Enhancing Manufacturing Training Through VR Simulations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916867416784896 |
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| author | Li, Vladislav Siniosoglou, Ilias Sarigiannidis, Panagiotis Argyriou, Vasileios |
| author_facet | Li, Vladislav Siniosoglou, Ilias Sarigiannidis, Panagiotis Argyriou, Vasileios |
| contents | In contemporary training for industrial manufacturing, reconciling theoretical knowledge with practical experience continues to be a significant difficulty. As companies transition to more intricate and technology-oriented settings, conventional training methods frequently inadequately equip workers with essential practical skills while maintaining safety and efficiency. Virtual Reality has emerged as a transformational instrument to tackle this issue by providing immersive, interactive, and risk-free teaching experiences. Through the simulation of authentic industrial environments, virtual reality facilitates the acquisition of vital skills for trainees within a regulated and stimulating context, therefore mitigating the hazards linked to experiential learning in the workplace. This paper presents a sophisticated VR-based industrial training architecture aimed at improving learning efficacy via high-fidelity simulations, dynamic and context-sensitive scenarios, and adaptive feedback systems. The suggested system incorporates intuitive gesture-based controls, reducing the learning curve for users across all skill levels. A new scoring metric, namely, VR Training Scenario Score (VRTSS), is used to assess trainee performance dynamically, guaranteeing ongoing engagement and incentive. The experimental assessment of the system reveals promising outcomes, with significant enhancements in information retention, task execution precision, and overall training efficacy. The results highlight the capability of VR as a crucial instrument in industrial training, providing a scalable, interactive, and efficient substitute for conventional learning methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21070 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Manufacturing Training Through VR Simulations Li, Vladislav Siniosoglou, Ilias Sarigiannidis, Panagiotis Argyriou, Vasileios Human-Computer Interaction In contemporary training for industrial manufacturing, reconciling theoretical knowledge with practical experience continues to be a significant difficulty. As companies transition to more intricate and technology-oriented settings, conventional training methods frequently inadequately equip workers with essential practical skills while maintaining safety and efficiency. Virtual Reality has emerged as a transformational instrument to tackle this issue by providing immersive, interactive, and risk-free teaching experiences. Through the simulation of authentic industrial environments, virtual reality facilitates the acquisition of vital skills for trainees within a regulated and stimulating context, therefore mitigating the hazards linked to experiential learning in the workplace. This paper presents a sophisticated VR-based industrial training architecture aimed at improving learning efficacy via high-fidelity simulations, dynamic and context-sensitive scenarios, and adaptive feedback systems. The suggested system incorporates intuitive gesture-based controls, reducing the learning curve for users across all skill levels. A new scoring metric, namely, VR Training Scenario Score (VRTSS), is used to assess trainee performance dynamically, guaranteeing ongoing engagement and incentive. The experimental assessment of the system reveals promising outcomes, with significant enhancements in information retention, task execution precision, and overall training efficacy. The results highlight the capability of VR as a crucial instrument in industrial training, providing a scalable, interactive, and efficient substitute for conventional learning methods. |
| title | Enhancing Manufacturing Training Through VR Simulations |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.21070 |