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Machines
Special Issues
State-of-the-Art in Digital Manufacturing Systems
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State-of-the-Art in Digital Manufacturing Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 34842

Special Issue Editors

Prof. Dr. Richard Hill

E-Mail Website
Guest Editor
Department of Computer Science, Centre for Industrial Analytics (CIndA), School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Interests: cloud computing; IIoT; digital manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Hussain

E-Mail Website
Guest Editor
School of Computing and Engineering, University of Huddersfield, West Yorkshire, UK
Interests: cloud computing; IIoT; digital manufacturing
Dr. Saqib Ali

E-Mail Website
Guest Editor
Faculty of Sciences, Department of Computer Science, University of Agriculture, Faisalabad, Pakistan
Interests: Blockchain/DLT; smart agriculture; Industry 4.0
Dr. Jamal Shah

E-Mail Website
Guest Editor
Department of Computer Science, Wah Campus, COMSATS University Islamabad, Wah Cantonment 47040, Pakistan
Interests: computer vision; machine learning; signal processing

Special Issue Information

Dear Colleagues,

Manufacturing is being transformed by the introduction of accessible, inexpensive digital technologies such as wireless sensors, single-board computers, pervasive broadband networking and access to massive computing power through cloud computing.

Innovations in the application of machine learning to data-rich processes have led to increased adoption of computer vision to enable non-invasive monitoring and control of complex systems. When such a capability can process images for fault detection in real-time, new capabilities become possible, especially when connected to other facilities through high-speed communications networks.

The combination of low-cost hardware, intelligent software systems and almost ubiquitous access to facilities anywhere in the globe, enables the smart orchestration of resources, where devices and the manufacturing plant can be scheduled, coordinated, commissioned, controlled and optimized digitally.

This Special Issue will provide a forum for researchers and practitioners to exchange their latest research and practical experiences and also to identify critical issues and challenges for future studies in this rapidly developing area of digital manufacturing systems. Results of experimental research in field conditions are encouraged, as are evaluations of practical case studies, and state-of-the-art reviews. Conceptual and experimental articles accepted into this Special Issue are expected to contain original ideas and potential solutions available for resolving real problems.

Topics include, but are not limited to, the following domains:

  • Digital Twins
  • Non-invasive sensing
  • Cloud computing for manufacturing
  • Automated fault detection
  • Machine vision
  • Smart object detection for industrial safety
  • Robotics and collaborative robots (‘cobots’)
  • Machine Learning/CNN models and workflows
  • Dynamic resource scheduling, allocation and optimization
  • Smart maintenance and condition monitoring
  • Manufacturing data visualization
  • In-transit analytics
  • Retrofitting plant with smart technologies
  • Field Programmable Gate Arrays for smart system integration
  • Operational Technology
  • Cyber Physical Systems for smart manufacture

Prof. Dr. Richard Hill
Dr. Muhammad Hussain
Dr. Saqib Ali
Dr. Jamal Shah
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. Machines is an international peer-reviewed open access monthly 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 2400 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

  • digital manufacturing
  • IIoT
  • computer vision
  • edge computing
  • automation
  • Industry 4.0
  • smart manufacturing
  • wireless sensor networks
  • CNN
  • machine learning
  • GAN
  • automated defect detection
  • quality assurance
  • SCADA
  • operational technology
  • smart commissioning
  • digital twin
  • non-invasive sensing
  • in-transit analytics
  • resource off-loading
  • resource scheduling
  • cobots
  • robotics
  • Cyber Physical Systems
  • finite state automata
  • formal methods
  • model checking
  • verification
  • smart maintenance
  • model-based engineering
  • Distributed Ledger Technologies

Published Papers (2 papers)

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Research

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18 pages, 3839 KiB  
Article
Analyzing Consultancy on Production Systems Based on the Digital Triplet Concept
by Takaomi Sato, Hiroki Takeuchi, Shinsuke Kondoh and Yasushi Umeda
Machines 2023, 11(7), 706; https://doi.org/10.3390/machines11070706 - 03 Jul 2023
Cited by 1 | Viewed by 1038
Abstract
This study aims to analyze the process flow of skilled consultants who utilize production improvement know-how and digital technology to enhance production systems in external companies. The concept of a Digital Triplet (D3), which expands the authors’ Digital Twin framework to include the [...] Read more.
This study aims to analyze the process flow of skilled consultants who utilize production improvement know-how and digital technology to enhance production systems in external companies. The concept of a Digital Triplet (D3), which expands the authors’ Digital Twin framework to include the intelligent activity world, is adopted as it aligns with this study’s objective. Given the complexity of the problems faced by production system consulting and the resulting inadequacy of reusing decision-making processes of skilled engineers based on the Generalized Process Model (GPM) using D3, a production system consulting modeling method is proposed. This method incorporates the Generalized Production System Consulting Process Model (GCPM) to generalize the production system consulting process. Using the proposed method, a case study focusing on energy-saving improvements was conducted to describe and analyze the consulting process of skilled consultants. The results show that the proposed method effectively captures the process flow of skilled consultants while considering the iterative structure of the GCPM. Additionally, utilizing the GCPM enables a comprehensive view of the entire process, facilitating an understanding of how knowledge and tools are utilized in various contexts. Full article
(This article belongs to the Special Issue State-of-the-Art in Digital Manufacturing Systems)
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Review

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25 pages, 5908 KiB  
Review
YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection
by Muhammad Hussain
Machines 2023, 11(7), 677; https://doi.org/10.3390/machines11070677 - 23 Jun 2023
Cited by 71 | Viewed by 33155
Abstract
Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are underpinned by the principle of real-time and high-classification performance, based on limited but [...] Read more.
Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are underpinned by the principle of real-time and high-classification performance, based on limited but efficient computational parameters. This principle has been found within the DNA of all YOLO variants with increasing intensity, as the variants evolve addressing the requirements of automated quality inspection within the industrial surface defect detection domain, such as the need for fast detection, high accuracy, and deployment onto constrained edge devices. This paper is the first to provide an in-depth review of the YOLO evolution from the original YOLO to the recent release (YOLO-v8) from the perspective of industrial manufacturing. The review explores the key architectural advancements proposed at each iteration, followed by examples of industrial deployment for surface defect detection endorsing its compatibility with industrial requirements. Full article
(This article belongs to the Special Issue State-of-the-Art in Digital Manufacturing Systems)
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