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.
| Yazar |
Metin Zontul Esra Kavalci Yilmaz Oguz Kaynar Ramazan Katirci |
| Yayın Türü | Makale |
| Tek Biçim Adres | https://hdl.handle.net/20.500.14081/1325 |
| Konu Başlıkları |
Deep learning
Convolutional neural network Mask RCNN Cr-III electroplating Machine learning Surface detection |
| Koleksiyonlar |
Fakülteler Mühendislik Fakültesi |
| Sayfalar | - |
| Yayın Yılı | 2021 |
| Eser Adı [dc.title] | Automated evaluation of Cr-III coated parts using Mask RCNN and ML methodse |
| Yazar [dc.contributor.author] | Metin Zontul |
| Yazar [dc.contributor.author] | Esra Kavalci Yilmaz |
| Yazar [dc.contributor.author] | Oguz Kaynar |
| Yazar [dc.contributor.author] | Ramazan Katirci |
| Yayın Yılı [dc.date.issued] | 2021 |
| Yayın Türü [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. |
| Yayıncı [dc.publisher] | ELSEVIER SCIENCE SA |
| Dil [dc.language.iso] | English |
| 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 |
| Esere Katkı Sağlayan [dc.contributor.other] | Katirci, R |
| Esere Katkı Sağlayan [dc.contributor.other] | Yilmaz, EK |
| Esere Katkı Sağlayan [dc.contributor.other] | Kaynar, O |
| Esere Katkı Sağlayan [dc.contributor.other] | Zontul, M |
| DOI [dc.identifier.doi] | 10.1016/j.surfcoat.2021.127571 |
| Orcid [dc.identifier.orcid] | 0000-0002-7557-2981 |
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