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Future Internet
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Digital Twins in Intelligent Manufacturing
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Digital Twins in Intelligent Manufacturing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (30 August 2024) | Viewed by 8099

Special Issue Editors

Dr. Xin Lu

E-Mail Website
Guest Editor
School of Computer Science, Leeds Trinity University, Leeds LS18 5HD, UK
Interests: deep learning; AI frameworks; digital twins; intelligent technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Wang

E-Mail Website
Guest Editor
School of Engineering Science, University of Skövde, 54128 Skövde, Sweden
Interests: Intelligent manufacturing; process planning; human–robot collaboration; laser welding; circular economy
Special Issues, Collections and Topics in MDPI journals
Dr. Dehao Wu

E-Mail Website
Guest Editor
Faculty of Science and Technology, Bournemouth University, Bournemouth, UK
Interests: 5G verticals; optical wireless communications; high-accuracy positioning; optical sensing and detecting; information-centric networking; information resilience
Dr. Xiaoxia Li

E-Mail Website
Guest Editor
College of Informatics, Huazhong Agricultural University, 1 Shizishan Jie, Hongshan Qu, Wuhan 430070, China
Interests: sustainable manufacturing; intelligent optimization; big data in manufacturing

Special Issue Information

Dear Colleagues,

Nowadays, sustainability and intelligent are becoming the two most critical factors for manufacturing plants within the version of industrial 4.0, where the complex manufacturing problems such as planning, scheduling, and controlling of production and logistic processes need to be accurately predicted, planned and executed by using advanced data analytics, modelling, computational intelligence and AI technologies. With the advancements of IT technologies, the potential of the novel technologies is being leveraged by interaction with smart sensors, Internet of Things (IoT), cloud-edge computing, big data, and Cyber-Physical systems (CPS), which a “digital twin (DT)” of a manufacturing plant is created to promote the real-time interaction and integration of information space and physical space. For an industrial DT system, it covers the whole life cycle and value chain of products that allows the complex manufacturing, automation, and production processes to be simulated, analysed, predicted, and optimised.

DT as a new emerging technology has drawn great attention worldwide. How to apply DT theory and technology to promote the intelligent manufacturing has become a key issue that needs to be urgently addressed. This Special Issue aims to publish state-of-the-art research papers to present innovative systems, methodologies, and trends on industrial DT applications, including but not limited to intelligent perception and information fusion, advanced modelling and analysis, intelligent collaborative optimization control, and precise execution and services for DT in intelligent manufacturing.

The topics of interest include, but are not limited to:

  • AI and Digital twin
  • IoT/Edge/cloud computing and digital twin
  • Digital twin for robot control and multi-robot systems
  • Digital twin for predictive maintenance and performance prediction
  • Digital twin for process control and optimisation
  • Digital twin for advanced modelling
  • Human factors and human digital twins
  • Real-time execution of simulation models for digital twin implementation
  • Digital twin for decision support in supply chain
  • Distributed edge intelligent
  • Digital twin of stochastic production environments
  • 5G digital twin network
  • Digital twin for industrial networks

Dr. Xin Lu
Dr. Wei Wang
Dr. Dehao Wu
Dr. Xiaoxia Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital twins
  • intelligent manufacturing
  • intelligent IoT
  • industrial networks
  • 5G
  • advanced simulation
  • deep learning
  • big data
  • industrial networks
  • edge intelligent

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Published Papers (4 papers)

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Research

35 pages, 7792 KiB  
Article
TWIN-ADAPT: Continuous Learning for Digital Twin-Enabled Online Anomaly Classification in IoT-Driven Smart Labs
by Ragini Gupta, Beitong Tian, Yaohui Wang and Klara Nahrstedt
Future Internet 2024, 16(7), 239; https://doi.org/10.3390/fi16070239 - 4 Jul 2024
Viewed by 1295
Abstract
In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead [...] Read more.
In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead to dynamic and non-stationary data distributions in evolving data streams. This phenomenon, known as concept drift, poses a substantial challenge for traditional data-driven digital twin static machine learning (ML) models for anomaly detection and classification. Subsequently, the drift in normal and anomalous data distributions over time causes the model performance to decay, resulting in high false alarm rates and missed anomalies. To address this issue, we present TWIN-ADAPT, a continuous learning model within a digital twin framework designed to dynamically update and optimize its anomaly classification algorithm in response to changing data conditions. This model is evaluated against state-of-the-art concept drift adaptation models and tested under simulated drift scenarios using diverse noise distributions to mimic real-world distribution shift in anomalies. TWIN-ADAPT is applied to three critical CPS datasets of Smart Manufacturing Labs (also known as “Cleanrooms”): Fumehood, Lithography Unit and Vacuum Pump. The evaluation results demonstrate that TWIN-ADAPT’s continual learning model for optimized and adaptive anomaly classification achieves a high accuracy and F1 score of 96.97% and 0.97, respectively, on the Fumehood CPS dataset, showing an average performance improvement of 0.57% over the offline model. For the Lithography and Vacuum Pump datasets, TWIN-ADAPT achieves an average accuracy of 69.26% and 71.92%, respectively, with performance improvements of 75.60% and 10.42% over the offline model. These significant improvements highlight the efficacy of TWIN-ADAPT’s adaptive capabilities. Additionally, TWIN-ADAPT shows a very competitive performance when compared with other benchmark drift adaptation algorithms. This performance demonstrates TWIN-ADAPT’s robustness across different modalities and datasets, confirming its suitability for any IoT-driven CPS framework managing diverse data distributions in real time streams. Its adaptability and effectiveness make it a versatile tool for dynamic industrial settings. Full article
(This article belongs to the Special Issue Digital Twins in Intelligent Manufacturing)
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19 pages, 3050 KiB  
Article
Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber–Physical Production Systems
by Yuning Jiang, Wei Wang, Jianguo Ding, Xin Lu and Yanguo Jing
Future Internet 2024, 16(4), 134; https://doi.org/10.3390/fi16040134 - 17 Apr 2024
Viewed by 2226
Abstract
The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable [...] Read more.
The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology. Full article
(This article belongs to the Special Issue Digital Twins in Intelligent Manufacturing)
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14 pages, 5946 KiB  
Article
Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization
by Yadi Zhao, Lei Yan, Jian Wu and Ximing Song
Future Internet 2024, 16(1), 7; https://doi.org/10.3390/fi16010007 - 25 Dec 2023
Cited by 1 | Viewed by 1759
Abstract
To address the low level of intelligence and low utilization of logs in current rotary cutting equipment, this paper proposes a digital twin-based system for optimizing the rotary cutting of logs using a five-dimensional model of digital twins. The system features a log [...] Read more.
To address the low level of intelligence and low utilization of logs in current rotary cutting equipment, this paper proposes a digital twin-based system for optimizing the rotary cutting of logs using a five-dimensional model of digital twins. The system features a log perception platform to capture three-dimensional point cloud data, outlining the logs’ contours. Utilizing the Delaunay3D algorithm, this model performs a three-dimensional reconstruction of the log point cloud, constructing a precise digital twin. Feature information is extracted from the point cloud using the least squares method. Processing parameters, determined through the kinematic model, are verified in rotary cutting simulations via Bool operations. The system’s efficacy has been substantiated through experimental validation, demonstrating its capability to output specific processing schemes for irregular logs and to verify these through simulation. This approach notably improves log recovery rates, decreasing volume error from 12.8% to 2.7% and recovery rate error from 23.5% to 5.7% The results validate the efficacy of the proposed digital twin system in optimizing the rotary cutting process, demonstrating its capability not only to enhance the utilization rate of log resources but also to improve the economic efficiency of the factory, thereby facilitating industrial development. Full article
(This article belongs to the Special Issue Digital Twins in Intelligent Manufacturing)
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19 pages, 9235 KiB  
Article
A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan
by Chao-Chung Peng and Yi-Ho Chen
Future Internet 2023, 15(9), 302; https://doi.org/10.3390/fi15090302 - 4 Sep 2023
Cited by 3 | Viewed by 1828
Abstract
Digital twins can reflect the dynamical behavior of the identified system, enabling self-diagnosis and prediction in the digital world to optimize the intelligent manufacturing process. One of the key benefits of digital twins is the ability to provide real-time data analysis during operation, [...] Read more.
Digital twins can reflect the dynamical behavior of the identified system, enabling self-diagnosis and prediction in the digital world to optimize the intelligent manufacturing process. One of the key benefits of digital twins is the ability to provide real-time data analysis during operation, which can monitor the condition of the system and prognose the failure. This allows manufacturers to resolve the problem before it happens. However, most digital twins are constructed using discrete-time models, which are not able to describe the dynamics of the system across different sampling frequencies. In addition, the high computational complexity due to significant memory storage and large model sizes makes digital twins challenging for online diagnosis. To overcome these issues, this paper proposes a novel structure for creating the digital twins of cooling fan systems by combining with neural ordinary differential equations and physical dynamical differential equations. Evaluated using the simulation data, the proposed structure not only shows accurate modeling results compared to other digital twins methods but also requires fewer parameters and smaller model sizes. The proposed approach has also been demonstrated using experimental data and is robust in terms of measurement noise, and it has proven to be an effective solution for online diagnosis in the intelligent manufacturing process. Full article
(This article belongs to the Special Issue Digital Twins in Intelligent Manufacturing)
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