is complex and data distribution is non-uniform.
dc.identifier.doi10.61112/jiens.1445518
dc.identifier.endpage513
dc.identifier.issn2791-7630
dc.identifier.issue2
dc.identifier.startpage495
dc.identifier.trdizinid1253301
dc.identifier.urihttps://doi.org/10.61112/jiens.1445518
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5126
dc.identifier.volume4
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofJournal of innovative engineering and natural science (Online)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR-Dizin_20260207
dc.subjectLow carbon steel
dc.subjectArtificial neural network
dc.subjectMechanical properties.
dc.subjectMultiple linear regression
dc.subjectContinuous cooling
dc.titleA comparative assessment of artificial neural network and regression models to predict mechanical properties of continuously cooled low carbon steels: an external data analysis approach
dc.typeArticle

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