Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect

TLDR

Digital twins typically rely on an information model to describe physical machines, and their integration into cyber‑physical cloud manufacturing systems demands reduced overhead and resource savings. The study aims to develop and evaluate a cloud‑based digital twin (CBDT) method that can be adapted to the CPCM platform. The approach introduces a knowledge resource center hosted on a cloud server, where an information model for a specific 3D printer type is integrated as a shared resource to support information‑intensive applications. Experiments demonstrate that the CBDT reduces computing resources in the information processing center and outperforms existing methods in terms of performance.

Abstract

The common modeling of digital twins uses an information model to describe the physical machines. The integration of digital twins into productive cyber-physical cloud manufacturing (CPCM) systems imposes strong demands such as reducing overhead and saving resources. In this paper, we develop and investigate a new method for building cloud-based digital twins (CBDT), which can be adapted to the CPCM platform. Our method helps reduce computing resources in the information processing center for efficient interactions between human users and physical machines. We introduce a knowledge resource center (KRC) built on a cloud server for information intensive applications. An information model for one type of 3D printers is designed and integrated into the core of the KRC as a shared resource. Several experiments are conducted and the results show that the CBDT has an excellent performance compared to existing methods.