A novel XR-based real-time machine interaction system for Industry 4.0: Usability evaluation in a learning factory
Kaveh Amouzgar, Justus Willebrand
Abstract
Traditional methods of data visualization and process monitoring are increasingly inadequate in fast-paced, data-intensive manufacturing environments. Extended Reality (XR) technologies, including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), have the potential to enhance human–machine interaction and operational efficiency in Industry 4.0 framework. While previous research has demonstrated the effectiveness of XR in areas such as assembly, training, maintenance, and human–robot interaction, limited attention has been given to developing and evaluating XR systems for real-time machine data visualization. Most existing studies focus on demonstrating AR applications without rigorous comparative evaluations against other XR technologies or traditional Human–Machine Interfaces (HMIs), often with limited user testing. This study addresses these gaps by developing and evaluating an XR application using Microsoft HoloLens 2 for real-time process control in a Learning Factory environment. A mixed-methods approach, including experimental design, surveys, and time measurements, compared the XR system with conventional 2D HMIs. Data from 22 participants were analyzed, focusing on alarm response times, usability, and preventive maintenance. The findings show that the XR system significantly improves alarm response times, increases frequency of preventive refills, and enhances usability compared to traditional HMIs. However, challenges related to ergonomics and limited field of view were noted. This study contributes to advancing smart manufacturing by showcasing the potential of XR to improve human–machine interfaces and foster better interaction between machines and operators.