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

Katirci R., Yilmaz E. K., KAYNAR O., Zontul M.

SURFACE & COATINGS TECHNOLOGY, cilt.422, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 422
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.surfcoat.2021.127571
  • Dergi Adı: SURFACE & COATINGS TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Biotechnology Research Abstracts, Chemical Abstracts Core, Communication Abstracts, INSPEC, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: Deep learning, Convolutional neural networks, Mask RCNN, Cr-III electroplating, Machine learning, Surface detection, ORGANIC ADDITIVES, ELECTRODEPOSITION, PERFORMANCE, DEPOSITION, CHROMIUM, ZINC
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

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.