Visual Analysis of Deep Learning Methods for Industrial Vacuum Metalized Film Product

Thiago Bastos, Luiz Stragevitch, Cleber Zanchettin

2022

Abstract

Extract information to support decisions in a complex environment as the industrial is not an easy task. Information technologies and cyber-physical systems have provided technical possibilities to extract, store, and process many data. In parallel, the recent advances in artificial intelligence permit the prediction and evaluation of features and information. Industry 4.0 can benefit from these approaches, allowing the visualization of process, feature prediction, and model interpretation. We evaluate the use of Machine Learning (ML) to support monitoring and quality prediction of an industrial vacuum metalization process. Therefore, we proposed a semantic segmentation approach to fault identification using images composed of optical density (OD) values from the vacuum metalized film process. Besides that, a deep neural network model is applied to product classification using the segmented OD profile. The semantic segmentation allowed film regions analysis and coating quality associations through their class and format. The proposed classifier presented 86.67% of accuracy. The use of visualization and ML approaches permits systematical real-time process monitoring that reduces time and material waste. Consequently, it is a promising approach for Industry 4.0 on monitoring and maintenance support.

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Paper Citation


in Harvard Style

Bastos T., Stragevitch L. and Zanchettin C. (2022). Visual Analysis of Deep Learning Methods for Industrial Vacuum Metalized Film Product. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5, pages 380-386. DOI: 10.5220/0010815400003124


in Bibtex Style

@conference{visapp22,
author={Thiago Bastos and Luiz Stragevitch and Cleber Zanchettin},
title={Visual Analysis of Deep Learning Methods for Industrial Vacuum Metalized Film Product},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={380-386},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010815400003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Visual Analysis of Deep Learning Methods for Industrial Vacuum Metalized Film Product
SN - 978-989-758-555-5
AU - Bastos T.
AU - Stragevitch L.
AU - Zanchettin C.
PY - 2022
SP - 380
EP - 386
DO - 10.5220/0010815400003124