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Artificial Intelligence as Driving Force for Industry 4.0, Sensors, and Digital Twin

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

Deadline for manuscript submissions: closed (28 April 2023) | Viewed by 8854

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Prof. Dr. Yungcheol Byun

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Department of Computer Engineering, Jeju National University, Jeju-si 63243, Republic of Korea
Interests: AI and machine learning; pattern recognition; sensor; knowledge discovery; time-series data analysis and prediction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Óscar García

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UNIR - Universidad Internacional de La Rioja, de García Martín, 21, 28224 Pozuelo de Alarcón, Madrid, Spain
Interests: big data; Artificial Intelligence; IoT; Industry 4.0; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main aim of this Special Issue is to present a forum for researchers comprising the entire range of artificial intelligence and machine learning-based applications in Industry 4.0.

The Fourth Industrial Revolution, or Industry 4.0, is focused on the continuous modifications and improvements in the production processes and methods used to create goods, as well as the efficiency of these processes, which is rising quickly with the development of digital twin and machine learning methods. A digital twin is a digital representation of a real-world object, such as a jet engine, wind farm, or even larger objects such as a building or even an entire city. Future industry development techniques and trends are embodied by Industry 4.0, which aims to create more sophisticated and intelligent manufacturing processes. In Industry 4.0, machine learning combines various technologies to allow computer programs and other devices to perceive, understand, respond to, and learn from human actions. IoT allows multiple systems and sensors to communicate, enabling real-time collaboration with other human systems and operators. Using advanced technology, the industrial production system can be made more effective. The manufacturing industry is constantly expanding because of Industry 4.0's technological advancements.

In this Special Issue, we would like to encourage people to contribute their latest developments, ideas and review articles on machine learning applications in Industry 4.0. This Special Issue will focus on the essential ML-based applications in smart manufacturing to optimize digital twins. However, this focus is not limited to the following:

Sensors of digital twin;
Digital twin for the renewable energy sector;
Smart factory;
Digital twin in healthcare;
Electric vehicles and machines;
Quality control;
Predictive maintenance;
Machine learning for prediction;
Blockchain in Industry 4.0;
Internet of Things for digital twin.

Prof. Dr. Yungcheol Byun
Prof. Dr. Óscar García
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

sensors of digital twin
digital twin for the renewable energy sector
smart factory
digital twin in healthcare
electric vehicles and machines
quality control
predictive maintenance
machine learning for prediction
blockchain in Industry 4.0
Internet of Things for digital twin

Published Papers (4 papers)

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Research

21 pages, 41368 KiB

Open AccessArticle

Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer

by Hibah Alatawi, Nouf Albalawi, Ghadah Shahata, Khulud Aljohani, A’aeshah Alhakamy and Mihran Tuceryan

Sensors 2023, 23(13), 6024; https://doi.org/10.3390/s23136024 - 29 Jun 2023

Cited by 3 | Viewed by 2248

Abstract

The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of

1.000

and a standard deviation of

0.000

. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is

0.000

shows that there is no variability in the outcomes. Full article

(This article belongs to the Special Issue Artificial Intelligence as Driving Force for Industry 4.0, Sensors, and Digital Twin)

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18 pages, 4272 KiB

Open AccessArticle

Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises

by Simona Skėrė, Aušra Žvironienė, Kazimieras Juzėnas and Stasė Petraitienė

Sensors 2023, 23(9), 4498; https://doi.org/10.3390/s23094498 - 05 May 2023

Cited by 4 | Viewed by 2068

Abstract

In the industrial sector, production processes are continuously evolving, but issues and delays in production are still commonplace. Complex problems often require input from production managers or experts even though Industry 4.0 provides advanced technological solutions. Small and medium-sized enterprises (SMEs) normally rely more on expert opinion since they face difficulties implementing the newest and most advanced Industry 4.0 technologies. This reliance on human expertise can cause delays in the production processes, ultimately, impacting the efficiency and profitability of the enterprise. As SMEs are mostly niche markets and produce small batches, dynamics in production operations and the need for quick responses cannot be avoided. To address these issues, a decision support method for dynamic production planning (DSM DPP) was developed to optimize the production processes. This method involves the use of algorithms and programming in Matlab to create a decision support module that provides solutions to complex problems in real-time. The aim of this method is to combine not only technical but also human factors to efficiently optimize dynamic production planning. It is hardly noticeable in other methods the involvement of human factors such as skills of operations, speed of working, or salary size. The method itself is based on real-time data so examples of the required I 4.0 technologies for production sites are described in this article—Industrial Internet of Things, blockchains, sensors, etc. Each technology is presented with examples of usage and the requirement for it. Moreover, to confirm the effectiveness of this method, tests were made with real data that were acquired from a metal processing company in Lithuania. The method was tested with existing production orders, and found to be universal, making it adaptable to different production settings. This study presents a practical solution to complex problems in industrial settings and demonstrates the potential for DSM DPP to improve production processes while checking the latest data from production sites that are conducted through cloud systems, sensors, IoT, etc. The implementation of this method in SMEs could result in significant improvements in production efficiency, ultimately, leading to increased profitability. Full article

(This article belongs to the Special Issue Artificial Intelligence as Driving Force for Industry 4.0, Sensors, and Digital Twin)

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12 pages, 2066 KiB

Open AccessArticle

Enhancing Quality Control in Web-based Participatory Augmented Reality Business Card Information System Design

by Yongjun Kim and Yung-Cheol Byun

Sensors 2023, 23(8), 4068; https://doi.org/10.3390/s23084068 - 18 Apr 2023

Cited by 1 | Viewed by 1467

Abstract

The rapid development of information and communication technology has fostered a natural integration of technology and design. As a result, there is increasing interest in Augmented Reality (AR) business card systems that leverage digital media. This research aims to advance the design of an AR-based participatory business card information system in line with contemporary trends. Key aspects of this study include applying technology to acquire contextual information from paper business cards, transmitting it to a server, and delivering it to mobile devices; facilitating interactivity between users and content through a screen interface; providing multimedia business content (video, image, text, 3D elements) via image markers recognized by users on mobile devices, while also adapting the type and method of content delivery. The AR business card system designed in this research enhances traditional paper business cards by incorporating visual information and interactive elements and automatically generating buttons linked to phone numbers, location information, and homepages. This innovative approach enables users to interact and enriches their overall experience while adhering to strict quality control measures. Full article

(This article belongs to the Special Issue Artificial Intelligence as Driving Force for Industry 4.0, Sensors, and Digital Twin)

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19 pages, 1737 KiB

Open AccessArticle

Data-Driven Supply Chain Operations—The Pilot Case of Postal Logistics and the Cross-Border Optimization Potential

by Tanja Zdolsek Draksler, Miha Cimperman and Matevž Obrecht

Sensors 2023, 23(3), 1624; https://doi.org/10.3390/s23031624 - 02 Feb 2023

Cited by 9 | Viewed by 2469

Abstract

According to the defined challenge of cross-border delivery, a pilot experiment based on the integration of new digital technologies to assess process optimization potential in the postal sector was designed. The specifics were investigated with events processing based on digital representation. Different events were simulated with scenario analysis with the integration of the Cognitive Advisor and supported by the monitoring of KPIs. The business environment is forcing logistics companies to optimize their delivery processes, integrate new technologies, improve their performance metrics, and move towards Logistics 4.0. Their main goals are to simultaneously reduce costs, environmental impact, delivery times, and route length, as well as to increase customer satisfaction. This pilot experiment demonstrates the integration of new digital technologies for process optimization in real time to manage intraday changes. Postal operators can increase flexibility, introduce new services, improve utilization by up to 50%, and reduce costs and route length by 12.21%. The Cognitive Advisor has shown great potential for the future of logistics by enabling a dynamic approach to managing supply chain disruptions using sophisticated data analytics for process optimization based on the existing delivery infrastructure and improving business processes. Research originality is identified with a novel approach of real-time simulation based on the integration of the Cognitive Advisor in postal delivery. Full article

(This article belongs to the Special Issue Artificial Intelligence as Driving Force for Industry 4.0, Sensors, and Digital Twin)

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Artificial Intelligence as Driving Force for Industry 4.0, Sensors, and Digital Twin

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