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

Liwen Hu, Ngoc Tu Nguyen, Wenjin Tao, Ming C. Leu, Xiaoqing Frank Liu, Md Rakib Shahriar, S. M.Nahian Al Sunny

Research output: Contribution to journalConference articlepeer-review

29 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1193-1203
Number of pages11
JournalProcedia Manufacturing
Volume26
DOIs
StatePublished - 2018
Event46th SME North American Manufacturing Research Conference, NAMRC 2018 - College Station, United States
Duration: Jun 18 2018Jun 22 2018

Bibliographical note

Funding Information:
This project is supported by NSF Grant CMMI 1551448 entitled “EAGER/Cybermanufacturing: Architecture and Protocols for Scalable Cyber-Physical Manufacturing Systems”.

Publisher Copyright:
© 2018 The Author(s).

Keywords

  • Cloud Manufacturing
  • Digital Twin
  • MT Connect
  • Smart Manufacturing

Fingerprint

Dive into the research topics of 'Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect'. Together they form a unique fingerprint.

Cite this