# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 1 Introduction  
Quality control (QC) holds paramount importance in industrial manufacturing, serving as a linchpin for operational efficiency, product reliability, and overall customer satisfaction [1, 2, 3]. The intricate nature of manufacturing processes involving diverse components and the critical safety implications of the final product underscores the indispensable role of QC within the industry. Swift and precise quality inspections not only have a profound impact on an organization's financial well-being but also have a significant influence on the broader trajectory of the industry [4]. The belated discovery of defects, especially after the manufacturing process or product release, excats a heavy toll, encompassing substantial costs such as reworking, recalls, and a detrimental impact on the manufacturer's reputation. Thorough inspections meticulously integrated across all production stages play a pivotal role in mitigating these costs, optimizing both productivity and profitability. Moreover, a robust quality control system functions as a bulvark, instilling confidence in the brand and thereby enhancing sales and fortifying competitiveness in the market [5, 6]. Quality control acts as a safeguard against the late-stage identification of defects, safeguarding against financial losses and reputational damage. Its multifaceted benefits not only ensure the reliability and safety of the end product but also contribute significantly to the organizational bottom line, shaping a positive trajectory for the entire manufacturing industry. As manufacturing environments evolve, traditional inspection methods face challenges in keeping pace with the increasing intricacies of modern products and efficiency demands. Innovative approaches, such as integrating smart glasses and advanced image recognition, become crucial for enhancing precision, speed, and the overall effectiveness of quality control processes. This study delves into such advancements, exploring the potential of mixed reality and cutting-edge computer vision algorithms to propel quality control into a new era of efficiency and reliability. Traditionally, manual inspection processes relying on human experience and judgment have dominated industry practices [7; 8]. However, the intricacy of modern products and efficiency demands have stretched these traditional methods. Manual inspections are time-consuming and error-prone and may struggle to detect subtle or complex defects, leading to inconsistencies among inspectors [9; 10; 11]. In the pursuit of enhanced efficiency and precision, industries globally are turning to technology. Mixed reality (MR), characterized by the augmentation of the real world with digital information, emerges as a promising solution to QC challenges. MR facilitates real-time interactive overlays of data in the user's field of view, potentially revolutionizing inspection processes [12]. The integration of MR into quality control processes offers several advantages. Firstly, it boosts efficiency [13; 14; 15; 16], allowing inspectors to swiftly identify and assess parts without time-consuming manual checks against plans or specifications. Moreover, MR improves accuracy [16] by leveraging sophisticated imaging and recognition technology to detect defects missed by the human eye. MR technology introduces the real-time tracking of inspection cycles and tasks, providing insights into manufacturing process efficiency [17; 18]. This aids in identifying bottlenecks and improving workflow designs, which are crucial in industries like automotive manufacturing for understanding operational efficiency and productivity [16; 19]. Despite these potential benefits, the integration of MR into industrial quality control processes is in its early stages, raising questions about practical feasibility and effectiveness. Motivated by the need for more efficient and reliable quality control methods and inspired by MR's potential to transform industrial inspections, this study explores how MR can be effectively employed in the QC industry.