Automated evaluation of Cr-III coated parts using Mask RCNN and ML methodse

Basit Öğe Kaydı
dc.contributor.authorKatirci, Ramazan
dc.contributor.authorYilmaz, Esra Kavalci
dc.contributor.authorKaynar, Oguz
dc.contributor.authorZontul, Metin
dc.date.accessioned2024-10-26T18:07:28Z
dc.date.available2024-10-26T18:07:28Z
dc.date.issued2021
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractIn this study, chrome coatings were carried out using a Cr-III electroplating bath. The coated parts were classified depending on their appearance. A new approach was developed to classify the coated parts automatically using artificial intelligence methods. Mask RCNN and machine learning (ML) methods such as Multilayer Perceptron (MLP), Support Vector Classifier (SVC), Gaussian Process (GP), K-nearest Neighbors (KNN), XGBoost, and Random Forest Classifier (RFC) were used together. Mask RCNN was used to clean the coated parts from the redundant data. The extracted data were flattened and converted to the row vectors for use as input in ML methods. ML algorithms were used to classify the coated parts as Pass and Fail. The classification accuracy was checked with the leave one out (loo) cross-validation method. RFC method gave the highest accuracy, 0.83, and F1 score, 0.88. The accuracy of Mask RCNN was checked using a dataset of separated validation images. It was observed that extracting the unnecessary data from the images increased the accuracy exceedingly. Moreover, the method exhibits a high potential to keep the parameters of the electroplating process under control.
dc.identifier.doi10.1016/j.surfcoat.2021.127571
dc.identifier.issn0257-8972
dc.identifier.issn1879-3347
dc.identifier.scopus2-s2.0-85111696105
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.surfcoat.2021.127571
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29523
dc.identifier.volume422
dc.identifier.wosWOS:000685607200072
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Science Sa
dc.relation.ispartofSurface & Coatings Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDeep learning
dc.subjectConvolutional neural networks
dc.subjectMask RCNN
dc.subjectCr-III electroplating
dc.subjectMachine learning
dc.subjectSurface detection
dc.titleAutomated evaluation of Cr-III coated parts using Mask RCNN and ML methodse
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu