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Digital Twins, Sensing Technologies and Automation in Industry 4.0

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 54660

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Special Issue Editors

Dr. Samad Sepasgozar

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Guest Editor

School of Built Environment, UNSW Sydney, Sydney, NSW 1466, Australia
Interests: sustainability; energy efficiency; artificial intelligence; smart city; digital twin; applications of the internet of things; advanced GIS; LiDAR; BIM; digital technology in infrastructure; mixed reality applications; information and communication technology; spatial analysis and visualization; authentic education
Special Issues, Collections and Topics in MDPI journals

Dr. Rafiq Ahmad

E-Mail Website
Guest Editor

Laboratory of Intelligent Manufacturing, Design and Automation, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: hybrid manufacturing; 3D printing of multimaterial polymers and alloys; smart manufacturing; systems design and development; Industry 4.0; polymer processing and manufacturing
Special Issues, Collections and Topics in MDPI journals

Dr. Limao Zhang

E-Mail Website
Guest Editor

School of Civil and Environmental Engineering, Nanyang Technological University, N1-01a-29, 50 Nanyang Avenue, Singapore 639798, Singapore
Interests: artificial intelligence; tunnelling excavation; bim data analytics; structural health monitoring; data-driven simulation; uncertainty modelling and risk analysis; decision support systems
Special Issues, Collections and Topics in MDPI journals

Dr. Sara Shirowzhan

E-Mail Website
Guest Editor

City Futures Research Centre, School of Built Environment, University of New South Wales, Sydney, Kensington, NSW 2052, Australia
Interests: sensing technologies; AI; machine learning; advanced GIS; BIM; digital twins; city analytics methods; digital construction; smart cities; smart construction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive overview of the current practices of digital twin, digital transformation, sensing technologies, Internet of Things, engineering, and automation. In addition, the SI will include papers suggesting the future agenda based on an intensive literature review. This SI aims to collect all the practices that may help practitioners to improve their productivity, safety, or quality.

This Special Issue covers a wide range of technologies and methods that can be employed to analyse the data, explore patterns, and predict events, properties, and features of any phenomenon, and visualise the analysis outcome. The context of applications can be a city, urban transportation, construction, or project. This Special Issue welcomes submissions from diverse disciplines, including research projects with different approaches, including quantitative, computational, visual analytics, data mining, analysis of the spatial and morphological structure of cities, urban transportation, and construction systems and activities. We encourage authors to develop or clarify the implications of the following topics and technologies in smart cities, infrastructure, and construction.

Potential topics include but are not limited to the application of digital and sensing technologies, robotics, and automation to address the following objectives:

Automation in construction, mining, manufacturing, and smart cities;
Sensors and Internet of Things;
Improving Smart Cities and intelligence infrastructure;
Facilitating the implementation of Sustainable Development Goals;
Improving quality by detecting damages, cracks, and defects;
Automating the modular and off-site construction;
Implementing Industry 4.0 in different sectors;
Improving the resilience of supply chain management;
Improving safety and manage risks and hazards;
Monitoring disaster management.

You may choose our Joint Special Issue in Automation.

Dr. Samad Sepasgozar
Dr. Rafiq Ahmad
Dr Limao Zhang
Dr. Sara Shirowzhan

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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 transformation, digitization
Hybrid and smart manufacturing
BIM advances and standards
Automation in design and construction operation
Visualization of digital information and services
Interoperability
Robotics and manipulator arms
Additive manufacturing (3D printing)
Automatic sensing
Data acquisition and sensor fusion
Sensor, smart devices, and IoT applications
Data analytics and wearable sensors
Unmanned aerial vehicles/drones
Machine learning
Artificial intelligence
Networking applications
Big data analytics
Mixed reality and immersive technologies
Computer vision
Simulation
Knowledge-based systems (ontologies)
Design for X (automation, additive manufacturing)
Smart manufacturing systems design and engineering

Published Papers (10 papers)

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Research

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18 pages, 5245 KiB

Open AccessArticle

Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems

by Abraham Reyes Yanes, Rabiya Abbasi, Pablo Martinez and Rafiq Ahmad

Sensors 2022, 22(19), 7393; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197393 - 28 Sep 2022

Cited by 7 | Viewed by 2652

Abstract

The use of automation, Internet-of-Things (IoT), and smart technologies is being rapidly introduced into the development of agriculture. Technologies such as sensing, remote monitoring, and predictive tools have been used with the purpose of enhancing agriculture processes, aquaponics among them, and improving the quality of the products. Digital twinning enables the testing and implementing of improvements in the physical component through the implementation of computational tools in a ‘twin’ virtual environment. This paper presents a framework for the development of a digital twin for an aquaponic system. This framework is validated by developing a digital twin for the grow beds of an aquaponics system for real-time monitoring parameters, namely pH, electroconductivity, water temperature, relative humidity, air temperature, and light intensity, and supports the use of artificial intelligent techniques to, for example, predict the growth rate and fresh weight of the growing crops. The digital twin presented is based on IoT technology, databases, a centralized control of the system, and a virtual interface that allows users to have feedback control of the system while visualizing the state of the aquaponic system in real time. Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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20 pages, 7501 KiB

Open AccessArticle

Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control

by Lilan Liu, Zifeng Xu, Chaojia Gao, Tingting Zhang and Zenggui Gao

Sensors 2022, 22(19), 7282; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197282 - 26 Sep 2022

Cited by 6 | Viewed by 1648

Abstract

During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line. Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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13 pages, 3261 KiB

Open AccessArticle

The Development of a Digital Twin Framework for an Industrial Robotic Drilling Process

by Ahmad Farhadi, Stephen K. H. Lee, Eoin P. Hinchy, Noel P. O’Dowd and Conor T. McCarthy

Sensors 2022, 22(19), 7232; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197232 - 23 Sep 2022

Cited by 14 | Viewed by 3265

Abstract

A digital twin is a digital representation of a physical entity that is updated in real-time by transfer of data between physical and digital (virtual) entities. In this manuscript we aim to introduce a digital twin framework for robotic drilling. Initially, a generic reference model is proposed to highlight elements of the digital twin relevant to robotic drilling. Then, a precise reference digital twin architecture model is developed, based on available standards and technologies. Finally, real-time visualisation of drilling process parameters is demonstrated as an initial step towards implementing a digital twin of a robotic drilling process. Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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24 pages, 22783 KiB

Open AccessArticle

Digital Twin-Based Risk Control during Prefabricated Building Hoisting Operations

by Zhansheng Liu, Anxiu Li, Zhe Sun, Guoliang Shi and Xintong Meng

Sensors 2022, 22(7), 2522; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072522 - 25 Mar 2022

Cited by 16 | Viewed by 3048

Abstract

Prefabricated buildings have advantages when it comes to environmental protection. However, the dynamics and complexity of building hoisting operations bring significant safety risks. Existing research on hoisting safety risk lacks a real-time information interaction mechanism and lacks scientific control decision-making tools based on considering the correlation between safety risks. Digital twin (DT) has the advantage of real-time interaction. This paper presents a safety risk control framework for controlling prefabricated building hoisting operations based on DT. In the case of considering the correlation of the safety risk index of hoisting, the safety risk hierarchy model of hoisting is defined in the process of building the DT model. The authors have established a Bayesian network model into the process of the integrated analysis of the digital twin mechanism model and monitoring data to realize the visualization of the decision analysis process of hoisting safety risk control. The key degree of the indirect inducement variable to direct inducement variable was calculated according to probability. The key factor leading to the occurrence of risk was found. The effectiveness of the hoisting safety risk control method is verified by a large, prefabricated building project. This method provides decision tools for hoisting safety risk control, assists in formulating effective control schemes, and improves the efficiency of information integration and sharing. Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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21 pages, 5409 KiB

Open AccessArticle

Digital Twins-Based Impact Response Prediction of Prestressed Steel Structure

by Zhansheng Liu, Chao Yuan, Zhe Sun and Cunfa Cao

Sensors 2022, 22(4), 1647; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041647 - 20 Feb 2022

Cited by 8 | Viewed by 2135

Abstract

Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the development of structural research. This study proposes a method for impact response prediction of prestressed steel structures driven by digital twins (DTs) and machine learning (ML). The high-fidelity DTs of a prestressed steel structure were constructed from the perspective of both a physical entity and virtual entity. A prediction of the impact response of prestressed steel structure’s key parts was established based on ML, and a structure response prediction of the parts driven by data was realized. To validate the effectiveness of the proposed prediction method, the authors carried out a case study in an experiment of a prestressed steel structure. This study provides a reference for fusion applications with DTs and ML in impact response prediction and analysis of prestressed steel structures. Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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17 pages, 1839 KiB

Open AccessArticle

Hybrid Virtual Commissioning of a Robotic Manipulator with Machine Vision Using a Single Controller

by Marek Noga, Martin Juhás and Martin Gulan

Sensors 2022, 22(4), 1621; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041621 - 18 Feb 2022

Cited by 12 | Viewed by 3331

Abstract

Digital twin (DT) is an emerging key technology that enables sophisticated interaction between physical objects and their virtual replicas, with applications in almost all engineering fields. Although it has recently gained significant attraction in both industry and academia, so far it has no unanimously adopted and established definition. One may therefore come across many definitions of what DT is and how to create it. DT can be designed for an existing process and help us to improve it. Another possible approach is to create the DT for a brand new device. In this case, it can reveal how the system would behave in given conditions or when controlled. One of purposes of a DT is to support the commissioning of devices. So far, recognized and used techniques to make the commissioning more effective are virtual commissioning and hybrid commissioning. In this article, we present a concept of hybrid virtual commissioning. This concept aims to point out the possibility to use real devices already at the stage of virtual commissioning. It is introduced in a practical case study of a robotic manipulator with machine vision controlled with a programmable logic controller in a pick-and-place application. This study presents the benefits that stem from the proposed approach and also details when it is convenient to use it. Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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24 pages, 4858 KiB

Open AccessArticle

Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems

by Toyosi Ademujimi and Vittaldas Prabhu

Sensors 2022, 22(4), 1430; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041430 - 13 Feb 2022

Cited by 11 | Viewed by 3959

Abstract

Smart manufacturing systems are being advocated to leverage technological advances that enable them to be more resilient to faults through rapid diagnosis for performance assurance. In this paper, we propose a co-simulation approach for engineering digital twins (DTs) that are used to train Bayesian Networks (BNs) for fault diagnostics at equipment and factory levels. Specifically, the co-simulation model is engineered by using cyber–physical system (CPS) consisting of networked sensors, high-fidelity simulation model of each equipment, and a detailed discrete-event simulation (DES) model of the factory. The proposed DT approach enables injection of faults in the virtual system, thereby alleviating the need for expensive factory-floor experimentation. It should be emphasized that this approach of injecting faults eliminates the need for obtaining balanced data that include faulty and normal factory operations. We propose a Structural Intervention Algorithm (SIA) in this paper to first detect all possible directed edges and then distinguish between a parent and an ancestor node of the BN. We engineered a DT research test-bed in our laboratory consisting of four industrial robots configured into an assembly cell where each robot has an industrial Internet-of-Things sensor that can monitor vibrations in two-axes. A detailed equipment-level simulator of these robots was integrated with a detailed DES model of the robotic assembly cell. The resulting DT was used to carry out interventions to learn a BN model structure for fault diagnostics. Laboratory experiments validated the efficacy of the proposed approach by accurately learning the BN structure, and in the experiments, the accuracy obtained by the proposed approach (measured using Structural Hamming Distance) was found to be significantly better than traditional methods. Furthermore, the BN structure learned was found to be robust to variations in parameters, such as mean time to failure (MTTF). Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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21 pages, 10969 KiB

Open AccessCommunication

Digital Twin-Driven Human Robot Collaboration Using a Digital Human

by Tsubasa Maruyama, Toshio Ueshiba, Mitsunori Tada, Haruki Toda, Yui Endo, Yukiyasu Domae, Yoshihiro Nakabo, Tatsuro Mori and Kazutsugu Suita

Sensors 2021, 21(24), 8266; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248266 - 10 Dec 2021

Cited by 28 | Viewed by 5614

Abstract

Advances are being made in applying digital twin (DT) and human–robot collaboration (HRC) to industrial fields for safe, effective, and flexible manufacturing. Using a DT for human modeling and simulation enables ergonomic assessment during working. In this study, a DT-driven HRC system was developed that measures the motions of a worker and simulates the working progress and physical load based on digital human (DH) technology. The proposed system contains virtual robot, DH, and production management modules that are integrated seamlessly via wireless communication. The virtual robot module contains the robot operating system and enables real-time control of the robot based on simulations in a virtual environment. The DH module measures and simulates the worker’s motion, behavior, and physical load. The production management module performs dynamic scheduling based on the predicted working progress under ergonomic constraints. The proposed system was applied to a parts-picking scenario, and its effectiveness was evaluated in terms of work monitoring, progress prediction, dynamic scheduling, and ergonomic assessment. This study demonstrates a proof-of-concept for introducing DH technology into DT-driven HRC for human-centered production systems. Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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33 pages, 5629 KiB

Open AccessArticle

Digital Twins Supporting Efficient Digital Industrial Transformation

by Dinithi Bamunuarachchi, Dimitrios Georgakopoulos, Abhik Banerjee and Prem Prakash Jayaraman

Sensors 2021, 21(20), 6829; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206829 - 14 Oct 2021

Cited by 20 | Viewed by 3404

Abstract

Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development. Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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Review

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35 pages, 8060 KiB

Open AccessReview

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

by Ziqi Huang, Yang Shen, Jiayi Li, Marcel Fey and Christian Brecher

Sensors 2021, 21(19), 6340; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196340 - 23 Sep 2021

Cited by 98 | Viewed by 21132

Abstract

Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined. Full article

(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)

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