Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System
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 combin...
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MDPI AG
2025-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/37 |
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| author | Asif Ullah Muhammad Younas Mohd Shahneel Saharudin |
| author_facet | Asif Ullah Muhammad Younas Mohd Shahneel Saharudin |
| author_sort | Asif Ullah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3bfc9d3a26ef42a1b6fe0e3355dbd569 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-3bfc9d3a26ef42a1b6fe0e3355dbd5692025-01-24T13:39:13ZengMDPI AGMachines2075-17022025-01-011313710.3390/machines13010037Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing SystemAsif Ullah0Muhammad Younas1Mohd Shahneel Saharudin2Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi 23460, PakistanSchool of Engineering, Robert Gordon University, Aberdeen AB10 7QB, UKSchool of Engineering, Robert Gordon University, Aberdeen AB10 7QB, UKThis 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.https://www.mdpi.com/2075-1702/13/1/37flexible manufacturing system (FMS)digital twindeep learningconvolutional neural networksWi-Fi fingerprintingindoor localization |
| spellingShingle | Asif Ullah Muhammad Younas Mohd Shahneel Saharudin Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System Machines flexible manufacturing system (FMS) digital twin deep learning convolutional neural networks Wi-Fi fingerprinting indoor localization |
| title | Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System |
| title_full | Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System |
| title_fullStr | Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System |
| title_full_unstemmed | Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System |
| title_short | Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System |
| title_sort | digital twin framework using real time asset tracking for smart flexible manufacturing system |
| topic | flexible manufacturing system (FMS) digital twin deep learning convolutional neural networks Wi-Fi fingerprinting indoor localization |
| url | https://www.mdpi.com/2075-1702/13/1/37 |
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