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A Hybrid Peer-to-Peer Architecture for Agent-Based Steel Manufacturing Processes
- Source :
- IFAC-PapersOnLine. 54:528-533
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- The new generation of steel manufacturing processes shaped by Industry 4.0 are more digitalized, networked, flexible and adaptable. Production processes use distributed information and communication structures, are more autonomous and capable to react to dynamic changings of the environment. Agent-based systems represent a paradigm, which is well suited to address these new generation of smart processes. The paper presents a hybrid peer-to-peer architecture for agent-based steel production processes. The architecture exploits a central database server for storing and retrieving updated information from peers about a cold rolling manufacturing process. The cold rolling process is modeled as a multi-agent system composed of four types of autonomous agents, each playing a different role in the steel production chain. Agents are designed to take autonomous decisions, and to coordinate and collaborate with each other, by ensuring the dynamic plant resources allocation even if unforeseen interruptions of the production flow may happen. The proposed approach is designed for the steel strip manufacturing process but can be easily readapted to any flat production process. The test of the design multi-agent system with the proposed architecture is supported though the simulation of the dynamic plant resources allocation under changing dynamic conditions.
- Subjects :
- Exploit
Computer science
Process (engineering)
Agents
Applications using mediators
Engineering applications of artificial intelligence
Factory of the future
Hybrid peer-to-peer
Industry 4.0
Multi-agent simulation
Multi-agent systems
Steel industry
Distributed computing
Autonomous agent
ComputerApplications_COMPUTERSINOTHERSYSTEMS
Peer to peer architecture
Control and Systems Engineering
Steel mill
Production (economics)
Architecture
Central database
Subjects
Details
- ISSN :
- 24058963
- Volume :
- 54
- Database :
- OpenAIRE
- Journal :
- IFAC-PapersOnLine
- Accession number :
- edsair.doi.dedup.....c23c0a8043ac847c50b59b3be7474ad0