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Automation
Special Issues
Digital Twins, Sensing Technologies and Automation in Industry 4.0
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Digital Twins, Sensing Technologies and Automation in Industry 4.0

A special issue of Automation (ISSN 2673-4052).

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 29639

Special Issue Editors

Dr. Sara Shirowzhan

E-Mail Website
Guest Editor
City Futures Research Centre, School of Built Environment, University of New South Wales, Kensington, Sydney, 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
Dr. Samad Sepasgozar

E-Mail Website
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: design and manufacturing; additive manufacturing; recycling-repair-&-remanufacturing; automation and robotics; system design and engineering; hybrid and smart manufacturing; industry 4.0; lean 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

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.

This special issue is focused more on automation. Papers focus on sensors may choose our Joint Special Issue in Sensors (ISSN 1424-8220, IF 3.275).

Dr. Sara Shirowzhan
Dr. Samad Sepasgozar
Dr. Rafiq Ahmad
Dr. Limao Zhang
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. Automation is an international peer-reviewed open access quarterly 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 1000 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

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26 pages, 10259 KiB  
Article
A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing
by M. Azizur Rahman, Md Shihab Shakur, Md. Sharjil Ahamed, Shazid Hasan, Asif Adnan Rashid, Md Ariful Islam, Md. Sabit Shahriar Haque and Afzaal Ahmed
Automation 2022, 3(3), 400-425; https://doi.org/10.3390/automation3030021 - 1 Aug 2022
Cited by 11 | Viewed by 5096
Abstract
With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration [...] Read more.
With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes. Full article
(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)
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23 pages, 7045 KiB  
Article
Digital Twin of a Flexible Manufacturing System for Solutions Preparation
by Tiago Coito, Paulo Faria, Miguel S. E. Martins, Bernardo Firme, Susana M. Vieira, João Figueiredo and João M. C. Sousa
Automation 2022, 3(1), 153-175; https://doi.org/10.3390/automation3010008 - 8 Mar 2022
Cited by 12 | Viewed by 4787
Abstract
In the last few decades, there has been a growing necessity for systems that handle market changes and personalized customer needs with near mass production efficiency, defined as the new mass customization paradigm. The Industry 5.0 vision further enhances the human-centricity aspect, in [...] Read more.
In the last few decades, there has been a growing necessity for systems that handle market changes and personalized customer needs with near mass production efficiency, defined as the new mass customization paradigm. The Industry 5.0 vision further enhances the human-centricity aspect, in the necessity for manufacturing systems to cooperate with workers, taking advantage of their problem-solving capabilities, creativity, and expertise of the manufacturing process. A solution is to develop a flexible manufacturing system capable of handling different customer requests and real-time decisions from operators. This paper tackles these aspects by proposing a digital twin of a robotic system for solution preparation capable of making real-time scheduling decisions and forecasts using a simulation model while allowing human interventions. A discrete event simulation model was used to forecast possible system improvements. The simulation handles real-time scheduling considering the possibility of adding identical parallel machines. Results show that processing multiple jobs simultaneously with more than one machine on critical processes, increasing the robot speed, and using heuristics that emphasize the shortest transportation time can reduce the overall completion time by 82%. The simulation model has an animated visualization window for a deeper understanding of the system. Full article
(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)
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19 pages, 713 KiB  
Article
The Road to Accountable and Dependable Manufacturing
by Jan Pennekamp, Roman Matzutt, Salil S. Kanhere, Jens Hiller and Klaus Wehrle
Automation 2021, 2(3), 202-219; https://doi.org/10.3390/automation2030013 - 13 Sep 2021
Cited by 6 | Viewed by 4303
Abstract
The Internet of Things provides manufacturing with rich data for increased automation. Beyond company-internal data exploitation, the sharing of product and manufacturing process data along and across supply chains enables more efficient production flows and product lifecycle management. Even more, data-based automation facilitates [...] Read more.
The Internet of Things provides manufacturing with rich data for increased automation. Beyond company-internal data exploitation, the sharing of product and manufacturing process data along and across supply chains enables more efficient production flows and product lifecycle management. Even more, data-based automation facilitates short-lived ad hoc collaborations, realizing highly dynamic business relationships for sustainable exploitation of production resources and capacities. However, the sharing and use of business data across manufacturers and with end customers add requirements on data accountability, verifiability, and reliability and needs to consider security and privacy demands. While research has already identified blockchain technology as a key technology to address these challenges, current solutions mainly evolve around logistics or focus on established business relationships instead of automated but highly dynamic collaborations that cannot draw upon long-term trust relationships. We identify three open research areas on the road to such a truly accountable and dependable manufacturing enabled by blockchain technology: blockchain-inherent challenges, scenario-driven challenges, and socio-economic challenges. Especially tackling the scenario-driven challenges, we discuss requirements and options for realizing a blockchain-based trustworthy information store and outline its use for automation to achieve a reliable sharing of product information, efficient and dependable collaboration, and dynamic distributed markets without requiring established long-term trust. 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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26 pages, 2858 KiB  
Review
Towards the Development of a Digital Twin for a Sustainable Mass Customization 4.0 Environment: A Literature Review of Relevant Concepts
by César Martínez-Olvera
Automation 2022, 3(1), 197-222; https://doi.org/10.3390/automation3010010 - 16 Mar 2022
Cited by 18 | Viewed by 5618
Abstract
Digital Twins (DTs) are one of the disruptive technologies associated with the Industry 4.0 concept. A DT connects the physical manufacturing system with the digital cyberspace, via the synchronization of the simulation (i.e., physical configurations) and data models (i.e., product, process, and resource [...] Read more.
Digital Twins (DTs) are one of the disruptive technologies associated with the Industry 4.0 concept. A DT connects the physical manufacturing system with the digital cyberspace, via the synchronization of the simulation (i.e., physical configurations) and data models (i.e., product, process, and resource models) of the manufacturing system. This synchronization of both worlds—the physical and digital—allows one to address the issue of manufacturing customized products. This challenge of mass customization (1) puts forward the goal of achieving the highest level of customer satisfaction, and (2) creates the need for the optimization of the complete value creation process. Within an Industry 4.0 context, the latter is translated as the interlinking of production resources and systems, via a DT, as it is in the physical world where the actual value-creation process takes place. The success of an Industry 4.0 mass customization environment (or mass customization 4.0), depends on its degree/level of sustainability. For these reasons, the present paper presents a review of relevant concepts related to the role of DTs in the achievement of a mass customization 4.0 environment, plus some proposals of how to address the identified research challenges. A future research agenda is proposed at the end of the paper. Full article
(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)
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Other

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32 pages, 7547 KiB  
Systematic Review
Scientometric Analysis for Cross-Laminated Timber in the Context of Construction 4.0
by Emanuel Martinez Villanueva, Jennifer Alejandra Cardenas Castañeda and Rafiq Ahmad
Automation 2022, 3(3), 439-470; https://doi.org/10.3390/automation3030023 - 10 Aug 2022
Cited by 7 | Viewed by 3906
Abstract
Cross-laminated timber (CLT) has been one of the principal materials in mass timber construction, and now it is possible to find mid-rise and high-rise projects around the globe. This study makes a scientometric review comparison between CLT and the impact of the fourth [...] Read more.
Cross-laminated timber (CLT) has been one of the principal materials in mass timber construction, and now it is possible to find mid-rise and high-rise projects around the globe. This study makes a scientometric review comparison between CLT and the impact of the fourth industrial revolution (formally known as Industry 4.0) in the construction industry, focusing on worldwide academic publications between 2006 and 2022. The analysis considers keywords, co-author, co-citation, and clustering analysis. This study used 1320 documents, including journals and conference proceedings from the Scopus database, where 753 were for cross-laminated timber and 567 for Industry 4.0. Key researchers, research institutions, journals, publications, citation patterns, and trends are some of the results obtained from the scientometric analysis. Once the knowledge mapping was conducted for both fields, scrutiny of the interconnection of both areas was performed to find possible research gaps from a manufacturing perspective. Among the conclusions, it is logical to say that Industry 4.0 implementation in cross-laminated timber is still in its infancy. One of the most popular technologies impacting construction is the digital twin concept; however, no work is reported for CLT on this topic. Additionally, digital automation is a necessity in any research practice, and the use of industrial robots is shown to be an essential asset for CLT as these robots can handle complex shapes. Full article
(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)
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Graphical abstract

22 pages, 1131 KiB  
Perspective
Symbiotic Evolution of Digital Twin Systems and Dataspaces
by Thomas Usländer, Michael Baumann, Stefan Boschert, Roland Rosen, Olaf Sauer, Ljiljana Stojanovic and Jan Christoph Wehrstedt
Automation 2022, 3(3), 378-399; https://doi.org/10.3390/automation3030020 - 1 Aug 2022
Cited by 15 | Viewed by 3996
Abstract
This paper proposes to combine the concept of digital twins with the concept of dataspaces to fulfill the original expectation that a digital twin is a comprehensive virtual representation of physical assets. Based upon a terminological and conceptual discussion of digital twins and [...] Read more.
This paper proposes to combine the concept of digital twins with the concept of dataspaces to fulfill the original expectation that a digital twin is a comprehensive virtual representation of physical assets. Based upon a terminological and conceptual discussion of digital twins and dataspaces, this paper claims that a systemic approach towards digital twin Systems is required. The key conceptual approach consists of a Reference Model for Digital Twin Systems (DTS-RM) and a hypothesis regarding a symbiotic evolution. The DTS-RM distinguishes between a digital twin back-end platform comprising the access and management of comprehensive digital twin instances and digital twin-related services, and digital twin front-end services that are tailored to the demands of applications and users. The main purpose of the back-end platform is to decouple the digital twin’s generation and management from the usage of the digital twin for applications. Full article
(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)
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