Skip to main navigation Skip to search Skip to main content

An hourglass-shaped digital twin framework for industrial computed tomography

  • Zhiyu Gao
  • , Haibin Lan
  • , Changsheng Zhang
  • , Qianni Wang
  • , Shengping Yuan
  • , Wenlei Xiao*
  • , Gang Zhao
  • , Jian Fu*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Industrial computed tomography (ICT) is one of the most advanced non-destructive testing, evaluation and characterization techniques, used for revealing internal structures, analyzing material compositions, and identifying defects within objects. However, in practical engineering applications, it has encountered challenges such as complex operation procedures and parameter settings, high costs, as well as artifacts and measurement errors. Digital twin (DT), by creating a virtual environment that closely mirrors and interacts with the physical space, offers an innovative solution to these challenges. In this paper, an hourglass-shaped DT framework for ICT is presented. By establishing both static and dynamic connections between the physical and virtual spaces, the ICT system is twinned across three dimensions: entities, behaviors, and data. This integration enables the applications and functions layer to improve the efficiency and accuracy of the ICT process. The implementation of the DT framework within the ICT system is explained in detail, and a case study of a workpiece’s ICT inspection process is used to validate its feasibility and effectiveness. This work has been helpful in advancing the digitization of the industrial inspection field under Industry 4.0.

Original languageEnglish
Article number104251
JournalAdvanced Engineering Informatics
Volume71
DOIs
StatePublished - Apr 2026

Keywords

  • Digital twin
  • Industrial computed tomography
  • Industrial inspection
  • Process optimization
  • Virtual reality

Fingerprint

Dive into the research topics of 'An hourglass-shaped digital twin framework for industrial computed tomography'. Together they form a unique fingerprint.

Cite this