Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.0
2.1
Journals
Machines
Special Issues
Digital Twins Applications in Manufacturing Optimization
share announcement

Digital Twins Applications in Manufacturing Optimization

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 3289

Special Issue Editors

Dr. Lelio Campanile

E-Mail Website
Guest Editor
Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
Interests: digital twins; AI; computer networks; machine learning; modeling and simulation; NLP; WSN; IoT
Dr. Laura Verde

E-Mail Website
Guest Editor
Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
Interests: signal processing; artificial intelligence; predictive maintenance; digital twins
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Marrone

E-Mail Website
Guest Editor
Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
Interests: model-driven engineering; software testing; critical system design and assurance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, Digital Twin (DT) technology has emerged as a transformative force, reshaping the landscape of manufacturing optimization across various industries. A DT is a virtual representation of a physical system, process, or product that enables real-time monitoring, analysis, and decision making. This innovative technology has opened new horizons for predictive maintenance, Machine Learning (ML) and Deep Learning (DL) applications, modeling and simulation techniques, reference architectures, big data-driven strategies, and the integration of IoT and edge architectures in the smart manufacturing sector.

This special issue aims to explore the dynamic evolution of DT applications in the context of manufacturing optimization and beyond. We seek to provide a comprehensive understanding of how DTs are revolutionizing industries such as the healthcare industry, the rail industry, aerospace, and beyond. Moreover, we will delve into the intricacies of ML and DL techniques, modeling and simulation advances, reference architectures for optimizing manufacturing processes, the role of big data in predictive maintenance, and the synergy between IoT and edge architectures in the era of Industry 4.0.

Contributions to this special issue will shed light on the latest breakthroughs and best practices, enabling researchers, engineers, and practitioners to harness the full potential of digital twins in revolutionizing the manufacturing domain. We encourage submissions that demonstrate innovative solutions and real-world case studies that showcase the transformative power of digital twins in manufacturing optimization.

Main Topics:

We invite researchers, experts, and practitioners to submit original research papers, review articles, and case studies that align with the following topics associated with the use of digital twin technology for manufacturing optimization:

  • Digital Twins for Predictive Maintenance: Exploring how digital twins are impacting predictive maintenance practices in industries such as the healthcare industry, the rail industry, aerospace, and more.
  • Leveraging and Optimizing Machine Learning and Deep Learning: Investigating the utilization of Machine Learning and Deep Learning algorithms to develop, enhance, and employ digital twins in manufacturing systems.
  • Innovative Modeling and Simulation Techniques: Showcasing innovative modeling and simulation techniques that support the development and application of digital twins in manufacturing.
  • Reference Architectures for Manufacturing Optimization: Presenting best practices and reference architectures for optimizing manufacturing systems and processes using digital twins.
  • Model-Driven and Data Driven Predictive Maintenance: Delving into the integration of data analytics into predictive maintenance strategies within digital twin-enabled manufacturing processes.
  • IoT and Edge Architectures in Digital Twin Optimization: Exploring the integration and effectiveness of IoT and edge architectures in harnessing the power of digital twins for manufacturing optimization.
  • Case study for design optimization using the DT approach: Investigating DT applications in the smart manufacturing sector.

Dr. Lelio Campanile
Dr. Laura Verde
Dr. Stefano Marrone
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 twins
  • smart manufacturing
  • predictive maintenance
  • modeling and simulation
  • IoT edge architecture
  • machine learning

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 policies can be found here.

Published Papers (2 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

28 pages, 1894 KiB  
Article
A Framework for Integrating Vision Transformers with Digital Twins in Industry 5.0 Context
by Attila Kovari
Machines 2025, 13(1), 36; https://doi.org/10.3390/machines13010036 - 7 Jan 2025
Cited by 1 | Viewed by 1207
Abstract
The transition from Industry 4.0 to Industry 5.0 gives more prominence to human-centered and sustainable manufacturing practices. This paper proposes a conceptual design framework based on Vision Transformers (ViTs) and digital twins, to meet the demands of Industry 5.0. ViTs, known for their [...] Read more.
The transition from Industry 4.0 to Industry 5.0 gives more prominence to human-centered and sustainable manufacturing practices. This paper proposes a conceptual design framework based on Vision Transformers (ViTs) and digital twins, to meet the demands of Industry 5.0. ViTs, known for their advanced visual data analysis capabilities, complement the simulation and optimization capabilities of digital twins, which in turn can enhance predictive maintenance, quality control, and human–machine symbiosis. The applied framework is capable of analyzing multidimensional data, integrating operational and visual streams for real-time tracking and application in decision making. Its main characteristics are anomaly detection, predictive analytics, and adaptive optimization, which are in line with the objectives of Industry 5.0 for sustainability, resilience, and personalization. Use cases, including predictive maintenance and quality control, demonstrate higher efficiency, waste reduction, and reliable operator interaction. In this work, the emergent role of ViTs and digital twins in the development of intelligent, dynamic, and human-centric industrial ecosystems is discussed. Full article
(This article belongs to the Special Issue Digital Twins Applications in Manufacturing Optimization)
►▼ Show Figures

Figure 1

23 pages, 4742 KiB  
Article
A Study on Service-Oriented Digital Twin Modeling Methods for Weaving Workshops
by Bo Yu, Liaoliao Fang, Laibing Luo, Xudong Hu and Chunya Shen
Machines 2024, 12(8), 542; https://doi.org/10.3390/machines12080542 - 7 Aug 2024
Cited by 1 | Viewed by 975
Abstract
With the rapid development of intelligent manufacturing, Digital Twin technology, as an advanced tool for the intelligentization of weaving workshops, has endowed weaving services with real-time simulation and dynamic optimization capabilities while also placing higher demands on the digital capabilities of workshops. The [...] Read more.
With the rapid development of intelligent manufacturing, Digital Twin technology, as an advanced tool for the intelligentization of weaving workshops, has endowed weaving services with real-time simulation and dynamic optimization capabilities while also placing higher demands on the digital capabilities of workshops. The diverse and multi-manufacturer equipment in weaving workshops exacerbates the complexity of multi-source heterogeneous data. Moreover, traditional data collection methods, which are mostly based on fixed frequencies, increase the network load during real-time high-frequency data reception, making stable, long-term operation difficult. Conversely, low-frequency collection might miss important state changes, thus affecting the quality of weaving big data. To address these issues, this paper proposes a service-oriented Digital Twin modeling method for weaving workshops. This method combines OPC Unified Architecture (OPC UA) with a state change-based data collection approach, utilizing a sliding time window (STW) to identify anomalous data and employing median interpolation to correct these anomalies. The goal is to enhance the representation capability of the Digital Twin in the weaving workshop by improving the data quality. For a specific service of predicting the warp-out time of 288 air-jet looms in a workshop, the average error of the predicted warp-out time using the dynamic data set proposed in this study was reduced from 0.85 h to 0.78 h compared to the static data set based on fixed frequency, an improvement of 8.2%, thereby validating the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Digital Twins Applications in Manufacturing Optimization)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop