Integrating the digital twin of a shop floor conveyor in the manufacturing control system

Abstract

The paper describes the architecture design and implementing solution for the digital twin of a shop floor transportation system embedded in the global manufacturing scheduling and control system. The products are assembled on pallets travelling on the conveyor between workstations, where assigned resources perform scheduled operations. The main functionalities of the digital twin are: mirroring the current stage of the physical pallet transportation process and the state of the physical conveyor components, predicting the values of the pallet’s transportation times along the conveyor’s segments between any two workstations, applying these values for enhanced reality-awareness of optimized product scheduling and resource allocation, and detecting anomalies in the behaviour of the conveyor equipment. Starting from a shortlist of generic scenarios, AI techniques are applied in the cloud layer of the virtual twin to optimally schedule products and early detect conveyor anomalies in the context of predictive maintenance.