Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Smart Manufacturing: Advances and Challenges
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Smart Manufacturing: Advances and Challenges

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 30514

Special Issue Editors

Prof. Dr. Hilde Perez

E-Mail Website
Guest Editor
Department of Mechanical, Computer and Aerospace Engineering, University of Leon, 24071 Leon, Spain
Interests: manufacturing; process optimization; process planning; industry 4.0; collaborative robots; local positioning systems (LPS); localization; automated guided vehicle (AGV); artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Javier Díez-González

E-Mail Website
Guest Editor
Department of Mechanical, Computer and Aerospace Engineering, University of Leon, 24071 Leon, Spain
Interests: wireless sensor networks; artificial intelligence; evolutionary computation; algorithms; Industry 4.0; manufacturing; optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rubén Álvarez

E-Mail Website
Guest Editor
Department of Mechanical, Computer and Aerospace Engineering, University of Leon, 24071 Leon, Spain
Interests: localization; wireless sensor networks; artificial intelligence; evolutionary computation; algorithms; Industry 4.0; manufacturing; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The COVID-19 crisis of 2020 has shown the necessity to strengthen our industrial sector, enhancing its resilience and flexibility in order to reduce dependencies on other countries. Digital transformation seems inevitable and the only way to improve the production system. It appears crucial to master digital-enabling technologies in order to improve production efficiency. However, digital transformation is just beginning.

This has encouraged scientists and researchers to contribute to advanced techniques in the field of artificial intelligence applied to manufacturing processes, industrial applications of the Internet of Things, industrial sensor networks, collaborative robots, augmented reality in industrial environments, and many more.

This Special Issue focuses on recent advances and new challenges in smart manufacturing. We welcome original research contributions and reviews of state-of-the-art studies from academia and industry. The Special Issue topics include, but are not limited to, the following:

  • Industry 4.0;
  • Smart manufacturing;
  • Industrial Internet platforms;
  • Collaborative robots;
  • Automated guided vehicles (AGV);
  • Industrial applications of the Internet of Things;
  • Smart logistics related to industrial applications;
  • Industrial sensor networks;
  • Augmented reality in industrial environments;
  • Automatic object identification (e.g., RFID);
  • Real-time locating in production and logistics;
  • Artificial intelligence for industrial applications;
  • Digital twin;
  • Cloud/digital manufacturing and edge computing.

Prof. Dr. Hilde Perez
Dr. Javier Díez-González
Prof. Dr. Rubén Álvarez
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

  • Smart manufacturing
  • Industry 4.0
  • Industrial Internet platforms
  • Collaborative robots
  • AGV
  • Sensor networks
  • Digital manufacturing
  • IIoT
  • Smart logistics

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

24 pages, 12699 KiB  
Article
Product Assembly Assistance System Based on Pick-To-Light and Computer Vision Technology
by Darko Hercog, Primož Bencak, Uroš Vincetič and Tone Lerher
Sensors 2022, 22(24), 9769; https://doi.org/10.3390/s22249769 - 13 Dec 2022
Cited by 6 | Viewed by 4507
Abstract
Product assembly is often one of the last steps in the production process. Product assembly is often carried out by workers (assemblers) rather than robots, as it is generally challenging to adapt automation to any product. When assembling complex products, it can take [...] Read more.
Product assembly is often one of the last steps in the production process. Product assembly is often carried out by workers (assemblers) rather than robots, as it is generally challenging to adapt automation to any product. When assembling complex products, it can take a long time before the assembler masters all the steps and can assemble the product independently. Training time has no added value; therefore, it should be reduced as much as possible. This paper presents a custom-developed system that enables the guided assembly of complex and diverse products using modern technologies. The system is based on pick-to-light (PTL) modules, used primarily in logistics as an additional aid in the order picking process, and Computer Vision technology. The designed system includes a personal computer (PC), several custom-developed PTL modules and a USB camera. The PC with a touchscreen visualizes the assembly process and allows the assembler to interact with the system. The developed PC application guides the operator through the assembly process by showing all the necessary assembly steps and parts. Two-step verification is used to ensure that the correct part is picked out of the bin, first by checking that the correct pushbutton on the PTL module has been pressed and second by using a camera with a Computer Vision algorithm. The paper is supported by a use case demonstrating that the proposed system reduces the assembly time of the used product. The presented solution is scalable and flexible as it can be easily adapted to show the assembly steps of another product. Full article
(This article belongs to the Special Issue Smart Manufacturing: Advances and Challenges)
►▼ Show Figures

Figure 1

13 pages, 2520 KiB  
Article
Data Twin-Driven Cyber-Physical Factory for Smart Manufacturing
by Jung-Sing Jwo, Cheng-Hsiung Lee and Ching-Sheng Lin
Sensors 2022, 22(8), 2821; https://doi.org/10.3390/s22082821 - 7 Apr 2022
Cited by 20 | Viewed by 3335
Abstract
Because of the complex production processes and technology-intensive operations that take place in the aerospace and defense industry, introducing Industry 4.0 into the manufacturing processes of aircraft composite materials is inevitable. Digital Twin and Cyber-Physical Systems in Industry 4.0 are key techniques to [...] Read more.
Because of the complex production processes and technology-intensive operations that take place in the aerospace and defense industry, introducing Industry 4.0 into the manufacturing processes of aircraft composite materials is inevitable. Digital Twin and Cyber-Physical Systems in Industry 4.0 are key techniques to develop digital manufacturing. Since it is very difficult to create high-fidelity virtual models, the development of digital manufacturing for aircraft manufacturers is challenging. In this study, we provide a view from a data simulation perspective and adopt machine learning approaches to simplify the high-fidelity virtual models in Digital Twin. The novel concept is called Data Twin, and the deployable service to support the simulation is known as the Data Twin Service (DTS). Relying on the DTS, we also propose a microservice software architecture, Cyber-Physical Factory (CPF), to simulate the shop floor environment. Additionally, there are two war rooms in the CPF that can be used to establish a collaborative platform: one is the Physical War Room, used to integrate real data, and the other is the Cyber War Room for handling simulation data and the results of the CPF. Full article
(This article belongs to the Special Issue Smart Manufacturing: Advances and Challenges)
►▼ Show Figures

Figure 1

26 pages, 23147 KiB  
Article
Multi-Robot Preemptive Task Scheduling with Fault Recovery: A Novel Approach to Automatic Logistics of Smart Factories
by Vivian Cremer Kalempa, Luis Piardi, Marcelo Limeira and André Schneider de Oliveira
Sensors 2021, 21(19), 6536; https://doi.org/10.3390/s21196536 - 30 Sep 2021
Cited by 9 | Viewed by 3153
Abstract
This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as Multi-Robot Preemptive Task Scheduling with Fault Recovery (MRPF). It considers [...] Read more.
This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as Multi-Robot Preemptive Task Scheduling with Fault Recovery (MRPF). It considers the interaction between running processes and their tasks for management at each new event, prioritizing the more relevant tasks without idleness and latency. The benefit of this approach is the optimization of production in smart factories, where autonomous robots are being employed to improve efficiency and increase flexibility. The evaluation of MRPF is performed through experimentation in small-scale warehouse logistics, referred to as Augmented Reality to Enhanced Experimentation in Smart Warehouses (ARENA). An analysis of priority scheduling, task preemption, and fault recovery is presented to show the benefits of the proposed approach. Full article
(This article belongs to the Special Issue Smart Manufacturing: Advances and Challenges)
►▼ Show Figures

Figure 1

23 pages, 5087 KiB  
Article
Digital Twin for Automatic Transportation in Industry 4.0
by Alberto Martínez-Gutiérrez, Javier Díez-González, Rubén Ferrero-Guillén, Paula Verde, Rubén Álvarez and Hilde Perez
Sensors 2021, 21(10), 3344; https://doi.org/10.3390/s21103344 - 11 May 2021
Cited by 66 | Viewed by 7893
Abstract
Industry 4.0 is the fourth industrial revolution consisting of the digitalization of processes facilitating an incremental value chain. Smart Manufacturing (SM) is one of the branches of the Industry 4.0 regarding logistics, visual inspection of pieces, optimal organization of processes, machine sensorization, real-time [...] Read more.
Industry 4.0 is the fourth industrial revolution consisting of the digitalization of processes facilitating an incremental value chain. Smart Manufacturing (SM) is one of the branches of the Industry 4.0 regarding logistics, visual inspection of pieces, optimal organization of processes, machine sensorization, real-time data adquisition and treatment and virtualization of industrial activities. Among these tecniques, Digital Twin (DT) is attracting the research interest of the scientific community in the last few years due to the cost reduction through the simulation of the dynamic behaviour of the industrial plant predicting potential problems in the SM paradigm. In this paper, we propose a new DT design concept based on external service for the transportation of the Automatic Guided Vehicles (AGVs) which are being recently introduced for the Material Requirement Planning satisfaction in the collaborative industrial plant. We have performed real experimentation in two different scenarios through the definition of an Industrial Ethernet platform for the real validation of the DT results obtained. Results show the correlation between the virtual and real experiments carried out in the two scenarios defined in this paper with an accuracy of 97.95% and 98.82% in the total time of the missions analysed in the DT. Therefore, these results validate the model created for the AGV navigation, thus fulfilling the objectives of this paper. Full article
(This article belongs to the Special Issue Smart Manufacturing: Advances and Challenges)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

40 pages, 747 KiB  
Review
Smart Manufacturing and Digitalization of Metrology: A Systematic Literature Review and a Research Agenda
by Carlos Roberto H. Barbosa, Manuel C. Sousa, Maria Fatima L. Almeida and Rodrigo F. Calili
Sensors 2022, 22(16), 6114; https://doi.org/10.3390/s22166114 - 16 Aug 2022
Cited by 14 | Viewed by 7247
Abstract
Smart manufacturing comprises fully integrated manufacturing systems that respond in real time to meet the changing demands and conditions in industrial activities, supply networks and customer needs. A smart manufacturing environment will face new challenges, including those concerning metrological issues, i.e., analysis of [...] Read more.
Smart manufacturing comprises fully integrated manufacturing systems that respond in real time to meet the changing demands and conditions in industrial activities, supply networks and customer needs. A smart manufacturing environment will face new challenges, including those concerning metrological issues, i.e., analysis of large quantities of data; communication systems for digitalization; measurement standards for automated process control; digital transformation of metrological services; and simulations and virtual measurement processes for the automatic assessment of measured data. Based on the assumption that the interplay between smart manufacturing and digitalization of metrology is an emerging research field, this paper aims to present a systematic literature review (SLR) based on a bibliographic data collection of 160 scientific articles retrieved from the Web of Science and Scopus databases over the 2016–2022 time frame. The findings presented in this review and recommendations for building a research agenda can help policy makers, researchers and practitioners by providing directions for the evolution of digital metrology and its role in the digitalization of the economy and society. Full article
(This article belongs to the Special Issue Smart Manufacturing: Advances and Challenges)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

24 pages, 9150 KiB  
Case Report
Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education
by Benjamin James Ralph, Marcel Sorger, Benjamin Schödinger, Hans-Jörg Schmölzer, Karin Hartl and Martin Stockinger
Sensors 2021, 21(9), 2944; https://doi.org/10.3390/s21092944 - 22 Apr 2021
Cited by 10 | Viewed by 3166
Abstract
Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the [...] Read more.
Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated. Full article
(This article belongs to the Special Issue Smart Manufacturing: Advances and Challenges)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop