Hybrid production-system control-architecture for smart manufacturing
| Autor: | Pasquale Merla, Antonio Giovannini, Michela Chimienti, Hervé Panetto, Michele Dassisti |
|---|---|
| Přispěvatelé: | Politecnico di Bari, InResLab scarl, Ali6 Srl, Centre de Recherche en Automatique de Nancy (CRAN), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Intelligent Manufacturing System
0209 industrial biotechnology 021103 operations research Supervisor business.industry Job shop Computer science Distributed computing 0211 other engineering and technologies Context (language use) 02 engineering and technology [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering Automation Cyber Physical Production System 020901 industrial engineering & automation Software Factory automation Production (economics) Reconfigurable Manufacturing System Architecture business Cyber physical production system - Factory automation - Hybrid control architecture - Intelligent manufacturing system - Reconfigurable manufacturing system Hybrid Control Archi- tecture |
| Zdroj: | On the Move to Meaningful Internet Systems. OTM 2017 Workshops 12th OTM/IFIP Enterprise Integration, Interoperability and Networking Workshop (EI2N) 12th OTM/IFIP Enterprise Integration, Interoperability and Networking Workshop (EI2N), Oct 2017, Kallithea, Rhodes, Greece. pp.5-15, ⟨10.1007/978-3-319-73805-5_1⟩ Lecture Notes in Computer Science ISBN: 9783319738048 OTM Workshops |
| DOI: | 10.1007/978-3-319-73805-5_1⟩ |
| Popis: | International audience; Highly customized products with shorter life cycles characterize the market today: the smart manufacturing paradigm can answer these needs. In this latter production system context, the interaction between production resources (PRs) can be swiftly adapted to meet both the variety of customers' needs and the optimization goals. In the scientific literature, several architectural configurations have been devised so far to this aim, namely: hierarchical, heterarchical or hybrid. Whether the hierarchical and heterarchical architectures provide respectively low reactivity and a reduced vision of the optimization opportunities at production system level, the hybrid architectures can mitigate the limit of both the previous architectures. However, no hybrid architecture can ensure all PRs are aware of how orienting their behavior to achieve the optimization goal of the manufacturing system with a minimal computational effort. In this paper, a new " hybrid architecture " is proposed to meet this goal. At each order entry, this architecture allows the PRs to be dynamically grouped. Each group has a supervisor , i.e. the optimizer, that has the responsibility: 1) to monitor the tasks on all the resources, 2) to compute the optimal manufacturing parameters and 3) to provide the optimization results to the resources of the group. A software prototype was developed to test the new architecture design in a simulated flow-shop and in a simplified job shop production. |
| Databáze: | OpenAIRE |
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