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Erschienen in:
Assembly Line Worker Assignment and Balancing Problem with Positional Constraints Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

When Softbots Meet Digital Twins: Towards Supporting the Cognitive Operator 4.0

verfasst von : Ricardo J. Rabelo, David Romero, Saulo P. Zambiasi, Luciano C. Magalhães

Erschienen in: Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

Verlag: Springer International Publishing

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Abstract

Digital Twins (DT) represent one of the most powerful technologies related to Industry 4.0 implementation. Combined with the advances in Computer Science and Production Management, DTs have become an emerging trend in many applications. Despite the DT potentials, Industry 4.0 is pushing companies to adopt digital solutions that provide higher human satisfaction to increase operational excellence. One approach to handle this involves how workers should interact with systems and smart machines. Softbots is a prominent direction to underpin this goal. This paper presents ongoing results of a research that exploits how softbots can be combined with DTs to create a more “symbiotic” human-machine/computer interaction within a smarter decision-making environment.

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Vorheriges Kapitel Applying Machine Learning for Adaptive Scheduling and Execution of Material Handling in Smart Production Logistics
Nächstes Kapitel The Implementation of Eulerian Coverings of a Graph for Solving Routing Problems
Fußnoten
3
ARISA NEST platform for softbots derivation – https://​arisa.​com.​br/​ [in Portuguese].
 
Literatur
1.
Mittal, S., Khan, M.A., Romero, D., Wuest, T.: Smart manufacturing: characteristics, technologies and enabling factors. J. Eng. Manuf. 233(5), 1342–1361 (2017)CrossRef
2.
Barricelli, B.R., Casiraghi, E., Fogli, D.: A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access 7, 167653–167671 (2019)CrossRef
3.
Romero, D., Vernadat, F.: Enterprise information systems state of the art: past, present and future trends. Comput. Ind. 79, 3–13 (2016)CrossRef
4.
5.
Abner, B., Rabelo, R.J., Zambiasi, S.P., Romero, D.: Production management as-a-service: a softbot approach. In: Lalic, B., Majstorovic, V., Marjanovic, U., von Cieminski, G., Romero, D. (eds.) Advances in Production Management Systems. Towards Smart and Digital Manufacturing, APMS 2020. IFIP Advances in Information and Communication Technology, vol. 592. Springer, Cham. https://​doi.​org/​10.​1007/​978-3-030-57997-5_​3
6.
7.
Jones, D., Snider, C., Nassehi, A., Yon, J., Hicks, B.: Characterising the digital twin: a systematic literature review. CIRP J. Manuf. Sci. Technol. 29(1), 36–52 (2020)CrossRef
8.
9.
Lua, Y., Liub, C., Xu, X.: Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot. Comput. Integr. Manuf. 61, 101837–101852 (2020)CrossRef
10.
Rabelo, R.J., Magalhaes, L.C., Cabral, F.G.: A proposal of a reference architecture for digital twins in industry 4.0 (in Portuguese). In: Proceedings of the XXIII Brazilian Congress of Automation (2020)
11.
Rooein, D., Bianchini, D., Leotta, F., Mecella, M., Paolini, P., Pernici, B.: Chatting about processes in digital factories: a model-based approach. In: Nurcan, S., Reinhartz-Berger, I., Soffer, P., Zdravkovic, J. (eds.) BPMDS/EMMSAD-2020. LNBIP, vol. 387, pp. 70–84. Springer, Cham (2020). https://​doi.​org/​10.​1007/​978-3-030-49418-6_​5CrossRef
12.
Frazzon, E.M., Agostino, I.S., Broda, E., Freitag, M.: Manufacturing networks in the era of digital production and operations: a socio-cyber-physical perspective. Annu. Rev. Control. 49, 288–294 (2020)CrossRef
13.
Romero, D., Stahre, J., Wuest, T., et al.: Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. In: International Conference on Computers & Industrial Engineering (CIE46), Tianjin, China, pp. 1–11 (2016)
14.
15.
16.
Somers, S., Oltramari, A., Lebiere, C.: Cognitive twin: a cognitive approach to personalized assistants. In: AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering (2020)
17.
Rosen, R., Wichert, G., Lo, G., Bettenhausen, K.: About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48(3), 567–572 (2015)CrossRef
18.
Rabelo, R., Zambiasi, S.P., Romero, D., Abner, B.: Softbots 4.0: excelling smart production management. Int. J. Comput. Integr. Manuf. (to appear)
19.
20.
Thorvald, P., Fast-Berglund, Å., Romero, D.: The cognitive operator 4.0. In: Proceedings of the 18th International Conference on Manufacturing Research. IOS Press (2021)
21.
Kritzinger, W., Karner, M., Traar, G.: Digital twin in manufacturing: a categorical literature review and classification. IFAC PapersOnLine 15(11), 1016–1022 (2018)CrossRef
22.
Zhong, R.Y., Xu, X., Klotz, E.: Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, 3(5):616–630 (2017)
23.
Kim, J.H.: Ubiquitous robot. In: Reusch B. (eds.) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol. 33, pp. 451–459 (2005)
24.
Papcun, P., Kajáti, E., Koziorek, J.: Human machine interface in concept of Industry 4.0. In: World Symposium on Digital Intelligence for Systems and Machines, pp. 289–296 (2018)
25.
26.
Rasheed, A., San, O., Kvamsdal, T.: Digital twin – values, challenges and enablers from a modeling perspective. IEEE Access 8, 21980–22012 (2020)CrossRef
27.
28.
Guo, J.: Digital twins are shaping future virtual worlds. SOCA 15(2), 93–95 (2021)CrossRef
29.
Boschert, S., et al.: Symbiotic Autonomous Systems. IEEE Digital Reality (2019)
30.
Bolton, R., McColl-Kennedy, J., et al.: Customer experience challenges: bringing together digital, physical and social realms. J. Serv. Manag. 29(5), 776–808 (2018)CrossRef
31.
Parri, J., Patara, F., Sampietro, S.: A framework for model-driven engineering of resilient software-controlled systems. Computing 103, 589–612 (2021)MathSciNetCrossRef
32.
Wellsandta, S., Foosheriana, M., Thoben, K-D.: Interacting with a digital twin using Amazon Alexa. In: 5th International Conference on System-Integrated Intelligence, pp. 4–8 (2020)
Metadaten
Titel
When Softbots Meet Digital Twins: Towards Supporting the Cognitive Operator 4.0
verfasst von
Ricardo J. Rabelo
David Romero
Saulo P. Zambiasi
Luciano C. Magalhães
Copyright-Jahr
2021
Buch
Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

Print ISBN: 978-3-030-85913-8

Electronic ISBN: 978-3-030-85914-5

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-85914-5_5

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