RetrofittAR: Supporting Hardware-Centered Expertise Sharing in Manufacturing Settings through Augmented Reality

Since almost the onset of computer-supported cooperative work (CSCW), the community has been concerned with how expertise sharing can be supported in different settings. Here, the complex handling of machines based on experience and knowledge is increasingly becoming a challenge. In our study, we in...

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Bibliographic Details
Published in:Computer supported cooperative work 2023-03, Vol.32 (1), p.93-139
Main Authors: Hoffmann, Sven, Ludwig, Thomas, Jasche, Florian, Wulf, Volker, Randall, David
Format: Article
Language:English
Subjects:
Augmented reality
CAI
Computer assisted instruction
Computer Science
Computer supported cooperative work
Cooperative work
Hardware
Manufacturing
Psychology
Research Article
Retrofitting
Social Sciences
User Interfaces and Human Computer Interaction
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Summary:Since almost the onset of computer-supported cooperative work (CSCW), the community has been concerned with how expertise sharing can be supported in different settings. Here, the complex handling of machines based on experience and knowledge is increasingly becoming a challenge. In our study, we investigated expertise sharing in a medium-sized manufacturing company in an effort to support the fostering of hardware-based expertise sharing by using augmented reality (AR) to ‘retrofit’ machines. We, therefore, conducted a preliminary empirical study to understand how expertise is shared in practice and what current support is available. Based on the findings, we derived design challenges and implications for the design of AR systems in manufacturing settings. The main challenges, we found, had to do with existing socio-technical infrastructure and the contextual nature of expertise. We implemented a HoloLens application called RetrofittAR that supports learning on the production machine during actual use. We evaluated the system during the company’s actual production process. The results show which data types are necessary to support expertise sharing and how our design supports the retrofitting of old machines. We contribute to the current state of research in two ways. First, we present the knowledge-intensive practice of operating older production machines through novel AR interfaces. Second, we outline how retrofitting measures with new visualisation technologies can support knowledge-intensive production processes.
ISSN:0925-9724
1573-7551
DOI:10.1007/s10606-022-09430-x