Augmented reality-enabled knowledge management in industrial maintenance: the DILEAF framework
File(s)
Published version
Author(s)
Mao, Wanting
Scheffer, Sara
Majumdar, Arnab
Type
Journal Article
Abstract
Effective maintenance in industrial operations relies on efficient management of task-critical knowledge, particularly in dynamic and unpredictable environments. Traditional knowledge management (KM) approaches face challenges in handling fragmented data, delivering procedural guidance, and adapting to evolving operational demands. To address these limitations, this study introduces the Data, Information, Learning, Engagement, Application, Feedback (DILEAF) framework, a structured KM model that integrates augmented reality (AR) as a digital enabler for improving knowledge capture, transfer, and application in industrial maintenance. By leveraging AR’s capabilities in real-time visualisation, interactive procedural guidance, and dynamic feedback, the DILEAF framework enhances user engagement and operational adaptability. The effectiveness of the framework is validated through a case study within a rolling stock organisation, where iterative experiments in both laboratory and field environments demonstrated improvements in task accuracy, real-time decision-making, and system adaptability. It was shown that AR overlays play a crucial role in enabling early error detection and correction, directly supporting the overall task success rate. Furthermore, 91 % of participants in the case study expressed satisfaction with the clarity and usefulness of the information presented via AR, underscoring the framework’s effectiveness in delivering task-relevant knowledge and supporting robust performance in maintenance scenarios. These findings illustrate that the DILEAF framework provides a system-informed and operationally structured approach to industrial KM, bridging theoretical KM principles with practical AR-driven implementations. This study establishes a scalable and adaptable foundation for integrating AR into industrial workflows, contributing to the advancement of digitalised maintenance strategies in industrial engineering.
Date Issued
2025-10-01
Date Acceptance
2025-07-01
Citation
Computers and Industrial Engineering, 2025, 208
ISSN
0360-8352
Publisher
Elsevier
Journal / Book Title
Computers and Industrial Engineering
Volume
208
Copyright Statement
© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Identifier
10.1016/j.cie.2025.111363
Publication Status
Published
Article Number
111363
Date Publish Online
2025-07-02