Systematic Selection of Visual and Cognitive Assistive Equipment: The Case of Gas Turbine Assembly in Narrow Spaces

As companies continue to optimize their processes, many are adopting digital technologies. In sectors like automotive manufacturing and equipment maintenance, technicians must handle large volumes of task-related information. To support them, assistive systems based on augmented or mixed reality have been developed to deliver the right information at the right time. This study proposes a prototype approach to identify the most suitable technology for providing visual and cognitive support in specific work situations. Grounded in design research methodology, the approach draws on criteria identified through systematic literature reviews. It incorporates multicriterion analysis, the taxonomy of assistance systems, the Analytic Hierarchy Process (AHP), and a use-value study to compare several head-mounted display alternatives. Empirical validation was conducted through a case study in an industrial gas turbine assembly. The method stands out by integrating industrial constraints, such as narrow spaces and operator mobility, into the decision-making process. Both technical (processor, autonomy, connectivity) and ergonomic (field of view, weight, interactions) criteria were considered. In the case studied, Magic Leap 2 emerged as the most appropriate device. The findings underscore the value of a structured approach for selecting assistive technologies in complex environments and highlight the importance of combining expert evaluation with context-specific constraints. However, the study also emphasizes the need for further testing with end users to fully assess the usability and acceptance of the chosen solution.