Unveiling the Potential of Mixed Reality: Enhancing Time Measurement and Operator Support in Manual Assembly Processes
2024 (English)In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 232, p. 2670-2679Article in journal (Refereed) Published
Abstract [en]
This study investigates the potential of Mixed Reality (MR) in the manual assembly processes and conducts a case study at a pump manufacturing plant in Sweden. An MR solution is developed to assist operators through visual instructions and guiding aides. The solution also captures the operator's motions using advanced hand and eye tracking features for real-time guidance and accurate time measurement. The proposed MR solution uses the build feature of HoLolens and a workstation editor, which facilitates the use of the solution in diverse assembly environments. The results of the experiments show that the developed MR solution can improve operator support, reduce errors, and enhance the overall efficiency of manual assembly processes. Moreover, it is shown to be an efficient tool for time measurement of the manual assembly process that has promising potential to replace sophisticated and time-consuming traditional time study methods.
Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 232, p. 2670-2679
Keywords [en]
Manual Assembly, Mixed Reality, Operator Support, Time study
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD)
Identifiers
URN: urn:nbn:se:his:diva-23728DOI: 10.1016/j.procs.2024.02.084ISI: 001196800602069Scopus ID: 2-s2.0-85189829918OAI: oai:DiVA.org:his-23728DiVA, id: diva2:1852605
Conference
5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023 Lisbon 22 November 2023 through 24 November 2023
Projects
ACCURATE 4.0
Funder
Knowledge Foundation, 20200181
Note
CC BY-NC-ND 4.0
© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
Correspondence Address: M. Fathi; Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, Skövde, 54128, Sweden; email: masood.fathi@his.se
The authors would like to thank the Knowledge Foundation (KKS), Sweden, for their financial support through the ACCURATE 4.0 project, under grant agreement No. 20200181. We also wish to extend our appreciation to the industrial partner of the project, Xylem Water Solutions Sweden AB. Their collaboration, expertise, and invaluable insights have significantly contributed to this study.
2024-04-182024-04-182024-08-15Bibliographically approved