Digital Twins Applications in Manufacturing Optimization

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.

• digital twins
• smart manufacturing
• predictive maintenance
• modeling and simulation
• IoT edge architecture
• machine learning