Engineering a Digital Twin for Manual Assembling
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 12479 LNCS, Page: 140-152
2021
- 7Citations
- 12Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
The paper synthesizes our preliminary work on developing a digital twin, with learning capabilities, for a system that includes cyber, physical, and social components. The system is an industrial workstation for manual assembly tasks that uses several machine learning models implemented as microservices in a hybrid architecture, a combination between the orchestrated and the event stream approaches. These models have either similar objectives but context-dependent performance, or matching functionalities when the results are fused to support real-life decisions. Some of the models are descriptive but easy to transform in inductive models with extra tuning effort, while others are purely inductive, requiring intrinsic connection with the real world.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85115846476&origin=inward; http://dx.doi.org/10.1007/978-3-030-83723-5_10; https://link.springer.com/10.1007/978-3-030-83723-5_10; https://link.springer.com/content/pdf/10.1007/978-3-030-83723-5_10; https://dx.doi.org/10.1007/978-3-030-83723-5_10; https://link.springer.com/chapter/10.1007/978-3-030-83723-5_10
Springer Science and Business Media LLC
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