The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor

TLDR

Digital simulation tools can improve production systems in a time‑ and cost‑effective manner, and enriching them with sensor data enhances realism and accuracy. The study proposes implementing a digital twin within a cyber‑physical system to optimize planning and commissioning of human‑based production processes using simulation. This is achieved through sensor‑data fusion and motion recognition of human activities on the factory floor, coupled with a knowledge‑management mechanism that captures implicit task‑execution knowledge. A case study in intra‑factory logistics of the white‑goods industry demonstrates the feasibility of the approach by enriching manual‑assembly simulations with operator‑derived spatiotemporal constraints.

Abstract

Production systems empowered by digital simulation tools can be improved in a time and cost-effective approach. The enrichment of digital simulations with sensor data, can enhance their realism and improve the accuracy of their results. Hence, this study proposes an implementation of the digital twin approach as part of a wider cyber-physical system (CPS) to enable the optimisation of the planning and commissioning of human-based production processes using simulation-based approaches. This is achieved through a) sensor data fusion and motion recognition of human activities in a factory floor and b) knowledge management mechanism for capturing the implicit knowledge of the task execution. A case study from the intra-factory logistics operations in the white goods industry, demonstrates the feasibility of the proposed approach, by enriching the simulation of manual assembly operations with the operator's knowledge in the form of spatiotemporal constraints.