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A novel human-centric framework for maintenance digitisation using augmented reality

Lorenzo Valentini et al.

International Journal of Agile Systems and Management2025https://doi.org/10.1504/ijasm.2025.145449article
AJG 1
Weight
0.41

What the paper says

Maintenance operators usually face complex challenges due to the complexity of modern sophisticated machinery and systems. Only highly experienced operators may have sufficient skills to perform recovery procedures properly and quickly; frequently, not expert operators need for assistance from the manufacturer's after-sales. Therefore, very long recovery times, high maintenance costs, and temporary drop in production take place. In this context, augmented reality (AR) can offer valid support to non-expert operators involved in maintenance activities. In this work, a framework based on user experience (UX) design approach was developed for designing highly usable AR applications to guide operators in performing maintenance procedures safely and efficiently. The proposed framework has been applied to the H2020 European project titled "XMANAI - Explainable Manufacturing Artificial Intelligence". Results showed that the application developed is suitable to assist not expert operators, preventing them from making mistakes, leading to the upskill and increased productivity for the company.

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https://doi.org/https://doi.org/10.1504/ijasm.2025.145449

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@article{lorenzo2025,
  title        = {{A novel human-centric framework for maintenance digitisation using augmented reality}},
  author       = {Lorenzo Valentini et al.},
  journal      = {International Journal of Agile Systems and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijasm.2025.145449},
}

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A novel human-centric framework for maintenance digitisation using augmented reality

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.