Licentiate thesis: XR Enabled Operator Training

This thesis explores how Extended Reality (XR) can address the growing need for workforce training in manufacturing by providing scalable, immersive learning solutions, while proposing guidelines and automated development methods to overcome current implementation barriers.

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Author: Henrik Söderlund

Examiner: Johan Stahre

Supervisor: Björn Johansson

Co-supervisor: Mélanie Despeisse

Opponent: Lennart Malmsköld

Year: 2025

The manufacturing industry is undergoing rapid transformation due to geopolitical shifts, climate goals, and demographic changes, driving a growing demand for skilled labour. In Sweden alone, it is estimated that 300,000 manufacturing workers will need training in the coming three years. Traditional training approaches struggle to effectively integrate practical and theoretical learning, highlighting the need for innovative, scalable, and immersive training solutions to meet future workforce demands.

Advancements in Extended Reality (XR) technology have paved the way for an alternative to traditional training, offering the potential of safe, efficient and scalable training with a high degree of realism and practical learning. Despite recent technological advances and reduced hardware costs, few large-scale industrial implementations of XR trainings have been observed, and 75% of all XR training projects fail to move beyond the prototype stage.

This thesis aims to lower the barrier to implementing XR trainings in manufacturing industry by addressing two main identified challenges. (1). Lacking design guidelines for how XR trainings in manufacturing should be developed and used from a knowledge perspective. (2) Resource intense development process of XR training content in manufacturing. Two case studies and one systematic literature review was deployed as part of the research to identify and theorize over the stated challenges.

The literature of XR training showed that the design of the XR training environment is heavily dependent on the applied learning style, and the manufacturing use case. A mapping of the applied learning styles and manufacturing use case are provided, giving a first indication of how design guidelines of XR training in manufacturing could be drawn from a knowledge perspective.

Addressing the second identified challenge, a method towards automated XR training development utilizing Product Lifecycle Management (PLM) data structure is presented. The highlighted method shows high potential for drastically reducing the time needed for XR training development.

Finally, this thesis contributes by introducing an initial framework of design and development guidelines based on the manufacturing use case and learning objective. The presented framework that is expected to address both of the stated challenges, lowering the barrier of implementation of XR training in manufacturing industry.

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