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Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer

The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowi...

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Autores principales: Alatawi, Hibah, Albalawi, Nouf, Shahata, Ghadah, Aljohani, Khulud, Alhakamy, A’aeshah, Tuceryan, Mihran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347177/
https://www.ncbi.nlm.nih.gov/pubmed/37447876
http://dx.doi.org/10.3390/s23136024
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author Alatawi, Hibah
Albalawi, Nouf
Shahata, Ghadah
Aljohani, Khulud
Alhakamy, A’aeshah
Tuceryan, Mihran
author_facet Alatawi, Hibah
Albalawi, Nouf
Shahata, Ghadah
Aljohani, Khulud
Alhakamy, A’aeshah
Tuceryan, Mihran
author_sort Alatawi, Hibah
collection PubMed
description The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of [Formula: see text] and a standard deviation of [Formula: see text]. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is [Formula: see text] shows that there is no variability in the outcomes.
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spelling pubmed-103471772023-07-15 Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer Alatawi, Hibah Albalawi, Nouf Shahata, Ghadah Aljohani, Khulud Alhakamy, A’aeshah Tuceryan, Mihran Sensors (Basel) Article The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of [Formula: see text] and a standard deviation of [Formula: see text]. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is [Formula: see text] shows that there is no variability in the outcomes. MDPI 2023-06-29 /pmc/articles/PMC10347177/ /pubmed/37447876 http://dx.doi.org/10.3390/s23136024 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alatawi, Hibah
Albalawi, Nouf
Shahata, Ghadah
Aljohani, Khulud
Alhakamy, A’aeshah
Tuceryan, Mihran
Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer
title Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer
title_full Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer
title_fullStr Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer
title_full_unstemmed Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer
title_short Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer
title_sort augmented reality-assisted deep reinforcement learning-based model towards industrial training and maintenance for nanodrop spectrophotometer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347177/
https://www.ncbi.nlm.nih.gov/pubmed/37447876
http://dx.doi.org/10.3390/s23136024
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