# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 4 Results
### Experimentation and Results
The assembly is considered successful if the observed configuration matches the expected one. On the other hand, if there is any deviation between the observed and expected configurations, the assembly is classified as incorrect. The validation of operational functionality and component assembly sequence will be analyzed in future work. Several tests were carried out with similar results. The consistency and accuracy of the server-based image recognition system were evident, contributing to high levels of quality control. The prototype confirmed the feasibility and potential benefits of the proposed quality control methodology, highlighting the advantages of incorporating mixed reality and computer vision technologies in the inspection process. Three people assembled the pneumatic cylinder 4 h a day for 5 days to evaluate the system's performance, totaling 818 assemblies. The system detected 11 incorrect assemblies, constituting 1.34% of the total pneumatic cylinder assemblies during the test period. This result highlights the system's effectiveness in identifying and rectifying errors in the assembly process. The system's ability to quickly identify errors substantially reduces defective products, promoting excellent product reliability and increasing customer satisfaction. The real-time functionality of the system, seamlessly integrated into the assembly process, enhances operational efficiency by promptly detecting errors and enabling immediate corrective measures. This proactive approach curtails the propagation of faults throughout the manufacturing line, establishing the system as a linchpin in pursuing high-level quality control standards. Figure 8: Validation process of the assembly quality control: (**a**) NOT OK; (**b**) OK. The integration process was not without its challenges. Several notable obstacles were encountered and addressed during the development and testing phases. * Hardware Limitations: The HoloLens 2, while a state-of-the-art device, poses challenges in terms of computational power and memory constraints. Optimizing the object detection model and implementing efficient data processing is crucial to ensure real-time performance on the device. * Network Latency: Ensuring low-latency communication between the HoloLens and the server is critical for real-time feedback. It was necessary to implement robust protocols to handle potential delays. * User Interface Design: Designing an intuitive and user-friendly interface for HoloLens requires careful consideration. The placement and visualization of holographic overlays needed to be precise and unobtrusive, enhancing the user experience without hindering the inspector's workflow. To overcome these challenges, a collaborative approach involving multidisciplinary expertise was adopted. Hardware optimizations, network protocol refinements, and iterative user interface testing were conducted. Continuous user feedback during simulated scenarios allowed for rapid adjustments, ensuring that the final integrated system met user expectations and performance standards. Our study has made notable progress in demonstrating the feasibility and advantages of integrating mixed reality (MR) technologies into industrial quality control. However, it is imperative to recognize that specific challenges linked with introducing MR technologies into industrial settings still need to be addressed. These challenges include potential user distractions, security risks, eyestrain, and cybersickness. Previous authors [62; 63] have noted that using smart glasses may induce visual fatigue and cybersickness, manifesting as symptoms like disorientation and dizziness. In our specific study, visual fatigue and cybersickness were not thoroughly explored. This limitation arises due to the short duration of each cylinder assembly, lasting less than 5 min, and the reduced continuous assembly time in the laboratory environment.