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High Performance Sensors and Actuators in the Context of Industry 4.0 and Society Wellbeing: Theory, Developments and Applications

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 13353

Special Issue Editors

Prof. Dr. Jose Machado

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Guest Editor
Mechanical Engineering Department, MEtRICs Research Center, University of Minho, 4800-058 Guimarães, Portugal
Interests: cyber-physical systems; dependable controllers for dependable mechatronic systems; mechatronic systems design for medical/biomedical applications, wellbeing and/or rehabilitation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sahin Yildirim

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Guest Editor
Department of Mechatronics Engineering, Erciyes Üniversitesi, Erciyes, Turkey
Interests: design; network analysis; mechatronics; PLC; control systems; aerospace; control theory; mechanical design; PLC programming; computer design
Prof. Dr. Katarzyna Antosz

E-Mail Website
Guest Editor
Department of Manufacturing Processes and Production Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland
Interests: industry 4.0; maintenance; production engineering; systems reliability; lean maintenance; decision support systems; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vítor Carvalho

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Guest Editor
1. The Applied Artificial Intelligence Laboratory (2Ai) of the School of Technology (EST), Polytechnic Institute of Cávado and Ave (IPCA), 4750-810 Barcelos, Portugal
2. Algoritmi R&D Centre, Minho University, 4710-057 Braga, Portugal
Interests: sensors; data acquisition; serious games; education; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vijaya Kumar Manupati

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Guest Editor
National Institute of Technology, Warangal, India
Interests: operations research; multi-objective; supply chain; inventory management; supply and management; industrial engineering; production planning; manufacturing; production systems; manufacturing systems
Special Issues, Collections and Topics in MDPI journals
Dr. Géza Husi

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Guest Editor
Faculty of Engineering, University of Debrecen, H-4032 Debrecen, Egyetem tér 1, Hungary
Interests: mechatronics; rehabilitation devices; electrical machines
Special Issues, Collections and Topics in MDPI journals
Dr. Camelia Claudia Avram

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Guest Editor
Department of Automation, Universitatea Tehnica Cluj-Napoca, Cluj-Napoca, Romania
Interests: data transmission; discrete event systems; modeling; simulation, formal methods; distributed systems; traffic control
Special Issues, Collections and Topics in MDPI journals
Dr. Justyna Trojanowska

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Guest Editor
Faculty of Mechanical Engineering, Poznan University of Technology, Poznan, Poland
Interests: mechanical engineering; production management; production scheduling; production process improvement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent scientific and technological achievements are leading companies and society to an advanced level of skills, competences, and efficiency as well as to a better level of quality of life and the improved wellbeing of civil society. This Special Issue is focused on all creations of added value and the respective analysis the entire process related to those advanced technological products and solutions. This Special Issue considers all of the steps in the developmental process, from theoretical study and the development of innovative solutions to the implementation of a product in a more sustainable and better world that is concerned with the efficiency of companies and the wellbeing of people.

The scope of this Special Issue is closely associated with that of the ICIE’2022 conference. This conference and Special Issue are to present the current innovations and engineering achievements of scientists and industrial practitioners in the thematic areas described above.

Topics of interest include but are not limited to the following:

  • Aerospace technology and astronautics;
  • Automotive engineering;
  • Biotechnological and environmental systems;
  • Biotechnology;
  • Cyber–physical systems;
  • Control theory and architecture;
  • Control technology;
  • Distributed and networked control;
  • Engineering design;
  • Fault-tolerant control;
  • Hardware for control systems;
  • Image processing and computer vision;
  • Industrial automation;
  • Industrial networking;
  • Instrumentation, sensors, and actuators;
  • Manufacturing engineering;
  • Mechanical systems design;
  • Mechatronics design;
  • Mechatronics modelling, simulation, and identification;
  • Medical devices;
  • MEMS;
  • Optics and optometry;
  • Process control;
  • Real time systems architecture;
  • Rehabilitation devices;
  • Reliable systems;
  • Robust control;
  • Robotics;
  • Wellbeing;
  • Wireless applications and systems

Dr. Jose Machado
Prof. Dr. Sahin Yildirim
Dr. Katarzyna Antosz
Prof. Dr. Vítor Carvalho
Prof. Dr. Vijaya Kumar Manupati
Dr. Géza Husi
Dr. Camelia Claudia Avram
Dr. Justyna Trojanowska
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 250 words) can be sent to the Editorial Office for assessment.

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

  • Advanced Sensors
  • Advanced actuators
  • Industry 4.0
  • Added Value Systems
  • Wellbeing
  • Quality of Life

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

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Research

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24 pages, 4026 KB  
Article
Three-Dimensionally Printed Sensors with Piezo-Actuators and Deep Learning for Biofuel Density and Viscosity Estimation
by Víctor Corsino, Víctor Ruiz-Díez, Andrei Braic and José Luis Sánchez-Rojas
Sensors 2026, 26(2), 526; https://doi.org/10.3390/s26020526 - 13 Jan 2026
Viewed by 429
Abstract
Biofuels have emerged as a promising alternative to conventional fuels, offering improved environmental sustainability. Nevertheless, inadequate control of their physicochemical properties can lead to increased emissions and potential engine damage. Existing methods for regulating these properties depend on costly and sophisticated laboratory equipment, [...] Read more.
Biofuels have emerged as a promising alternative to conventional fuels, offering improved environmental sustainability. Nevertheless, inadequate control of their physicochemical properties can lead to increased emissions and potential engine damage. Existing methods for regulating these properties depend on costly and sophisticated laboratory equipment, which poses significant challenges for integration into industrial production processes. Three-dimensional printing technology provides a cost-effective alternative to traditional fabrication methods, offering particular benefits for the development of low-cost designs for detecting liquid properties. In this work, we present a sensor system for assessing biofuel solutions. The presented device employs piezoelectric sensors integrated with 3D-printed, liquid-filled cells whose structural design is refined through experimental validation and novel optimization strategies that account for sensitivity, recovery and resolution. This system incorporates discrete electronic circuits and a microcontroller, within which artificial intelligence algorithms are implemented to correlate sensor responses with fluid viscosity and density. The proposed approach achieves calibration and resolution errors as low as 0.99% and 1.48×10−2 mPa·s for viscosity, and 0.0485% and 1.9×10−4 g/mL for density, enabling detection of small compositional variations in biofuels. Additionally, algorithmic methodologies for dimensionality reduction and data treatment are introduced to address temporal drift, enhance sensor lifespan and accelerate data acquisition. The resulting system is compact, precise and applicable to diverse industrial liquids. Full article
(This article belongs to the Special Issue High Performance Sensors and Actuators in the Context of Industry 4.0 and Society Wellbeing: Theory, Developments and Applications)
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19 pages, 3576 KB  
Article
A Novel Experimental Apparatus for Characterizing Flow Regime in Mechanically Stirred Tanks through Force Sensors
by Miguel Magos-Rivera, Carlos Avilés-Cruz and Jorge Ramírez-Muñoz
Sensors 2024, 24(7), 2319; https://doi.org/10.3390/s24072319 - 5 Apr 2024
Cited by 4 | Viewed by 2095
Abstract
Pressure fluctuations in a mixing tank can provide valuable information about the existing flow regime within the tank, which in turn influences the degree of mixing that can be achieved. In the present work, we propose a prototype for identifying the flow regime [...] Read more.
Pressure fluctuations in a mixing tank can provide valuable information about the existing flow regime within the tank, which in turn influences the degree of mixing that can be achieved. In the present work, we propose a prototype for identifying the flow regime in mechanically stirred tanks equipped with four vertical baffles through the characterization of pressure fluctuations. Our innovative proposal is based on force sensors strategically placed in the baffles of the mixing tank. The signals coming from the sensors are transmitted to an electronic module based on an Arduino UNO development board. In the electronic module, the pressure signals are conditioned, amplified and sent via Bluetooth to a computer. In the computer, the signals can be plotted or stored in an Excel file. In addition, the proposed system includes a moving average filtering and a hierarchical bottom-up clustering analysis that can determine the real-time flow regime (i.e., the Reynolds number, Re) in which the tank was operated during the mixing process. Finally, to demonstrate the versatility of the proposed prototype, experiments were conducted to identify the Reynolds number for different flow regimes (static, laminar, transition and turbulent), i.e., 0≤Re≤ 42,955. Obtained results were in agreement with the prevailing consensus on the onset and developed from different flow regimes in mechanically stirred tanks. Full article
(This article belongs to the Special Issue High Performance Sensors and Actuators in the Context of Industry 4.0 and Society Wellbeing: Theory, Developments and Applications)
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14 pages, 6352 KB  
Article
Deep Learning Based Apples Counting for Yield Forecast Using Proposed Flying Robotic System
by Şahin Yıldırım and Burak Ulu
Sensors 2023, 23(13), 6171; https://doi.org/10.3390/s23136171 - 5 Jul 2023
Cited by 11 | Viewed by 4422
Abstract
Nowadays, Convolution Neural Network (CNN) based deep learning methods are widely used in detecting and classifying fruits from faults, color and size characteristics. In this study, two different neural network model estimators are employed to detect apples using the Single-Shot Multibox Detection (SSD) [...] Read more.
Nowadays, Convolution Neural Network (CNN) based deep learning methods are widely used in detecting and classifying fruits from faults, color and size characteristics. In this study, two different neural network model estimators are employed to detect apples using the Single-Shot Multibox Detection (SSD) Mobilenet and Faster Region-CNN (Faster R-CNN) model architectures, with the custom dataset generated from the red apple species. Each neural network model is trained with created dataset using 4000 apple images. With the trained model, apples are detected and counted autonomously using the developed Flying Robotic System (FRS) in a commercially produced apple orchard. In this way, it is aimed that producers make accurate yield forecasts before commercial agreements. In this paper, SSD-Mobilenet and Faster R-CNN architecture models trained with COCO datasets referenced in many studies, and SSD-Mobilenet and Faster R-CNN models trained with a learning rate ranging from 0.015–0.04 using the custom dataset are compared experimentally in terms of performance. In the experiments implemented, it is observed that the accuracy rates of the proposed models increased to the level of 93%. Consequently, it has been observed that the Faster R-CNN model, which is developed, makes extremely successful determinations by lowering the loss value below 0.1. Full article
(This article belongs to the Special Issue High Performance Sensors and Actuators in the Context of Industry 4.0 and Society Wellbeing: Theory, Developments and Applications)
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26 pages, 17866 KB  
Article
Development of an Innovative Mechatronic Binder Machine
by João Sousa, Luis Figueiredo, Carlos Ventura, João Pedro Mendonça and José Machado
Sensors 2022, 22(3), 741; https://doi.org/10.3390/s22030741 - 19 Jan 2022
Cited by 1 | Viewed by 3045
Abstract
This paper describes the development of a mechatronic punch and bind office machine. Integrating smart technologies in the existing traditional business machines will ease the evolution of these systems, enabling productivity and efficiency. The development of an experimental platform that enables further advances [...] Read more.
This paper describes the development of a mechatronic punch and bind office machine. Integrating smart technologies in the existing traditional business machines will ease the evolution of these systems, enabling productivity and efficiency. The development of an experimental platform that enables further advances in servitization is required. To increase the binding rate of the office document, as well as to reduce the likelihood of errors, efforts have been made to develop a measuring system that allows the document to be properly measured and specifies the appropriate binding spine at the same time. As a complement, developments have been conducted in a system that enables the verification of the inserted spine. In addition, a system for automated document binding along with an integrated platform that allows the communication between all systems is presented. In both its hardware design and its underlying sensors, the new system has several advantages, providing significant performance improvements and upgradability over existing systems. This alternative comprises a system that enables a variety of sheets of paper, plastic or other materials to be punched. Full article
(This article belongs to the Special Issue High Performance Sensors and Actuators in the Context of Industry 4.0 and Society Wellbeing: Theory, Developments and Applications)
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Review

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26 pages, 4329 KB  
Review
Advanced Sensor Technologies in Cutting Applications: A Review
by Motaz Hassan, Roan Kirwin, Chandra Sekhar Rakurty and Ajay Mahajan
Sensors 2026, 26(3), 762; https://doi.org/10.3390/s26030762 - 23 Jan 2026
Viewed by 972
Abstract
Advances in sensing technologies are increasingly transforming cutting operations by enabling data-driven condition monitoring, predictive maintenance, and process optimization. This review surveys recent developments in sensing modalities for cutting systems, including vibration sensors, acoustic emission sensors, optical and vision-based systems, eddy-current sensors, force [...] Read more.
Advances in sensing technologies are increasingly transforming cutting operations by enabling data-driven condition monitoring, predictive maintenance, and process optimization. This review surveys recent developments in sensing modalities for cutting systems, including vibration sensors, acoustic emission sensors, optical and vision-based systems, eddy-current sensors, force sensors, and emerging hybrid/multi-modal sensing frameworks. Each sensing approach offers unique advantages in capturing mechanical, acoustic, geometric, or electromagnetic signatures related to tool wear, process instability, and fault development, while also showing modality-specific limitations such as noise sensitivity, environmental robustness, and integration complexity. Recent trends show a growing shift toward hybrid and multi-modal sensor fusion, where data from multiple sensors are combined using advanced data analytics and machine learning to improve diagnostic accuracy and reliability under changing cutting conditions. The review also discusses how artificial intelligence, Internet of Things connectivity, and edge computing enable scalable, real-time monitoring solutions, along with the challenges related to data needs, computational costs, and system integration. Future directions highlight the importance of robust fusion architectures, physics-informed and explainable models, digital twin integration, and cost-effective sensor deployment to accelerate adoption across various manufacturing environments. Overall, these advancements position advanced sensing and hybrid monitoring strategies as key drivers of intelligent, Industry 4.0-oriented cutting processes. Full article
(This article belongs to the Special Issue High Performance Sensors and Actuators in the Context of Industry 4.0 and Society Wellbeing: Theory, Developments and Applications)
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