Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/121398
Título: Production Process Modelling Architecture to Support Improved Cyber-Physical Production Systems
Autor: Seixas-Lopes, Fabio
Ferreira, José
Agostinho, Carlos
Jardim-Gonçalves, Ricardo
Palavras-chave: Artificial Intelligence
Cyber-physical systems
Internet of Things
Interoperability
Model driven architecture
Process modelling
Information Systems
Computer Networks and Communications
Information Systems and Management
Data: 2020
Editora: Springer
Citação: Seixas-Lopes, F., Ferreira, J., Agostinho, C., & Jardim-Gonçalves, R. (2020). Production Process Modelling Architecture to Support Improved Cyber-Physical Production Systems. In L. M. Camarinha-Matos, N. Farhadi, F. Lopes, & H. Pereira (Eds.), Technological Innovation for Life Improvement - 11th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020, Proceedings (pp. 206-213). (IFIP Advances in Information and Communication Technology; Vol. 577). Springer. https://doi.org/10.1007/978-3-030-45124-0_19
Resumo: With the proliferation of intelligent networks in industrial environments, manufacturing SME’s have been in a continuous search for integrating and retrofitting existing assets with modern technologies that could provide low-cost solutions for optimizations in their production processes. Their willingness to support a technological evolution is firmly based on the perception that, in the future, better tools will guarantee process control, surveillance and maintenance. For this to happen, the digitalization of valuable and extractable information must be held in a cost-effective manner, through contemporary approaches such as IoT, creating the required fluidity between hardware and software, for implementing Cyber-Physical modules in the manufacturing process. The goal of this work is to develop an architecture that will support companies to digitize their machines and processes through an MDA approach, by modeling their production processes and physical resources, and transforming into an implementation model, using contemporary CPS and IoT concepts, to be continuously improved using forecasting/predictive algorithms and analytics.
Peer review: yes
URI: http://hdl.handle.net/10362/121398
DOI: https://doi.org/10.1007/978-3-030-45124-0_19
ISBN: 978-3-030-45123-3
978-3-030-45124-0
ISSN: 1868-4238
Aparece nas colecções:Home collection (FCT)

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