Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
3.3
Journals
JMMP
Special Issues
Digital Twinning for Manufacturing
share announcement

Digital Twinning for Manufacturing

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 16933

Special Issue Editor

Dr. Ali Abdelhafeez Hassan

E-Mail Website
Guest Editor
Institute of Engineering, Computing and Advanced Manufacturing, University of Cumbria, Carlisle, UK
Interests: manufacturing; machining; material integrity; simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue focusing on digital twinning for manufacturing. This Special Issue aims to explore the latest advancements, applications, and innovations in digital twin technology and modeling within the realm of advanced manufacturing processes and systems.

Scope and Topics:

Digital twin technology has emerged as a transformative approach in the manufacturing sector, enabling the real-time monitoring, simulation, and optimization of manufacturing processes and systems. This Special Issue seeks to gather high-quality research papers that address, but are not limited to, the following topics:

  • Development and implementation of digital twin simulation models for manufacturing processes and for manufacturing systems and production lines;
  • Integration of digital twins with IoT, AI, and big data analytics;
  • Case studies and applications of digital twins in smart factories;
  • Predictive maintenance and real-time monitoring using digital twins;
  • Enhancing product lifecycle management through digital twin technology;
  • Challenges and future directions in digital twinning for manufacturing.

Submission Guidelines:

Authors are invited to submit original research articles, review papers, and case studies. All submissions will undergo a rigorous peer-review process to ensure the highest quality of published content. Manuscripts should be prepared according to the journal’s guidelines and submitted through the online submission system.

Dr. Ali Abdelhafeez Hassan
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 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 250 words) can be sent to the Editorial Office for assessment.

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. Journal of Manufacturing and Materials Processing 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 1800 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

  • advanced manufacturing
  • digital twin technology
  • smart factories
  • manufacturing processes and systems

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 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

31 pages, 5554 KB  
Article
Process–Design Co-Optimisation of Laser Powder Bed Fusion Titanium Gyroid Lattices via Deep Learning
by Alexander Dawes, Ali Abdelhafeez Hassan, Hany Hassanin and Khamis Essa
J. Manuf. Mater. Process. 2026, 10(3), 92; https://doi.org/10.3390/jmmp10030092 - 9 Mar 2026
Viewed by 898
Abstract
Laser powder bed fusion (LPBF) enables controlled gyroid lattices, but mapping both process and design to performance remains challenging when datasets are small and interactions are non-linear. In this study, data-driven models that link energy density and lattice geometry to Young’s modulus and [...] Read more.
Laser powder bed fusion (LPBF) enables controlled gyroid lattices, but mapping both process and design to performance remains challenging when datasets are small and interactions are non-linear. In this study, data-driven models that link energy density and lattice geometry to Young’s modulus and yield strength were established for sheet and network gyroid architectures. To stabilise small-data learning, stacked-autoencoder pre-training was benchmarked against greedy layer-wise pre-training. Compression characterisation data at under-represented energy-density conditions were added to fill data gaps and validate predictions. The models support property-driven design in which given modulus and yield strength targets inform a method that returns feasible combinations of laser powder bed fusion settings and gyroid density and size. Pre-trained models reduced error and captured the relationship between stiffness and density and between strength and density, with yield strength prediction errors of 3.51% for sheet architectures and 8.76% for network architectures. Young’s modulus showed a higher variability that is consistent with sensitivities in LPBF such as surface roughness and thin walls. This work contributes an artificial intelligence method for manufacturing datasets using stacked autoencoder pre-training with fine-tuning, and an inverse-design workflow that maps energy density and gyroid geometry to Young’s modulus and yield strength in titanium lattices. Full article
(This article belongs to the Special Issue Digital Twinning for Manufacturing)
►▼ Show Figures

Graphical abstract

23 pages, 6136 KB  
Article
A Bidirectional Digital Twin System for Adaptive Manufacturing
by Klaas Maximilian Heide, Berend Denkena and Martin Winkler
J. Manuf. Mater. Process. 2025, 9(12), 400; https://doi.org/10.3390/jmmp9120400 - 4 Dec 2025
Cited by 1 | Viewed by 1778
Abstract
Digital Twin Systems (DTSs) are increasingly recognized as enablers of data-driven manufacturing, yet many implementations remain limited to monitoring or visualization without closed-loop control. This study presents a fully integrated DTS for CNC milling that emphasizes real-time bidirectional coupling between a real machine [...] Read more.
Digital Twin Systems (DTSs) are increasingly recognized as enablers of data-driven manufacturing, yet many implementations remain limited to monitoring or visualization without closed-loop control. This study presents a fully integrated DTS for CNC milling that emphasizes real-time bidirectional coupling between a real machine and a virtual counterpart as well as the use of machine-native signals. The architecture comprises a physical space defined by a five-axis machining center, a virtual space implemented via a dexel-based technological simulation environment, and a digital thread for continuous data exchange between those. A full-factorial simulation study investigated the influence of dexel density and cycle time on engagement accuracy and runtime, yielding an optimal configuration that minimizes discretization errors while maintaining real-time feasibility. Latency measurements confirmed a mean response time of 34.2 ms, supporting process-parallel decision-making. Two application scenarios in orthopedic implant milling validated the DTS: process force monitoring enabled an automatic machine halt within 28 ms of anomaly detection, while adaptive feed rate control reduced predicted form error by 20 µm. These findings demonstrate that the DTS extends beyond passive monitoring by actively intervening in machining processes; enhancing process reliability and part quality; and establishing a foundation for scalable, interpretable digital twins in regulated manufacturing. Full article
(This article belongs to the Special Issue Digital Twinning for Manufacturing)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research

29 pages, 5293 KB  
Review
Review of Applications of Digital Twins and Industry 4.0 for Machining
by Leonardo Rosa Ribeiro da Silva, Danil Yurievich Pimenov, Rosemar Batista da Silva, Ali Ercetin and Khaled Giasin
J. Manuf. Mater. Process. 2025, 9(7), 211; https://doi.org/10.3390/jmmp9070211 - 24 Jun 2025
Cited by 11 | Viewed by 12947
Abstract
Digital twins, as part of Industry 4.0, are critical for advanced smart manufacturing processes, including machining. Sensor systems in smart manufacturing allow for real-time tracking of all changes in the machining process as well as simulation of an object’s behavior in the real [...] Read more.
Digital twins, as part of Industry 4.0, are critical for advanced smart manufacturing processes, including machining. Sensor systems in smart manufacturing allow for real-time tracking of all changes in the machining process as well as simulation of an object’s behavior in the real world. It can also intervene and correct any defects that may arise during the machining process. The current review covers basic concepts for machining processes for the first time in detail, including Big Data, the Internet of Things, product lifecycle management, continuous acquisition and lifecycle support, machine learning, digital twin prototypes, digital twin instances, digital twin aggregates, and digital twin environments. The review article examines digital twins for the most common machining processes, such as turning, milling, drilling, and grinding. This review also highlights the benefits and drawbacks, as well as the prospects for using digital twins in smart manufacturing. Full article
(This article belongs to the Special Issue Digital Twinning for Manufacturing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop