From data to actionable knowledge: AI-AR integration framework for industrial knowledge management
File(s)
Published version
Author(s)
Scheffer, Sara
Mao, Wanting
Majumdar, Arnab
Type
Journal Article
Abstract
Industrial knowledge management (KM) remains highly fragmented, with knowledge capture, structuring, and application often treated as isolated activities. Conventional approaches depend on static documentation and experience-based practices, which limit their responsiveness in dynamic, operational environments. In this paper, we design a comprehensive framework for the integration of artificial intelligence (AI) and augmented reality (AR) in industrial maintenance and diagnostics. The framework leverages AI techniques, including natural language processing (NLP) for extracting and structuring domain knowledge and machine learning (ML) models for predictive fault classification. These AI capabilities are seamlessly combined with AR technologies to deliver immersive, context-aware, in-situ task guidance, thereby enhancing decision-making, reducing downtime, and supporting efficient, knowledge-based maintenance processes. A case study is employed to demonstrate the framework’s feasibility by developing a prototype system deployed in a maintenance setting. The AI module clusters maintenance actions and predicts task categories, while the AR component renders step-by-step maintenance instructions anchored in the physical workspace. This integration enables the transition from unstructured, digital maintenance records to step-by-step in-situ repair guidance, reducing reliance on expert memory or static manuals. The results show that combining AI-driven text analytics with AR-based visualisation creates a cohesive knowledge workflow that improves operational efficiency. The proposed framework offers a scalable approach to embedding KM into frontline industrial routines, laying the foundation for more adaptive, technician-centred knowledge systems.
Date Issued
2026-04-01
Date Acceptance
2026-02-25
Citation
Applied Intelligence, 2026, 56 (5)
ISSN
0924-669X
Publisher
Springer Science and Business Media LLC
Journal / Book Title
Applied Intelligence
Volume
56
Issue
5
Copyright Statement
© The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Publication Status
Published
Article Number
144
Date Publish Online
2026-03-10