The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing
Abstract
Digital Twin (DT) implementations can contribute to smart manufacturing by integrating the physical and the cyber space. Artificial Intelligence (AI) applications based on Machine Learning (ML) are widely accepted as promising technologies in manufacturing. However, ML methods require large volumes of quality training datasets and in the case of supervised ML, manual input is usually required for labelling those datasets. Such an approach is expensive, prone to errors and labour as well as time-intensive, especially in a highly complex and dynamic production environment. DT models can be utilized for accelerating the training phase in ML by creating suitable training datasets as well as by automatic labelling via the simulation tools chain and thus alleviating user’s involvement during training. These synthetic datasets can be enhanced and cross-validated with real-world information which is not required to be extensive. A framework for implementing the proposed DT-driven approach for developing ML models is presented. The proposed framework has been implemented in an industrially relevant use case. The use case has provided evidence that the proposed concept can be used for training vision-based recognition of parts’ orientation using simulation of DT models, which in turn can be used for adaptively controlling the production process.
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Cites
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Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
Domain randomization for transferring deep neural networks from simulation to the real world
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“…presented a system for training deep neural networks for object detection using synthetic images based on the technique of domain randomization (Tobin et al. 2017), in which the parameters of the simulator – such as lighting, pose, object textures, etc. – are randomized in non-realistic ways…”
MethodDeep learning for smart manufacturing: Methods and applications
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About The Importance of Autonomy and Digital Twins for the Future of Manufacturing
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“Rosen et al. (2015) argue how DT implementations could increase the autonomy of manufacturing systems by combining the real data with simulation models from design, thus allowing the DT to predict future states based on the realistic data.”
MethodDigital twin shop-floor: a new shop-floor paradigm towards smart manufacturing
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Cited by
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects
Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R. V., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208773 · Cited by 1,049
A survey on AI-driven digital twins in Industry 4.0: smart manufacturing and advanced robotics
Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021) · Sensors · doi:10.3390/s21196340 · Cited by 418
Li, X., Liu, H., Wang, W., Zheng, Y., Lv, H., & Lv, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.10.006 · Cited by 398
A framework for data-driven digital twins of smart manufacturing systems
Friederich, J., Francis, D. P., Lazarova-Molnar, S., & Mohamed, N. (2022) · Computers in Industry · doi:10.1016/j.compind.2021.103586 · Cited by 323
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304
Metadata
- Authors
- Alexopoulos, K., Nikolakis, N., & Chryssolouris, G.
- Venue
- International Journal of Computer Integrated Manufacturing
- Publisher
- Informa UK Limited
- Published
- 2020-04-08
- Volume
- 33
- Issue
- 5
- Pages
- 429-439
- ISSN
- 0951-192X (ISSN-L)
- 1362-3052
- OpenAlex
- W3015487859
- Cited by
- 309
- References
- 24
- FWCI
- 24.93
- Type
- journal article
- Language
- en
- Open access
- closed
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