A Conceptual Framework of Adaptive AR-Based Digital Guidance System for High-Mix Low-Volume Manual Assembly

Zhi Lon Gan, S. Nurmaya Musa, Hwa Jen Yap, Chao Liu, Yuchun Xu

Research output: Chapter in Book/Published conference outputConference publication

Abstract

In a high-mix, low-volume (HMLV) manufacturing environment, operators had long relied on paper instructions to identify product variations, which have cognitive disadvantages that digital assembly guidance and assistance systems addressed. Together with AR technology, the digitalised systems are used in guiding, training, designing and planning manual assembly. However, their implementation faces challenges arising from the high variation of products and the authoring expertise required. As easy tasks have negligible benefit under AR guidance, it is proposed that a multi-mode adaptive guidance system use AR guidance only at necessary assembly steps. This paper proposes an Adaptive Digital Guidance System for HMLV manufacturing, which includes a multi-mode assembly assist system for varying tasks complexity. The assembly information is shown adaptively based on the type of assembly process, user experience level, and task complexity. The assembly information manager application is proposed to track product variation and complexity in order to recommend appropriate instructional visual assets. The operator’s experience profile will group similar product variations while tracking the user’s improvement in task performance for each variant. Finally, all of these systems will be integrated into a proposed framework to create an adaptive digital guidance system.
Original languageEnglish
Title of host publication2022 27th International Conference on Automation and Computing (ICAC)
PublisherIEEE
Number of pages5
ISBN (Electronic)978-1-6654-9807-4
ISBN (Print)978-1-6654-9808-1
DOIs
Publication statusPublished - 10 Oct 2022
Event2022 27th International Conference on Automation and Computing (ICAC) - Bristol, United Kingdom
Duration: 1 Sep 20223 Sep 2022

Conference

Conference2022 27th International Conference on Automation and Computing (ICAC)
Period1/09/223/09/22

Bibliographical note

Funding Information:
UNIVERSITI MALAYA Impact-Oriented Interdisciplinary Research Grant Programme (IIRG008B-19IISS)

Funding Information:
ACKNOWLEDGMENT This work is funded by the Impact-Oriented Interdisciplinary Research Grant Programme from the University of Malaya (Grant No. IIRG008B-19IISS). The authors would also like to thank European Union Erasmus+ grant (Erasmus+ 2019-1-UK01-KA107-061113) for the collaboration opportunities between the two universities.

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Training
  • Visualization
  • Adaptive systems
  • Navigation
  • Manuals
  • User experience
  • Complexity theory

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