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Digital twin framework using real-time asset tracking for smart flexible manufacturing system.

Ullah, Asif; Younas, Muhammad; Saharudin, Mohd Shahneel

Authors

Asif Ullah



Abstract

This research article proposes a new method for an enhanced Flexible Manufacturing System (FMS) using a combination of smart methods. These methods use a set of three technologies of Industry 4.0, namely Artificial Intelligence (AI), Digital Twin (DT), and Wi-Fi-based indoor localization. The combination tackles the problem of asset tracking through Wi-Fi localization using machine-learning algorithms. The methodology utilizes the extensive "UJIIndoorLoc" dataset which consists of data from multiple floors and over 520 Wi-Fi access points. To achieve ultimate efficiency, the current study experimented with a range of machine-learning algorithms. The algorithms include Support Vector Machines (SVM), Random Forests (RF), Decision Trees, K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN). To further optimize, we also used three optimizers: ADAM, SDG, and RMSPROP. Among the lot, the KNN model showed superior performance in localization accuracy. It achieved a mean coordinate error (MCE) between 1.2 and 2.8 m and a 100% building rate. Furthermore, the CNN combined with the ADAM optimizer produced the best results, with a mean squared error of 0.83. The framework also utilized a deep reinforcement learning algorithm. This enables an Automated Guided Vehicle (AGV) to successfully navigate and avoid both static and mobile obstacles in a controlled laboratory setting. A cost-efficient, adaptive, and resilient solution for real-time tracking of assets is achieved through the proposed framework. The combination of Wi-Fi fingerprinting, deep learning for localization, and Digital Twin technology allows for remote monitoring, management, and optimization of manufacturing operations.

Citation

ULLAH, A., YOUNAS, M. and SAHARUDIN, M.S. 2025. Digital twin framework using real-time asset tracking for smart flexible manufacturing system. Machines [online], 13(1), article 37. Available from: https://doi.org/10.3390/machines13010037

Journal Article Type Article
Acceptance Date Jan 4, 2025
Online Publication Date Jan 7, 2025
Publication Date Jan 31, 2025
Deposit Date Jan 12, 2025
Publicly Available Date Jan 21, 2025
Journal Machines
Electronic ISSN 2075-1702
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 13
Issue 1
Article Number 37
DOI https://doi.org/10.3390/machines13010037
Keywords Flexible manufacturing system (FMS); Digital twin; Deep learning; Convolutional neural networks; Wi-Fi fingerprinting; Indoor localization; Internet of Things (IoT)
Public URL https://rgu-repository.worktribe.com/output/2656630

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ULLAH 2025 Digital twin framework (VOR) (19.4 Mb)
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2025 by the authors. 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/).




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