A digital twin-enhanced system for engineering product family design and optimization

Kendrik Yan Hong Lim, Pai Zheng, Chun Hsien Chen, Lihui Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)

Abstract

Engineering product family design and optimization in complex environments has been a major bottleneck in today's industrial transformation towards smart manufacturing. Digital twin (DT), as a core part of cyber-physical system (CPS), can provide decision support to enhance engineering product lifecycle management workflows via remote monitoring and control, high-fidelity simulation, and solution generation functionalities. Although many studies have proven DT to be highly suited for industry needs, little has been reported on the product family design and optimization capabilities specifically with context awareness, which could be leaving many enterprises ambivalent on its adoption. To fill this gap, a reusable and transparent DT capable of situational recognition and self-correction is essentially required. This paper develops a generic DT architecture reference model to enable the context-aware product family design optimization process in a cost-effective manner. A case study featuring asset re-/configuration within a dynamic environment is further described to demonstrate its in-context decision-aiding capabilities. The authors hope this study can provide valuable insights to both academia and industry in improving their engineering product family management process.

Original languageEnglish
Pages (from-to)82-93
Number of pages12
JournalJournal of Manufacturing Systems
Volume57
DOIs
Publication statusPublished - Oct 2020

Keywords

  • Context awareness
  • Digital twin
  • Product configuration
  • Product family design
  • Product lifecycle management
  • Product optimization

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Hardware and Architecture
  • Industrial and Manufacturing Engineering

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