Citation Link: https://doi.org/10.25819/ubsi/11009
Human Assistance System Supporting Forming Machine Reconfiguration
Publication Type
InProceedings
Institute
Subjects
Wearable Systems
Digital Manufacturing
Augmented Guidance
Workforce Training
Operator 5.0
DDC
620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
GHBS-Clases
Source
Engel, Bernd (Hrsg.): Datengestützte Umformtechnik: Künstliche Intelligenz zwischen Prozesswissen, Regelung und Produktionsrealität : Tagungsband zum 7. Biegeforum Siegen : 05.-10. März 2026. Siegen: universi - Universitätsverlag Siegen, 2026. - ISBN 978-3-96182-237-9, DOI https://doi.org/10.25819/ubsi/10982 (S. 177-189)
Date Issued
2026
Abstract
Increased product complexity in present manufacturing sector, shorter product lifecycles, and more variable customer demands, require production systems to be flexible and reliable. As a result, machines are required to be reconfigured more frequently and within shorter timeframes. Reconfiguring forming machines is a complex and delicate task that depends strongly on operator experience and practical know-how gained over several years. Appropriate support system is required to transfer the accumulated experience and knowledge in order to enable novice operators to perform such tasks effectively. This work presents an assistance system designed to support novice operators during forming machine reconfiguration tasks. The proposed system comprises an eye-gaze sensor to observe operator actions and visual attention during task execution. Based on this information, the system identifies potential mistakes and provides real-time feedback on a smartwatch to guide the operator through the reconfiguration process. The approach is evaluated in a controlled laboratory environment, where both task performance and user experience are assessed. The results show potential for the use of human assistance systems to support novice operators in real time during forming machine reconfiguration on the shop floor. In addition to improving the overall system and its evaluation in a real production environment under industrial conditions, future work will focus on multimodal sensor fusion to enable more robust intent recognition and contextual understanding of an operator during reconfiguration of forming machines.
Subjects
Wearable Systems
Digital Manufacturing
Augmented Guidance
Workforce Training
Operator 5.0
License
Creative Commons license
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