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Towards Even-Smarter Factories: Advances in Sensor and Digital Technology Integration

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 4047

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, University of Bristol, Bristol BS8 1QU, UK
Interests: smart sensors; IoT; digital twins; smart manufacturing; supply chains; modelling; simulation; systems thinking; sustainability; human-centric
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Ingeniería Electrónica y de Comunicaciones, Universidad de Zaragoza, Zaragoza, Spain
Interests: CMOS integrated circuits; low-power electronics; data acquisition; electric sensing devices; field programmable gate arrays; integrated circuit design; peripheral interfaces; wireless LAN; EPROM; Hall effect transducers; analogue-digital conversion
Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: industrial blockchain; digital twin/cyber-physical system; smart manufacturing; wearable devices; Auto-ID, RFID and industrial sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in sensor and digital technologies like Micro-Electro-Mechanical Systems (MEMS), the Internet of Things (IoT), Cloud Computing and Big Data, among others, are transforming the manufacturing industry by enabling the so-called “smart factories”. Connected technology and data-driven factories and smart factories seek to adapt and respond to production challenges through enhanced and real-time monitoring, control, and the optimization of industrial processes.

The use of advanced miniaturized sensors (AMS) can offer substantial benefits over traditional systems. AMS small size enables easier installation in tight/small spaces or on moving parts, leading to improved or even enabled monitoring and control of previously inaccessible areas and processes. With increased accuracy and precision, AMS can also offer improved data collection capabilities, even in harsh industrial environments. Furthermore, AMS can be incorporated in greater numbers within manufacturing processes due to their lower cost.

By combining the increased diversity, amount, and quality of data provided by AMS with digital technologies, smart factories can be capable of superior decision-making through Big Data analytics, improved collaboration through Cloud Computing, and enhanced optimization through IoT-powered process monitoring and actuation. The result is “even-smarter” factories with the superior levels of efficiency, flexibility, innovativeness and responsiveness required in today’s highly competitive and unpredictable global market.

This Special Issue aims to publish original and visionary research works and review articles on advances in miniaturized sensor development and integration, including theoretical methods and algorithms, conceptual models, technologies, case studies, and industrial applications. Topics to be covered include, but are not limited to, the following:

  • Sensor applications in industrial automation;
  • MEMS for industrial sensors;
  • Smart manufacturing and industry;
  • Industrial IoT sensors (IIoT sensors);
  • Smart sensors/edge sensors;
  • Sensors for machinery;
  • Industry 5.0—sensor interaction/collaboration with humans.

Dr. Maria Valero Bernal
Prof. Dr. Nicolás Medrano
Dr. Ming 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. 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

  • smart sensors
  • sensors and sensing techniques
  • smart manufacturing
  • internet of things (IoT)
  • industrial Internet of Things (IIoT)
  • MEMS
  • Industry 4.0
  • Industry 5.0
  • manufacturing systems

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Published Papers (2 papers)

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Research

14 pages, 4820 KiB  
Article
Digital Twin for a Frequency Mixer Used as a Phase Sensor
by Carlos Pires, Manuel Abreu, Isabel Godinho, Rui Agostinho and João A. Sousa
Sensors 2024, 24(23), 7574; https://doi.org/10.3390/s24237574 - 27 Nov 2024
Viewed by 797
Abstract
The Portuguese Institute for Quality is responsible for the realization and dissemination of the frequency standard in Portugal. There are several techniques for frequency transfer, but we use a frequency mixer to detect phase variations between two light signals with different wavelengths, traveling [...] Read more.
The Portuguese Institute for Quality is responsible for the realization and dissemination of the frequency standard in Portugal. There are several techniques for frequency transfer, but we use a frequency mixer to detect phase variations between two light signals with different wavelengths, traveling along an optical fibre. In this paper, we present the development of a digital twin (DT) that replicates the use of a frequency mixer to improve the frequency transfer problem. A setup was built to train and validate the technique: a frequency mixer was used to determine the phase difference between the two signals, which are caused by temperature gradients in the fibre, together with real-time temperature data from sensors placed along the fibre and on the mixer itself. The DT was trained with two machine learning algorithms, in particular, ARIMA and LSTM networks. To estimate the accuracy of the frequency mixer working as a phasemeter, several sources of uncertainty were considered and included in the DT model, with the goal of obtaining a phase value measurement and its uncertainty in real time. The JCGM 100:2008 and JCGM 101:2008 approaches were used for the estimation of the uncertainty budget. With this work, we merge DT technology with a frequency mixer used for phase detection to provide its value and uncertainty in real time. Full article
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18 pages, 3980 KiB  
Article
From Sensors to Digital Twins toward an Iterative Approach for Existing Manufacturing Systems
by Dimitri Renard, Ramla Saddem, David Annebicque and Bernard Riera
Sensors 2024, 24(5), 1434; https://doi.org/10.3390/s24051434 - 23 Feb 2024
Cited by 2 | Viewed by 2566
Abstract
Digital twin technology is a highly valued asset in the manufacturing sector, with its unique capability to bridge the gap between the physical and virtual parts. The impact of the rapid increase in this technology is based on the collection of real-world data, [...] Read more.
Digital twin technology is a highly valued asset in the manufacturing sector, with its unique capability to bridge the gap between the physical and virtual parts. The impact of the rapid increase in this technology is based on the collection of real-world data, its standardization, and its widespread deployment on an existing manufacturing system. This encompasses sensor values, PLC internal states, and IoT, as well as how the means of linking these data with their digital counterparts. It is challenging to implement digital twins on a large scale due to the heterogeneity of protocols and data structuring of subsystems. To facilitate the integration of the digital twin into existing manufacturing architectures, we propose in this paper a framework that enables the deployment of scalable digital twins from sensors to services of digital twins in an iterative manner. Full article
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