Abstract
AbstractTo cope with individualization and the high costs of downtimes, modern production systems should be flexible, adaptable, and resilient. Multi-Agent Systems are suitable to address these requirements by decentralizing production systems. However, the agent paradigm is still not widely applied. One of the key reasons is that the agents’ knowledge bases had to be created manually, which is cumbersome, error-prone, and insufficiently standardized. Digital Twins have the potential to solve this issue, as they describe relevant information in a standardized way. This paper presents an approach to leveraging Digital Twins, i. e., the Asset Administration Shell, to realize Multi-Agent Systems in the production context. For this, a parser automatically extracts relevant information from the Digital Twins and initializes the individual agents in a Multi-Agent System, i. e., PADE.
Highlights
For evaluating the framework developed, we ran a quantitative performance assessment and assessed it qualitatively regarding the Cyber-Physical Production Systems (CPPSs) requirements defined by Ribeiro and Hochwallner [38]
We presented an overview of information required by the different agents and showed that it can be represented using the Administration Shell (AAS) metamodel (RQ1)
Using Digital Twins (DTs) with this information as an input, we showed how a MultiAgent Systems (MASs) can be created and how the individual Resource Agents (RAs) and Product Agent (PA) can be automatically initialized from their respective DTs (RQ2)
Summary
Modern manufacturing systems face significant challenges resulting from globalized and customer-driven markets as well as an ongoing acceleration of technological development. Shorter product life cycles and customization are essential trends that make increased flexibility and manufacturing concepts necessary [1] To cope with these challenges, production systems have to evolve into smart factories. The intelligent agents require information about their environment, their abilities, their needs, and their goals. It has to be analyzed which information is required by the different agents and what can and cannot be provided by DTs (RQ1). It needs to be researched how a MAS can be created from these DTs (RQ2).
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