TY - GEN
T1 - Enhancing manufacturing training through VR simulations
AU - Li, Vladislav
AU - Siniosoglou, Ilias
AU - Sarigiannidis, Panagiotis
AU - Argyriou, Vasileios
PY - 2025/8/13
Y1 - 2025/8/13
N2 - 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.
AB - 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.
KW - Health and Safety Education
KW - Immersive Learning
KW - Industrial Manufacturing
KW - Interactive Simulations
KW - Task Performance
KW - Training Effectiveness
KW - User Engagement
KW - Virtual Reality Training
U2 - 10.1109/ICE/ITMC65658.2025.11106519
DO - 10.1109/ICE/ITMC65658.2025.11106519
M3 - Conference contribution
AN - SCOPUS:105015468228
T3 - Proceedings of the 31st ICE IEEE/ITMC Conference on Engineering, Technology, and Innovation: AI-Driven Industrial Transformation: Digital Leadership in Technology, Engineering, Innovation and Entrepreneurship, ICE 2025
BT - Proceedings of the 31st ICE IEEE/ITMC Conference on Engineering, Technology, and Innovation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st ICE IEEE/ITMC Conference on Engineering, Technology, and Innovation, ICE 2025
Y2 - 16 June 2025 through 19 June 2025
ER -