Data-Driven Digital Twin for Smart Manufacturing and Industry 4.0
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 30 December 2025 | Viewed by 5
Special Issue Editors
Interests: maintenance, development and implementation of information systems and decision support
Special Issues, Collections and Topics in MDPI journals
Interests: HC: applied statistics; design and analysis of experiments; production process optimization; TO: logistics; manufacturing and logistics process simulation; process planning; human factors and ergonomics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The convergence of industrial engineering, data science, and cyber–physical systems has enabled the rise of digital twins as transformative assets in Smart Manufacturing and Industry 4.0. This Special Issue focuses on the development and deployment of data-driven digital twins—dynamic, data-connected virtual counterparts of physical systems—designed to support decision-making, system optimization, and lifecycle management across industrial operations.
Digital twins offer tools for industrial engineers to enhance productivity, quality, and sustainability through real-time analytics, simulation, and control. Integration with data collected from Industrial IoT devices, edge computing platforms, and cloud infrastructures opens new horizons for process optimization, intelligent automation, and condition-based maintenance.
This Special Issue invites contributions that explore novel methods, architectures, and applications of digital twins, particularly emphasizing approaches based on data and their role in evolving manufacturing systems. Research involving predictive modeling, maintenance strategies, decision support tools, and AI-driven optimization is especially welcome.
Topics of interest include, but are not limited to, the following:
- Data architectures for digital twins in manufacturing;
- Integration with Industrial IoT and edge/cloud systems;
- Predictive analytics and health monitoring;
- Applications in production logistics, asset management, and quality control.
Dr. Davor Kolar
Dr. Tihomir Opetuk
Guest Editors
Manuscript Submission Information
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Keywords
- digital twin
- smart manufacturing
- Industry 4.0
- industrial engineering
- data-driven modelling
- predictive maintenance
- cyber–physical systems
- industrial IoT
- process optimization
- condition monitoring
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