Special Issue "Industrial Informatics and Digital Twin"
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 6551
Interests: robotics and mechatronics; high-performance parallel robotic machine development; sustainable/green manufacturing systems; micro/nanomanipulation and MEMS devices (sensors); micro mobile robots and control of multi-robot cooperation; intelligent servo control system for the MEMS-based high-performance micro-robot; web-based remote manipulation; rehabilitation robot and rescue robot
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Interests: Industry 4.0; intelligent manufacturing system; artificial intelligence
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The essence of the digital twin is virtual–real fusion, which uses sensors, computational models, and industrial data to provide predictions for the current and future states of physical systems. The expected benefit of adopting this high-fidelity approach is to ultimately reduce the uncertainty in the performance of the physical automated system in use.
To capture and comprehend the full complexity of automated systems, the fusion of multi-physics, multi-scale, multi-stage industrial data is required in the digital twin, and frequent and regular (even real-time) updates of the previous predictions through the acquired data are also essential. However, huge challenges still lie in handling the high data variety, complexity, and timeliness embedded in the controlling and decision-making in automated systems.
Industrial information technology, such as AR/VR, cloud computing, deep learning, and knowledge graph, is changing dramatically and has shown promising prospects for managing massive industrial data, establishing digital twin models, and enabling smart services for automated systems. To this end, this Special Issue solicits articles relating to the automation area for digital twins, and concentrates on digital twin models, digital twin informatics, and digital twin behavior in automatic systems to develop research on self-decision making, self-adaptation, and automatic evolution of automatic systems. Topics of interest include but are not limited to the following areas:
- Methodologies for digital twins of automation system
System architectures for digital twins;
Representation and modelling for digital twins.
- Industrial informatics for digital twins
Industrial knowledge graph of digital twins;
Graph neural networks for industrial knowledge graph;
Verification techniques for digital twins.
- Digital Twin-Driven Approaches for automation system
Integrating digital twins with existing industrial approaches such as Industry 4.0;
Augmented reality and virtual reality;
Informatics-based and digital twin-enabled industrial services.
Prof. Dr. Dan Zhang
Prof. Dr. Jinsong Bao
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.
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