Special Issue "Industry 4.0 Based Smart Manufacturing Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editor

Prof. Dr. Tobias Meisen
Website
Guest Editor
School of Electrical, Information and Media Engineering, University of Wuppertal, Rainer-Gruenter-Str. 21, D-42119 Wuppertal, Germany
Interests: Deep and Machine Learning; Transfer Learning; Explainable and Transparent Artificial Intelligence; Knowledge Graphs; Semantic Interoperability

Special Issue Information

Dear Colleagues,

In a traditional way, manufacturing means the engineering process of creating industrial products from raw materials using a variety of subtractive and additive methods. However, in recent years, the concept of manufacturing has drastically shifted. After the first wave of digitization, new and modernized technologies such as integrated sensors, advanced robotics, and artificial intelligence led to the so-called Smart Manufacturing as part of the fourth industrial revolution—often referred to as Industry 4.0. In Smart Manufacturing, production tools are connected to constantly gather data, monitor production processes, and perform real-time optimization. Smart Manufacturing therefore includes not only data collection and processing, but also inferring from and reasoning about data by means of cognitive computing to improve the end product. In doing so, the vision of Smart Manufacturing leads to a self-monitoring and self-optimization of the entire end-to-end manufacturing process.

The key challenges of Smart Manufacturing are manifold, and several aspects need to be taken into consideration:

  • New ways of data acquisition that require implementing new sensors and the capability for connectivity in production machines and products, as well as new ways to store and propagate such data in a meaningful way;
  • Employing data science approaches to automate or optimize manufacturing to remove ‘trial-and-error’ approaches;
  • Developing new robotics and closed loop control feedback at the hardware level;
  • Sustainably transferring and deploying solutions into the world while addressing broader clean energy challenges and reducing material waste for the environment.

Industry 4.0 adds, among other things, aspects of business model development to this mostly technological perspective. The new value of data leads to new and changed business models and opportunities regarding the internal optimization of business processes. However, we have not yet fully understood how data can be managed as a central resource and how the full potential of data as a resource can be harnessed.

The aim of the edition “Industry 4.0-Based Smart Manufacturing Systems” is therefore to present new and innovative methods in which data can be better and more efficiently extracted, collected, processed, and finally used in Smart Manufacturing environments.

Prof. Dr. Tobias Meisen
Guest Editor

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 papers will be 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. Applied Sciences 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 1800 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 Manufacturing
  • Industry 4.0
  • Artificial intelligence
  • Data-driven production
  • Interoperability
  • Data as an asset

Published Papers (5 papers)

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Research

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Open AccessArticle
PetriNet Editor + PetriNet Engine: New Software Tool For Modelling and Control of Discrete Event Systems Using Petri Nets and Code Generation
Appl. Sci. 2020, 10(21), 7662; https://doi.org/10.3390/app10217662 - 29 Oct 2020
Abstract
Petri nets are an important tool for creation of new platforms for digitised production systems due to their versatility in modelling discrete event systems. For the development of modern complex production processes for Industry 4.0, using advanced mathematical models based on Petri nets [...] Read more.
Petri nets are an important tool for creation of new platforms for digitised production systems due to their versatility in modelling discrete event systems. For the development of modern complex production processes for Industry 4.0, using advanced mathematical models based on Petri nets is an appropriate and effective option. The main aim of the proposed article is to design a new software tool for modelling and control of discrete event systems using Arduino-type microcontrollers and code generation techniques. To accomplish this task, a new tool called “PetriNet editor + PetriNet engine” based on Petri nets is proposed able to generate the code for the microcontroller according to the modelled Petri net. The developed software tool was successfully verified in control of a laboratory plant. Offering a graphical environment for the design of discrete event system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0). Full article
(This article belongs to the Special Issue Industry 4.0 Based Smart Manufacturing Systems)
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Open AccessArticle
Measurement of Wafer Focus by Grating Shearing Interferometry
Appl. Sci. 2020, 10(21), 7467; https://doi.org/10.3390/app10217467 - 23 Oct 2020
Abstract
A method applied for improving the measurement precision and efficiency of wafer focusing in an optical lithography instrument (OLI) is introduced. Based on grating shearing interferometry, the defocus and tilt of the wafer are measured by testing the phase difference in the interference [...] Read more.
A method applied for improving the measurement precision and efficiency of wafer focusing in an optical lithography instrument (OLI) is introduced. Based on grating shearing interferometry, the defocus and tilt of the wafer are measured by testing the phase difference in the interference pattern. To validate the feasibility, an experiment is implemented, of which the measurement precision is indicated as 30 nm due to the high precision of phase-resolving arithmetic after analyzing the measurement uncertainty and indicating the precision by interferometer. Full article
(This article belongs to the Special Issue Industry 4.0 Based Smart Manufacturing Systems)
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Open AccessArticle
Integration of an MES and AIV Using a LabVIEW Middleware Scheduler Suitable for Use in Industry 4.0 Applications
Appl. Sci. 2020, 10(20), 7054; https://doi.org/10.3390/app10207054 - 11 Oct 2020
Cited by 1
Abstract
Industry 4.0 uses the analysis of real-time data, artificial intelligence, automation, and the interconnection of components of the production lines to improve manufacturing efficiency and quality. Manufacturing Execution Systems (MESs) and Autonomous Intelligent Vehicles (AIVs) are key elements of Industry 4.0 implementations. An [...] Read more.
Industry 4.0 uses the analysis of real-time data, artificial intelligence, automation, and the interconnection of components of the production lines to improve manufacturing efficiency and quality. Manufacturing Execution Systems (MESs) and Autonomous Intelligent Vehicles (AIVs) are key elements of Industry 4.0 implementations. An MES connects, monitors, and controls data flows on the factory floor, while automation is achieved by using AIVs. The Robot Operating System (ROS) built AIVs are targeted here. To facilitate MES and AIV interactions, there is a need to integrate the MES and the AIVs to help in building an automated and interconnected manufacturing environment. This integration needs middleware, which understands both MES and AIVs. To address this issue, a LabVIEW-based scheduler is proposed here as the middleware. LabVIEW communicates with the MES through webservices and has support for ROS. The main task of the scheduler is to control the AIV based on MES requests. The scheduler developed was tested in a real factory environment using the SAP MES and a Robotnik ‘RB-1′ robot. The scheduler interface provides real-time information about the current status of the MES, AIV, and the current stage of scheduler processing. The proposed scheduler provides an efficient automated product delivery system that transports the product from process cell to process cell using the AIV, based on the production sequences defined by the MES. In addition, using the proposed scheduler, integration of an MES is possible with any low-cost ROS-built AIV. Full article
(This article belongs to the Special Issue Industry 4.0 Based Smart Manufacturing Systems)
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Open AccessArticle
New Software Tool for Modeling and Control of Discrete-Event and Hybrid Systems Using Timed Interpreted Petri Nets
Appl. Sci. 2020, 10(15), 5027; https://doi.org/10.3390/app10155027 - 22 Jul 2020
Cited by 1
Abstract
For the development of modern complex production processes in Industry 4.0, it is appropriate to effectively use advanced mathematical models based on Petri nets. Due to their versatility in modeling discrete-event systems, Petri nets are an important support in creating new platforms for [...] Read more.
For the development of modern complex production processes in Industry 4.0, it is appropriate to effectively use advanced mathematical models based on Petri nets. Due to their versatility in modeling discrete-event systems, Petri nets are an important support in creating new platforms for digitized production systems. The main aim of the proposed article is to design a new software tool for modeling and control of discrete-event and hybrid systems using Arduino and similar microcontrollers. To accomplish these tasks, a new tool called PN2ARDUINO based on Petri nets is proposed able to communicate with the microcontroller. Communication with the microcontroller is based on the modified Firmata protocol hence, the control algorithm can be implemented on all microcontrollers that support this type of protocol. The developed software tool was successfully verified in control of laboratory systems. In addition, it can be used for education and research purposes as it offers a graphical environment for designing control algorithms for hybrid and mainly discrete-event systems. The proposed software tool can improve education and practice in cyber-physical systems (Industry 4.0). Full article
(This article belongs to the Special Issue Industry 4.0 Based Smart Manufacturing Systems)
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Review

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Open AccessReview
A Scoping Review on Virtual Reality-Based Industrial Training
Appl. Sci. 2020, 10(22), 8224; https://doi.org/10.3390/app10228224 - 20 Nov 2020
Abstract
The fourth industrial revolution has forced most companies to technologically evolve, applying new digital tools, so that their workers can have the necessary skills to face changing work environments. This article presents a scoping review of the literature on virtual reality-based training systems. [...] Read more.
The fourth industrial revolution has forced most companies to technologically evolve, applying new digital tools, so that their workers can have the necessary skills to face changing work environments. This article presents a scoping review of the literature on virtual reality-based training systems. The methodology consisted of four steps, which pose research questions, document search, paper selection, and data extraction. From a total of 350 peer-reviewed database articles, such as SpringerLink, IEEEXplore, MDPI, Scopus, and ACM, 44 were eventually chosen, mostly using the virtual reality haptic glasses and controls from Oculus Rift and HTC VIVE. It was concluded that, among the advantages of using this digital tool in the industry, is the commitment, speed, measurability, preservation of the integrity of the workers, customization, and cost reduction. Even though several research gaps were found, virtual reality is presented as a present and future alternative for the efficient training of human resources in the industrial field. Full article
(This article belongs to the Special Issue Industry 4.0 Based Smart Manufacturing Systems)
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