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

In 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.

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Eser Adı
(dc.title)
Automated evaluation of Cr-III coated parts using Mask RCNN and ML methodse
Yazar
(dc.contributor.author)
Metin Zontul
Yayın Yılı
(dc.date.issued)
2021
Tür
(dc.type)
Makale
Özet
(dc.description.abstract)
In 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.
Açık Erişim Tarihi
(dc.date.available)
2021-03-14
Yayıncı
(dc.publisher)
Elsevier Science Sa
Dil
(dc.language.iso)
En
Konu Başlıkları
(dc.subject)
Deep learning
Konu Başlıkları
(dc.subject)
Convolutional neural network
Konu Başlıkları
(dc.subject)
Mask RCNN
Konu Başlıkları
(dc.subject)
Cr-III electroplating
Konu Başlıkları
(dc.subject)
Machine learning
Konu Başlıkları
(dc.subject)
Surface detection
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.14081/1325
ISSN
(dc.identifier.issn)
0257-8972
Dergi
(dc.relation.journal)
Surface & Coatings Technology
Esere Katkı Sağlayan
(dc.contributor.other)
Zontul, Metin
Esere Katkı Sağlayan
(dc.contributor.other)
Katirci, Ramazan
Esere Katkı Sağlayan
(dc.contributor.other)
Yilmaz, Esra Kavalci
Esere Katkı Sağlayan
(dc.contributor.other)
Kaynar, Oguz
DOI
(dc.identifier.doi)
10.1016/j.surfcoat.2021.127571
Orcid
(dc.identifier.orcid)
0000-0002-7557-2981
Dergi Cilt
(dc.identifier.volume)
422
wosquality
(dc.identifier.wosquality)
Q1
wosauthorid
(dc.contributor.wosauthorid)
EIV-4571-2022
Department
(dc.contributor.department)
Bilgisayar Mühendisliği
Wos No
(dc.identifier.wos)
WOS:000685607200072
Veritabanları
(dc.source.platform)
Wos
Veritabanları
(dc.source.platform)
Scopus
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