The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
A framework for data-driven digital twins of smart manufacturing systems
Open accessAbstract
Adoption of digital twins in smart factories, that model real statuses of manufacturing systems through simulation with real time actualization, are manifested in the form of increased productivity, as well as reduction in costs and energy consumption. The sharp increase in changing customer demands has resulted in factories transitioning rapidly and yielding shorter product life cycles. Traditional modeling and simulation approaches are not suited to handle such scenarios. As a possible solution, we propose a generic data-driven framework for automated generation of simulation models as basis for digital twins for smart factories. The novelty of our proposed framework is in the data-driven approach that exploits advancements in machine learning and process mining techniques, as well as continuous model improvement and validation. The goal of the framework is to minimize and fully define, or even eliminate, the need for expert knowledge in the extraction of the corresponding simulation models. We illustrate our framework through a case study.
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Cites
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“This enables the digitally extracted simulation model to be a high-fidelity replica of the real entity that it is modeling in real-time (Kritzinger et al., 2018), (Tao et al., 2019).”
BackgroundInfluentialDigital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920
Workflow mining: discovering process models from event logs
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Cited by
Hybrid learning-based digital twin for manufacturing process: modeling framework and implementation
Huang, Z., Fey, M., Liu, C., Beysel, E., Xu, X., & Brecher, C. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102545 · Cited by 117
Literature review of digital twin technologies for civil infrastructure
Liu, C., Zhang, P., & Xu, X. (2023) · Journal of Infrastructure Intelligence and Resilience · doi:10.1016/j.iintel.2023.100050 · Cited by 98
Civil Infrastructure Digital Twins: Multi-Level Knowledge Map, Research Gaps, and Future Directions
Naderi, H., & Shojaei, A. (2022) · IEEE Access · doi:10.1109/access.2022.3223557 · Cited by 31
Metadata
- Authors
- Friederich, J., Francis, D. P., Lazarova-Molnar, S., & Mohamed, N.
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 136
- Pages
- 103586
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W4200612054
- Cited by
- 323
- References
- 53
- FWCI
- 20.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Friederich, J., Francis, D. P., Lazarova-Molnar, S., & Mohamed, N. (2022). A framework for data-driven digital twins of smart manufacturing systems. Computers in Industry. https://doi.org/10.1016/j.compind.2021.103586
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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