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IoT, AI, and Digital Twin for Smart Manufacturing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (25 December 2022) | Viewed by 15637

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Dr. Kim Phuc Tran

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Department of Automation and Industrial Informatics, ENSAIT & GEMTEX, University of Lille, 2 allée Louise et Victor Champier, 59056 Roubaix, France
Interests: Internet of Things; web sites; clothing; computer network security; condition monitoring; data analysis; data description; data mining; industrial control; learning (artificial intelligence); natural language processing; production engineering computing; support vector machines; telecommunication computing; text analysis; wireless sensor networks
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Dear Colleagues,

We are now witnessing the rapid development and powerful application of advanced technologies, leading to the fourth industrial revolution, or Industry 4.0. The wide use of cyber–physical systems, artificial intelligence (AI), and the Internet of things (IoT) lead to the era of big data in industrial manufacturing. These advanced technologies result in the introduction of a new concept in Industry 4.0: smart manufacturing (SM). In particular, digital twins, which play a significant role in SM, are seen as simulations of a physical system. More than a simulation, a digital twin system can behave according to near-real-time data coming from an actual physical counterpart. Currently, the capabilities of digital twins are only promoted to optimize the industrial process when important issues in manufacturing are solved by applying artificial intelligence (AI) and Internet of things (IoT) technology. Moreover, digital twins are based in the cloud, and the massive amounts of data being collected and utilized are drawn from numerous endpoints that lead to new security threats. Although this technology has its own challenges, the benefits are much greater. The technology of digital twins is still far from reaching its full potential; therefore, obtaining an entire view of digital twins, including their characteristics, benefits, implementation, and challenges, is essential in order to unlock the true power of this technology. In the context of SM, digital twins can be applied in diagnostics and the monitoring of the system/production line. Due to the rapid communication between devices, real-time system maintenance with high efficiency can be developed.

The aim of this Special Issue is to highlight innovative developments with respect to the current challenges and opportunities for the applications of an “IoT, AI-Based Digital Twin for Smart Manufacturing”. Topics include, but are not limited to, the following:

Real-time monitoring with machine learning and deep learning;
Artificial intelligence for predictive maintenance;
Artificial intelligence for smarter cybersecurity;
Production scheduling with reinforcement learning;
Artificial intelligence and robotics in smart manufacturing;
IoT-enabled smart manufacturing;
Digital twins integrated with the IoT and AI;
Smart applications of an IoT, AI-based digital twin;
Data-driven scenarios based on digital twins;
Blockchain and security for digital twins with AI;
Cognitive digital twins;
Digital twins’ role in the digital transformation process.

Dr. Kim Phuc Tran
Guest Editor

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Keywords

Internet of Things
digital twin
smart manufacturing
artificial intelligence
big data
virtual reality
augmented reality
mixed reality
cloud computing

Published Papers (4 papers)

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Research

19 pages, 11988 KiB

Open AccessArticle

Interaction with Industrial Digital Twin Using Neuro-Symbolic Reasoning

by Aziz Siyaev, Dilmurod Valiev and Geun-Sik Jo

Sensors 2023, 23(3), 1729; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031729 - 03 Feb 2023

Cited by 4 | Viewed by 3263

Abstract

Digital twins have revolutionized manufacturing and maintenance, allowing us to interact with virtual yet realistic representations of the physical world in simulations to identify potential problems or opportunities for improvement. However, traditional digital twins do not have the ability to communicate with humans using natural language, which limits their potential usefulness. Although conventional natural language processing methods have proven to be effective in solving certain tasks, neuro-symbolic AI offers a new approach that leads to more robust and versatile solutions. In this paper, we propose neuro-symbolic reasoning (NSR)—a fundamental method for interacting with 3D digital twins using natural language. The method understands user requests and contexts to manipulate 3D components of digital twins and is able to read maintenance manuals and implement installations and removal procedures autonomously. A practical neuro-symbolic dataset of machine-understandable manuals, 3D models, and user queries is collected to train the neuro-symbolic reasoning interaction mechanism. The evaluation demonstrates that NSR can execute user commands accurately, achieving 96.2% accuracy on test data. The proposed method has industrial importance since it provides the technology to perform maintenance procedures, request information from manuals, and serve as a tool to interact with complex virtual machinery using natural language. Full article

(This article belongs to the Special Issue IoT, AI, and Digital Twin for Smart Manufacturing)

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16 pages, 4682 KiB

Open AccessArticle

Digital Twin for a Collaborative Painting Robot

by Ratchatin Chancharoen, Kantawatchr Chaiprabha, Lunchakorn Wuttisittikulkij, Widhyakorn Asdornwised, Muhammad Saadi and Gridsada Phanomchoeng

Sensors 2023, 23(1), 17; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010017 - 20 Dec 2022

Cited by 6 | Viewed by 2761

Abstract

A collaborative painting robot that can be used as an alternative to workers has been developed using a digital twin framework and its performance was demonstrated experimentally. The digital twin of the automatic painting robot simulates the entire process and estimates the paint result before the real execution. An operator can view the simulated process and result with an option to either confirm or cancel the task. If the task is accepted, the digital twin generates all the parameters, including the end effector trajectory of the robot, the material flow to the collaborative robot, and a spray mechanism. This ability means that the painting process can be practiced in a virtual environment to decrease set costs, waste, and time, all of which are highly demanded in single-item production. In this study, the screen was fixtureless and, thus, a camera was used to capture it in a physical environment, which was further analyzed to determine its pose. The digital twin then builds the screen in real-time in a virtual environment. The communication between the physical and digital twins is bidirectional in this scenario. An operator can design a painting pattern, such as a basic shape and/or letter, along with its size and paint location, in the resulting procedure. The digital twin then generates the simulation and expected painting result using the physical twin’s screen pose. The painting results show that the root mean square error (RMSE) of the painting is less than 1.5 mm and the standard deviation of RMSE is less than 0.85 mm. Additionally, the initial benefits of the technique include lower setup costs, waste, and time, as well as an easy-to-use operating procedure. More benefits are expected from the digital twin framework, such as the ability of the digital twin to (1) find a solution when a fault arises, (2) refine the control or optimize the operation, and (3) plan using historic data. Full article

(This article belongs to the Special Issue IoT, AI, and Digital Twin for Smart Manufacturing)

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18 pages, 4877 KiB

Open AccessArticle

A Digital Twin Case Study on Automotive Production Line

by Arif Furkan Mendi

Sensors 2022, 22(18), 6963; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186963 - 14 Sep 2022

Cited by 14 | Viewed by 3902

Abstract

The manufacturing sector is one of the areas where the advantages of digital twin technology can benefit mostly. The product development, including its software, electronics, mechanics, and physical behavior, is included in the digital twin of the product. Furthermore, simultaneous data capturing from the sensors and data processing are also available in the digital twin. This enables each phase of the development cycle to be simulated, processed, and validated to discover the potential problems before the production of real components. In this study, the use of digital twin technology in the commercial production phase of the automotive production line with a case study is introduced. This study is one of the most comprehensive studies in the literature related to automotive production; therefore, it puts forth the power of using digital twin technology in that area. As the result of this case study, a 6.01% increase in the commercial production line efficiency and an 87.56% gain for downtime are achieved. Full article

(This article belongs to the Special Issue IoT, AI, and Digital Twin for Smart Manufacturing)

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22 pages, 3282 KiB

Open AccessArticle

Digital Twin and Smart Manufacturing in Industries: A Bibliometric Analysis with a Focus on Industry 4.0

by Georgiana Moiceanu and Gigel Paraschiv

Sensors 2022, 22(4), 1388; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041388 - 11 Feb 2022

Cited by 25 | Viewed by 4476

Abstract

Technology is being used in our society in all areas, mostly in industry, and generates the most interest in current research since it is a part of day-to-day activities. The main objective of this research was to use bibliometric analysis to analyze the production of scientific literature on digital twin and smart manufacturing with a focus on Industry 4.0, using information from the Web of Science database. To conduct the study, the keywords necessary for data selection were chosen, and then analyzed based on different variables such as author productivity, citations, most productive institutions, publishers with the highest number of publications, scientific document classification, countries with the highest number of publications, and a network analysis using VOSviewer. The results showed Tao F. and Soderberg R. were the main authors, that China was the country with the highest knowledge, and Elsevier was the main publisher. Although the subject has only been in publication for five years, digital twin will constitute an important part of future technologies due to its rapid ascension, proof of this being its yearly productivity (2020 producing the highest number of materials). Papers published in 2021 were excluded, but the difference between the numbers of materials found and those analyzed shows that 2021 will be even more productive than 2020. Full article

(This article belongs to the Special Issue IoT, AI, and Digital Twin for Smart Manufacturing)

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