Abstract
The rapid evolution of manufacturing technologies and the demand for a skilled workforce highlight the need for innovative training methods. Traditional approaches often lack flexibility for virtual training, especially during disruptions like pandemics, and do not effectively integrate sensor-based tracking for studying problem-solving. To address these gaps, this study developed a collaborative virtual learning factory to advance manufacturing education and analyze both human–human teaming and human–machine interaction. The virtual factory simulates the physical assembly of toy cars in a virtual reality environment, reflecting manufacturing paradigms such as craft production, mass production, mass customization, and personalized production. It offers an immersive, interactive space where learners gain hands-on experience, collaborate, and build problem-solving skills. Collaborative features include a voice system for communication, team-based performance requirements, and interdependencies along the production line, fostering teamwork and communication. While the broader goal of the virtual learning factory is to support modern manufacturing training, this paper focuses specifically on evaluating usability, workload, and team collaboration as critical foundations for educational impact. A study involving 30 participants engaged them in assembly tasks within the virtual factory, while their interactions and collaboration were recorded. Data collected included physiological signals (e.g., heart rate, electrodermal activity) and performance measures such as perceived workload and system usability. Analysis revealed that higher physiological synchrony among group members was linked to better task performance, underscoring the importance of physiological alignment in team effectiveness. Additionally, a significant negative correlation (r = –.60, p < 0.05) was found between workload and system usability. Overall, this study demonstrates the potential of virtual learning factories to improve user experience by enhancing system usability and reducing cognitive workload, offering promising avenues for modern manufacturing training.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 128-132 |
| Number of pages | 5 |
| Journal | Manufacturing Letters |
| Volume | 46 |
| DOIs | |
| State | Published - Dec 2025 |
All Science Journal Classification (ASJC) codes
- Mechanics of Materials
- Industrial and Manufacturing Engineering
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