Abstract
The process of training a manufacturing operator is usually long and complex, involving time, resources, and expert trainers. This paper proposes a new approach to train novice workers using wearable technologies. The solution is formed by hardware elements (finger-tracking gloves), and a software platform which records the performance of an expert manufacturing operator, and where a novice operator can learn and self-compare with the expert. This new solution 1) does not require the continuous presence of a trainer, 2) makes the factory autonomous to generate its own learning content, 3) allows a quantifiable and objective readiness measure of the novice operator, and overall 4) means a complementary and more effective and faster learning method. The solution has been validated as a proof of concept at the Stellantis Vigo factory in Spain, with positive reviews from their workers. This new approach can be applicable in many fields, especially when dealing with tasks requiring high manual dexterity.
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Acknowledgements
The authors want to acknowledge the contribution of the project “Facendo 4.0" and respectively to the agency GAIN from the Xunta de Galicia regional government of Spain, for its funding.
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Dacal-Nieto, A., Raña, B., Moreno-Rodríguez, J., Areal, J.J., Alonso-Ramos, V. (2022). Self-training of Manufacturing Operators Using Finger-Tracking Wearable Technologies. In: Guarda, T., Portela, F., Augusto, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2022. Communications in Computer and Information Science, vol 1675. Springer, Cham. https://doi.org/10.1007/978-3-031-20319-0_1
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DOI: https://doi.org/10.1007/978-3-031-20319-0_1
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