Special Issue "Smart Manufacturing Systems in Industry 4.0"

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

Deadline for manuscript submissions: closed (10 September 2021).

Special Issue Editors

Dr. Przemysław Zawadzki
E-Mail Website
Guest Editor
Faculty of Mechanical Engineering and Management, Poznan University of Technology, Poznań, Poland
Interests: The wide scope of the engineering design process (automation with KBE, advanced CAM/CAM/CAE tools, reverse engineering methods; additive manufacturing techniques; the implementation of virtual and augmented reality systems in production processes; Smart manufacturing systems—smart Factory as a way to realize the mass customization strategy
Dr. Justyna Trojanowska
E-Mail Website
Guest Editor
Department of Production Engineering, Faculty of Mechanical Engineering, Poznan University of Technology, Poznań, Poland
Interests: quality assurance engineering; mechanical engineering; manufacturing engineering
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Special Issue Information

Dear Colleagues,

The fourth industrial revolution is usually defined using the broadly understood concepts of digitalization and integration, which allow for improving the efficiency of production processes. While this is essential from an engineering point of view, when considering the social and economic environment, however, the main idea of Industry 4.0 is to meet individual customer’s expectations—i.e., through implementing a mass customization strategy.

Given the above, the main goals of this Special Issue are to collect and promote advanced studies in the field of smart manufacturing systems. These studies should focus on the integration, development, and improvement of all production processes, from a product’s design to its delivery to the customer. Submissions detailing research in the field of manufacturing process automation, digital twin development, implementation of VR/AR techniques in industrial services are highly desirable. Practical experience through the description of case studies and original solutions within industry operations will bring significant value. Also of interest to this Special Issue are theoretically based works, including methodological and procedural considerations about smart manufacturing systems and their detailed processes.

Dr. Przemysław Zawadzki
Dr. Justyna Trojanowska
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 2000 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 processes
  • smart factory
  • cyberphysical systems
  • digital twin
  • virtual and augmented reality systems in the production process
  • Industry 4.0 for mass customization strategy
  • manufacturing process automation and simulation
  • computer-integrated manufacturing (CIM)
  • digital manufacturing
  • additive manufacturing
  • intelligent assistants—cooperating robots
  • smart design systems
  • knowledge-based processes

Published Papers (3 papers)

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Research

Article
Profit-Driven Methodology for Servo Press Motion Selection under Material Variability
Appl. Sci. 2021, 11(20), 9530; https://doi.org/10.3390/app11209530 - 14 Oct 2021
Viewed by 311
Abstract
Servo presses enable new types of forming motion profiles that can be used to stamp difficult materials, such as high strength steels. This paper presents an application of Bayesian statistics to intelligently select which motion profile maximizes the expected utility given the properties [...] Read more.
Servo presses enable new types of forming motion profiles that can be used to stamp difficult materials, such as high strength steels. This paper presents an application of Bayesian statistics to intelligently select which motion profile maximizes the expected utility given the properties of the incoming material. Bayesian logistic regression was used in conjunction with expected utility to estimate manufacturing returns, which can be used to make informed process decisions. A use case is presented, which demonstrates that the Smart Forming Algorithm can increase expected returns by more than 20%. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems in Industry 4.0)
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Article
A Mixed Reality Interface for a Digital Twin Based Crane
Appl. Sci. 2021, 11(20), 9480; https://doi.org/10.3390/app11209480 - 12 Oct 2021
Cited by 1 | Viewed by 849
Abstract
Digital twin technology empowers the digital transformation of the industrial world with an increasing amount of data, which meanwhile creates a challenging context for designing a human–machine interface (HMI) for operating machines. This work aims at creating an HMI for digital twin based [...] Read more.
Digital twin technology empowers the digital transformation of the industrial world with an increasing amount of data, which meanwhile creates a challenging context for designing a human–machine interface (HMI) for operating machines. This work aims at creating an HMI for digital twin based services. With an industrial crane platform as a case study, we presented a mixed reality (MR) application running on a Microsoft HoloLens 1 device. The application, consisting of visualization, interaction, communication, and registration modules, allowed crane operators to both monitor the crane status and control its movement through interactive holograms and bi-directional data communication, with enhanced mobility thanks to spatial registration and tracking of the MR environment. The prototype was quantitatively evaluated regarding the control accuracy in 20 measurements following a step-by-step protocol that we defined to standardize the measurement procedure. The results suggested that the differences between the target and actual positions were within the 10 cm range in three dimensions, which were considered sufficiently small regarding the typical crane operation use case of logistics purposes and could be improved with the adoption of robust registration and tracking techniques in our future work. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems in Industry 4.0)
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Article
A Model for Working Environment Monitoring in Smart Manufacturing
Appl. Sci. 2021, 11(6), 2850; https://doi.org/10.3390/app11062850 - 23 Mar 2021
Cited by 4 | Viewed by 869
Abstract
The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working [...] Read more.
The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems in Industry 4.0)
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