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28 pages, 6735 KiB

Open AccessArticle

Data Spine: A Federated Interoperability Enabler for Heterogeneous IoT Platform Ecosystems

by Rohit A. Deshmukh, Dileepa Jayakody, Alexander Schneider and Violeta Damjanovic-Behrendt

Sensors 2021, 21(12), 4010; https://doi.org/10.3390/s21124010 - 10 Jun 2021

Cited by 14 | Viewed by 4211

Today, the Internet of Things (IoT) is pervasive and characterized by the rapid growth of IoT platforms across different application domains, enabling a variety of business models and revenue streams. This opens new opportunities for companies to extend their collaborative networks and develop [...] Read more.

Today, the Internet of Things (IoT) is pervasive and characterized by the rapid growth of IoT platforms across different application domains, enabling a variety of business models and revenue streams. This opens new opportunities for companies to extend their collaborative networks and develop innovative cross-platform and cross-domain applications. However, the heterogeneity of today’s platforms is a major roadblock for mass creation of IoT platform ecosystems, pointing at the current absence of technology enablers for an easy and innovative composition of tools/services from the existing platforms. In this paper, we present the Data Spine, a federated platform enabler that bridges IoT interoperability gaps and enables the creation of an ecosystem of heterogeneous IoT platforms in the manufacturing domain. The Data Spine allows the ecosystem to be extensible to meet the need for incorporating new tools/services and platforms. We present a reference implementation of the Data Spine and a quantitative evaluation to demonstrate adequate performance of the system. The evaluation suggests that the Data Spine provides a multitude of advantages (single sign-on, provision of a low-code development environment to support interoperability and an easy and intuitive creation of cross-platform applications, etc.) over the traditional approach of users joining multiple platforms separately. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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21 pages, 1110 KiB

Open AccessArticle

Training the Next Industrial Engineers and Managers about Industry 4.0: A Case Study about Challenges and Opportunities in the COVID-19 Era

by Arriel Benis, Sofia Amador Nelke and Michael Winokur

Sensors 2021, 21(9), 2905; https://doi.org/10.3390/s21092905 - 21 Apr 2021

Cited by 18 | Viewed by 4720

Abstract

Training the next generation of industrial engineers and managers is a constant challenge for academia, given the fast changes of industrial technology. The current and predicted development trends in applied technologies affecting industry worldwide as formulated in the Industry 4.0 initiative have clearly [...] Read more.

Training the next generation of industrial engineers and managers is a constant challenge for academia, given the fast changes of industrial technology. The current and predicted development trends in applied technologies affecting industry worldwide as formulated in the Industry 4.0 initiative have clearly emphasized the needs for constantly adapting curricula. The sensible socioeconomic changes generated by the COVID-19 pandemic have induced significant challenges to society in general and industry. Higher education, specifically when dealing with Industry 4.0, must take these new challenges rapidly into account. Modernization of the industrial engineering curriculum combined with its migration to a blended teaching landscape must be updated in real-time with real-world cases. The COVID-19 crisis provides, paradoxically, an opportunity for dealing with the challenges of training industrial engineers to confront a virtual dematerialized work model which has accelerated during and will remain for the foreseeable future after the pandemic. The paper describes the methodology used for adapting, enhancing, and evaluating the learning and teaching experience under the urgent and unexpected challenges to move from face-to-face university courses distant and online teaching. The methodology we describe is built on a process that started before the onset of the pandemic, hence in the paper we start by describing the pre-COVID-19 status in comparison to published initiatives followed by the real time modifications we introduced in the faculty to adapt to the post-COVID-19 teaching/learning era. The focus presented is on Industry 4.0. subjects at the leading edge of the technology changes affecting the industrial engineering and technology management field. The manuscript addresses the flow from system design subjects to implementation areas of the curriculum, including practical examples and the rapid decisions and changes made to encompass the effects of the COVID-19 pandemic on content and teaching methods including feedback received from participants. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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8 pages, 1652 KiB

Open AccessCommunication

Design Study for Automatic Production Line of a Sub-Assemblies of New Generation Car Body Structures Compliant with the “Industry 4.0” Concept

by Ireneusz Wrobel and Marcin Sidzina

Sensors 2021, 21(7), 2434; https://doi.org/10.3390/s21072434 - 01 Apr 2021

Cited by 5 | Viewed by 3488

Abstract

A design study of automatic line-to-production of a new generation of car body structures compliant with the Industry 4.0 concept is described in this paper. The line is based on the hot-stamping technology of components of a car body structure from 22MnB5 steel [...] Read more.

A design study of automatic line-to-production of a new generation of car body structures compliant with the Industry 4.0 concept is described in this paper. The line is based on the hot-stamping technology of components of a car body structure from 22MnB5 steel sheets. Additional modules of the designed production line are: laser-trimming station, station to completion (kitting-up), and spot-welding station of the subassemblies. Technical requirements to be complied with by such line and scheme of exchange of information between modules of the line were defined. The conclusions were formulated. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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32 pages, 4226 KiB

Open AccessArticle

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

by Pavol Tanuska, Lukas Spendla, Michal Kebisek, Rastislav Duris and Maximilian Stremy

Sensors 2021, 21(7), 2376; https://doi.org/10.3390/s21072376 - 29 Mar 2021

Cited by 23 | Viewed by 4342

Abstract

One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In [...] Read more.

One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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21 pages, 6272 KiB

Open AccessArticle

Towards Aircraft Maintenance Metaverse Using Speech Interactions with Virtual Objects in Mixed Reality

by Aziz Siyaev and Geun-Sik Jo

Sensors 2021, 21(6), 2066; https://doi.org/10.3390/s21062066 - 15 Mar 2021

Cited by 116 | Viewed by 13096

Abstract

Metaverses embedded in our lives create virtual experiences inside of the physical world. Moving towards metaverses in aircraft maintenance, mixed reality (MR) creates enormous opportunities for the interaction with virtual airplanes (digital twin) that deliver a near-real experience, keeping physical distancing during pandemics. [...] Read more.

Metaverses embedded in our lives create virtual experiences inside of the physical world. Moving towards metaverses in aircraft maintenance, mixed reality (MR) creates enormous opportunities for the interaction with virtual airplanes (digital twin) that deliver a near-real experience, keeping physical distancing during pandemics. 3D twins of modern machines exported to MR can be easily manipulated, shared, and updated, which creates colossal benefits for aviation colleges who still exploit retired models for practicing. Therefore, we propose mixed reality education and training of aircraft maintenance for Boeing 737 in smart glasses, enhanced with a deep learning speech interaction module for trainee engineers to control virtual assets and workflow using speech commands, enabling them to operate with both hands. With the use of the convolutional neural network (CNN) architecture for audio features and learning and classification parts for commands and language identification, the speech module handles intermixed requests in English and Korean languages, giving corresponding feedback. Evaluation with test data showed high accuracy of prediction, having on average 95.7% and 99.6% on the F1-Score metric for command and language prediction, respectively. The proposed speech interaction module in the aircraft maintenance metaverse further improved education and training, giving intuitive and efficient control over the operation, enhancing interaction with virtual objects in mixed reality. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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17 pages, 4094 KiB

Open AccessArticle

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

by Nika Brili, Mirko Ficko and Simon Klančnik

Sensors 2021, 21(5), 1917; https://doi.org/10.3390/s21051917 - 09 Mar 2021

Cited by 23 | Viewed by 3227

Abstract

This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite [...] Read more.

This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator’s knowledge and competence. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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25 pages, 3312 KiB

Open AccessArticle

A Real-Time Physical Progress Measurement Method for Schedule Performance Control Using Vision, an AR Marker and Machine Learning in a Ship Block Assembly Process

by Taihun Choi and Yoonho Seo

Sensors 2020, 20(18), 5386; https://doi.org/10.3390/s20185386 - 20 Sep 2020

Cited by 9 | Viewed by 4740

Abstract

Progress control is a key technology for successfully carrying out a project by predicting possible problems, particularly production delays, and establishing measures to avoid them (decision-making). However, shipyard progress management is still dependent on the empirical judgment of the manager, and this has [...] Read more.

Progress control is a key technology for successfully carrying out a project by predicting possible problems, particularly production delays, and establishing measures to avoid them (decision-making). However, shipyard progress management is still dependent on the empirical judgment of the manager, and this has led to delays in delivery, which raises ship production costs. Therefore, this paper proposes a methodology for shipyard ship block assembly plants that enables objective process progress measurement based on real-time work performance data, rather than the empirical judgment of a site manager. In particular, an IoT-based physical progress measurement method that can automatically measure work performance without human intervention is presented for the mounting and welding activities of ship block assembly work. Both an augmented reality (AR) marker-based image analysis system and a welding machine time-series data-based machine learning model are presented for measuring the performances of the mounting and welding activities. In addition, the physical progress measurement method proposed in this study was applied to the ship block assembly plant of shipyard H to verify its validity. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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23 pages, 2499 KiB

Open AccessArticle

Formal Verification of Control Modules in Cyber-Physical Systems

by Iwona Grobelna

Sensors 2020, 20(18), 5154; https://doi.org/10.3390/s20185154 - 10 Sep 2020

Cited by 6 | Viewed by 3532

Abstract

The paper proposes a novel formal verification method for a state-based control module of a cyber-physical system. The initial specification in the form of user-friendly UML state machine diagrams is written as an abstract rule-based logical model. The logical model is then used [...] Read more.

The paper proposes a novel formal verification method for a state-based control module of a cyber-physical system. The initial specification in the form of user-friendly UML state machine diagrams is written as an abstract rule-based logical model. The logical model is then used both for formal verification using the model checking technique and for prototype implementation in FPGA devices. The model is automatically transformed into a verifiable model in nuXmv format and into synthesizable code in VHDL language, which ensures that the resulting models are consistent with each other. It also allows the early detection of any errors related to the specification. A case study of a manufacturing automation system is presented to illustrate the approach. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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24 pages, 1787 KiB

Open AccessArticle

Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0

by Attila Frankó, Gergely Vida and Pal Varga

Sensors 2020, 20(13), 3709; https://doi.org/10.3390/s20133709 - 02 Jul 2020

Cited by 53 | Viewed by 5893

Abstract

Revolutionizing logistics and supply chain management in smart manufacturing is one of the main goals of the Industry 4.0 movement. Emerging technologies such as autonomous vehicles, Cyber-Physical Systems and digital twins enable highly automated and optimized solutions in these fields to achieve full [...] Read more.

Revolutionizing logistics and supply chain management in smart manufacturing is one of the main goals of the Industry 4.0 movement. Emerging technologies such as autonomous vehicles, Cyber-Physical Systems and digital twins enable highly automated and optimized solutions in these fields to achieve full traceability of individual products. Tracking various assets within shop-floors and the warehouse is a focal point of asset management; its aim is to enhance the efficiency of logistical tasks. Global players implement their own solutions based on the state of the art technologies. Small and medium companies, however, are still skeptic toward identification based tracking methods, because of the lack of low-cost and reliable solutions. This paper presents a novel, working, reliable, low-cost, scalable solution for asset tracking, supporting global asset management for Industry4.0. The solution uses high accuracy indoor positioning—based on Ultra-Wideband (UWB) radio technology—combined with RFID-based tracking features. Identifying assets is one of the most challenging parts of this work, so this paper focuses on how different identification approaches can be combined to facilitate an efficient and reliable identification scheme. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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Review

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24 pages, 24258 KiB

Open AccessEditor’s ChoiceReview

Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review

by Shehzad Ahmed, Tahera Kalsoom, Naeem Ramzan, Zeeshan Pervez, Muhammad Azmat, Bassam Zeb and Masood Ur Rehman

Sensors 2021, 21(12), 4158; https://doi.org/10.3390/s21124158 - 17 Jun 2021

Cited by 41 | Viewed by 8770

Abstract

The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by [...] Read more.

The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by improving inaccuracies. Extant literature has given attention to the organisation’s capability to collect and evaluate information to balance between strategy and goals. The majority of studies focus on investigating IoT’s impact on different areas such as sustainability, organisational structure, lean manufacturing, product development, and strategic management. However, research investigating the relationships and impact of IoT on SCV is minimal. This study closes this gap using a structured literature review to critically analyse existing literature to synthesise the use of IoT applications in SCs to gain visibility, and the SC. We found key IoT technologies that help SCs gain visibility, and seven benefits and three key challenges of these technologies. We also found the concept of Supply 4.0 that grasps the element of Industry 4.0 within the SC context. This paper contributes by combining IoT application synthesis, enablers, and challenges in SCV by highlighting key IoT technologies used in the SCs to gain visibility. Finally, the authors propose an empirical research agenda to address the identified gaps. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

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