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

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

doi:10.1109/UPEC55022.2022.9917706

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

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

Department of Electrical Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	2022 57th International Universities Power Engineering Conference
Subtitle of host publication	Big Data and Smart Grids, UPEC 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665455053
DOIs	https://doi.org/10.1109/UPEC55022.2022.9917706
Publication status	Published - 2022
Event	57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Istanbul, Turkey Duration: 30 Aug 2022 → 2 Sept 2022

Publication series

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

Conference

Conference	57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022
Country/Territory	Turkey
City	Istanbul
Period	30/08/22 → 2/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

contact angle
deep learning
hydrophobicity characteristic
image classification
silicone-rubber

Access to Document

10.1109/UPEC55022.2022.9917706

Cite this

Demiroglu, N., Ozdemir, I., Uckol, H. I., & Ilhan, S. (2022). The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. In 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings (2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UPEC55022.2022.9917706

Demiroglu, Nesibe ; Ozdemir, Idris ; Uckol, Halil Ibrahim et al. / The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings).

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title = "The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms",

abstract = "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.",

keywords = "contact angle, deep learning, hydrophobicity characteristic, image classification, silicone-rubber",

author = "Nesibe Demiroglu and Idris Ozdemir and Uckol, {Halil Ibrahim} and Suat Ilhan",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 ; Conference date: 30-08-2022 Through 02-09-2022",

year = "2022",

doi = "10.1109/UPEC55022.2022.9917706",

language = "English",

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

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Demiroglu, N, Ozdemir, I , Uckol, HI & Ilhan, S 2022, The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. in 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings. 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022, Istanbul, Turkey, 30/08/22. https://doi.org/10.1109/UPEC55022.2022.9917706

The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. / Demiroglu, Nesibe; Ozdemir, Idris ; Uckol, Halil Ibrahim et al.
2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms

AU - Demiroglu, Nesibe

AU - Ozdemir, Idris

AU - Uckol, Halil Ibrahim

AU - Ilhan, Suat

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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KW - deep learning

KW - hydrophobicity characteristic

KW - image classification

KW - silicone-rubber

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AN - SCOPUS:85141485123

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BT - 2022 57th International Universities Power Engineering Conference

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Demiroglu N, Ozdemir I , Uckol HI , Ilhan S. The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms. In 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings). doi: 10.1109/UPEC55022.2022.9917706

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

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