The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms

Nesibe Demiroglu, Idris Ozdemir, Halil Ibrahim Uckol, Suat Ilhan

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Özet

This paper presents an approach to classify the hydrophobicity characteristic of silicone rubber (SiR) samples using deep learning algorithms. By deforming the hydrophobicity property of SiR samples using corona discharges, images of water droplets placed on the sample surface were acquired. From the images, the contact angles of the droplets were determined to find the hydrophobicity classes. The generated water droplet image dataset was trained, validated, and tested utilizing AlexNet, VGGNet, and ResNet. The result shows that the modified AlexNet model with an accuracy of 99.36% is a reliable diagnostic method to identify the hydrophobicity qualification of the SiR samples.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2022 57th International Universities Power Engineering Conference
Ana bilgisayar yayını alt yazısıBig Data and Smart Grids, UPEC 2022 - Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781665455053
DOI'lar
Yayın durumuYayınlandı - 2022
Etkinlik57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Istanbul, Turkey
Süre: 30 Ağu 20222 Eyl 2022

Yayın serisi

Adı2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings

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???event.eventtypes.event.conference???57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot30/08/222/09/22

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Publisher Copyright:
© 2022 IEEE.

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