A Novel Bionic Digital Twin-Based Manufacturing System Toward the Mass Customization Paradigm

Shimin Liu, Pai Zheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Introducing the digital twin into the manufacturing system has formed an intelligent manufacturing system with paired physical entities and digital models, which can monitor and control the entire manufacturing process. With the acceleration of Internet-driven product research and development, the manufacturing demand for mass customization products is shifting towards high accuracy, high complexity, and multiple varieties. The dynamic characteristics of manufacturing processes are not considered, and the uncertainty of manufacturing processes has not yet been discussed. The lack of the above research points has seriously affected the robustness of digital twin-based manufacturing systems, making it difficult to adapt to these growing demands for small-batch customization. In this paper, a novel digital twin-based manufacturing system is proposed, which endows the digital twin with bionic characteristics to improve the system's adaptability. During the manufacturing process, the system adaptively changes to meet the changing manufacturing requirements. In the context of dynamically changing production requirements, the mutual transformation between knowledge and algorithms is required to achieve self-evolution of system performance. It is believed that introducing the bionic concept can enable digital twin-based manufacturing systems to adapt to mass customization paradigms quickly, ensuring product quality and manufacturing efficiency.

Original languageEnglish
Title of host publication2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PublisherIEEE Computer Society
Pages6
ISBN (Electronic)9798350320695
DOIs
Publication statusPublished - Aug 2023
Event19th IEEE International Conference on Automation Science and Engineering, CASE 2023 - Auckland, New Zealand
Duration: 26 Aug 202330 Aug 2023

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2023-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Country/TerritoryNew Zealand
CityAuckland
Period26/08/2330/08/23

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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