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I4.0/5.0 MR training: Investigating MR tools to enhance learning experiences

As the manufacturing industry continues its shift towards highly complex Industry 4.0 production environments, there is an expected exponential increase and change in the demanded skills and qualifications among employees. However, traditional teaching methods may pose challenges when it comes to ap...

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Bibliographic Details
Published in:MATEC web of conferences 2024-01, Vol.401, p.07006
Main Authors: Bondin Andrea, Zammit Joseph Paul
Format: Article
Language:English
Online Access:Get full text
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Summary:As the manufacturing industry continues its shift towards highly complex Industry 4.0 production environments, there is an expected exponential increase and change in the demanded skills and qualifications among employees. However, traditional teaching methods may pose challenges when it comes to applying learned skills in real-life engineering situations, given the complexity of these environments. Recent advancements in technologies enabling virtual co-existence have opened up new opportunities for personalised and immersive services in pedagogy. While Mixed Reality (MR) and, more significantly, Metaverse infrastructure are still in their early stages, researchers and educators have the opportunity to lead the exploration of new avenues for reskilling educators and enhancing student learning experiences. This paper presents research conducted at the University of Malta, focusing on exploring the potential transformative pedagogical effects of MR in specialised Industry 4.0/5.0 engineering training. The paper proposes a framework for developing a Virtual Learning Factory (VLF) using MR technology, grounded in established and effective learning methodologies. The envisioned VLF aims to create an immersive experiential learning environment where engineering students can better adapt to the evolving industrial landscape, preparing them to excel in the dynamic era of advanced manufacturing. Additionally, the research delves into the potential impacts of MR-based training on enhancing training precision and efficiency.
ISSN:2261-236X
DOI:10.1051/matecconf/202440107006