A multi-agent and internet of things framework of digital twin for optimized manufacturing control

The demand for customized products is increasing, which brings great challenges to the traditional manufacturing system. The traditional manufacturing system lacks the analysis and feedback of the data collected in manufacturing, which hinders the rational utilization of the resources. In order to reasonably allocate resources according to the production requirements and effectively avoid the influence of external interference on the manufacturing system, a framework for the intelligent digital twin shopfloor is proposed in this paper. The framework consists of three primary components, namely physical shopfloor, virtual shopfloor, and digital twin service system. A model of digital twin shopfloor, a self-organizing model which achieves automation function in physical shopfloor, and an adaptive model which provides additional external disturbance resistance in virtual shopfloor are developed to improve the above-mentioned components. The mechanism of a contract network protocol and some cooperation strategies used in multi-agents are applied to achieve the capabilities above, which improve the reconfiguration and response ability of the shopfloor. A design case is studied to illustrate that the intelligent digital twin shopfloor can configure resources and deal with disturbances effectively.

Automated summary

The increasing demand for customized products poses challenges to traditional manufacturing systems. A framework for an intelligent digital twin shopfloor is proposed to improve resource allocation and handle external disturbances effectively. By developing components such as digital twin models, self-organizing models, and adaptive models, the shopfloor's reconfiguration and response abilities are enhanced.