Advanced Digital Twin in Smart Manufacturing
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 27
Special Issue Editor
Interests: numerical simulation; thermomechanic; digital twin; manufacturing; neural networks; additive manufacturing; severe plastic deformation; friction welding
Special Issue Information
Dear Colleagues,
The advancements brought about by artificial intelligence tools and technological improvements in production unit monitoring equipment have led to the emergence of digital twin, defined as a virtual replica of a physical entity, with both connected bidirectionally for automatic data and instruction exchange. The primary advantage of digital twin lies in its ability to assist decision-making or even make decisions autonomously, thanks to the real-time analysis and processing of data from both physical and virtual sensors. In the context of manufacturing processes, it enables the anticipation of defects and anomalies in production and, in some cases, their real-time correction. This offers a significant advantage for complex manufacturing processes, where multiple sensitive parameters can be adjusted in real time, enhancing overall process efficiency.
Therefore, the development of digital twins for manufacturing processes relies on four essential components: (I) real-time controllable manufacturing machines; (II) physical sensors capable of delivering in situ, real-time measurements; (III) real-time process simulators that provide virtual data inaccessible or costly to obtain with physical instrumentation; and (IV) a decision-making model that integrates expert knowledge from fields such as metallurgy, tribology, or process engineering with artificial intelligence to support or automate process parameter adjustments.
This Special Issue encourages researchers to present their recent works in the field of digital twins applied to smart manufacturing, exploring the technical challenges and opportunities they offer in complex process handling. These contributions are organized around four key themes:
- Real-time process simulation for implementing virtual sensors.
- Process instrumentation for real-time measurement.
- Real-time analysis and processing of large volumes of data.
- Expert knowledge modeling (metallurgists, tribologists, process engineers) and AI for decision support or autonomous decision-making.
Dr. Amevi Tongne
Guest Editor
Manuscript Submission Information
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Keywords
- digital twin
- manufacturing
- machine learning
- AI
- real-time computation
- real-time data monitoring and processing
- real time
- real-time defect detection and correction
- expert knowledge
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