Development of an Immersive, Digital Twin-Supported Smart Reconfigurable Educational Platform for Manufacturing Training: A Proof of Concept
The challenge of providing students with practical, hands-on experience in realistic industrial environments is increasingly prevalent in modern technical education.The concept of a Learning Factory addresses this issue by facilitating skill acquisition through immersive, practice-oriented training that integrates advanced digital technologies.An innovative educational platform has been developed, incorporating Internet of Things (IoT) devices, Cyber-Physical Systems (CPS), and Digital Twin (DT) technology to enhance manufacturing education.This platform combines modular hardware and software, enabling immersive visualisation and real-time monitoring through DT-supported systems.These features offer a comprehensive, interactive learning experience that closely simulates real-world manufacturing processes.The system's smart reconfigurability further enhances its educational potential by enabling customisable training scenarios tailored to specific learning outcomes.The proposed approach aligns with the principles of Industry 4.0 and serves as a catalyst for the improvement of both educational and professional training environments.By leveraging digitalisation, this platform not only supports adaptive learning but also enhances the efficiency of educational models.Through the simulation of dynamic manufacturing systems, students are exposed to a variety of industrial scenarios, fostering deeper understanding and skill development.The integration of IoT, CPS, and DT technologies is expected to provide a scalable framework for future educational environments, ultimately improving the adaptability and effectiveness of manufacturing training.
Authors
- Szabolcs Fischer (ORCID: https://orcid.org/0000-0001-7298-9960)
- Gergő Dávid Monek (ORCID: https://orcid.org/0000-0003-2086-6875)
- Norbert Szántó
Publication Details
- Journal
- Journal of Engineering Management and Systems Engineering
- Published
- 2024-12-03
- DOI
- https://doi.org/10.56578/jemse030402
- Citations
- 2
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- Széchenyi István Egyetem
- Nemzeti Kutatási, Fejlesztési és Innovaciós Alap