# Augmented Reality for PCB Component Identification and Localization
## 6 Discussion  
This study evaluated the effectiveness of augmented reality (AR) technology, specifically the Microsoft(tm) HoloLens(tmtm) 2, for identifying and localizing PCB components through two complementary experiments. Experiment 1 compared AR with traditional methods, revealing that the traditional approach enabled faster localization times, particularly for experienced users and smaller components. In contrast, the AR system offered comparable performance in some cases, notably among non-expert users. These findings align with previous observations that traditional methods benefit from user familiarity and well-established workflows [20; 24]. However, challenges with AR, such as marker stability, ergonomic comfort, and variability in task execution times, were evident, corroborating concerns raised in recent industrial AR evaluations [25]. Experiment 2 focused exclusively on the AR system to assess identification correctness across varying viewing angles. The results demonstrated that AR allowed for accurate component identification overall, though performance varied noticeably with viewing angle and component size. Specifically, oblique viewing angles and smaller components were associated with reduced accuracy, highlighting the critical role of visual alignment and marker recognition [26; 27]. These findings align with prior research on AR tracking, which has identified marker visibility and alignment as significant determinants of accu \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Focus** & **Key Variables** & **Main Findings** \\ \hline \multirow{4}{*}{**Exp. 1**} & AR vs. Traditional Method & Method, Component Size, User Expertise & Traditional method was faster overall, especially for experts. AR slightly better for non-experts. Component size and method order affected outcomes. \\ \cline{2-4}  & Usability and Qualitative Feedback & AR usability, marker precision, interface & Mixed usability ratings. Issues included marker flicker, interface learning curve, and headset discomfort. Suggestions made for improving UI and comfort. \\ \hline \multirow{4}{*}{**Exp. 2**} & Accuracy at Different Viewing Angles & Viewing Angle (90\({}^{\circ}\), 45\({}^{\circ}\)), Position Error & Highest accuracy at 90\({}^{\circ}\). Significant decline in AR augmentation accuracy at 45\({}^{\circ}\). Users compensated for AR drift using spatial cues. \\ \cline{2-4}  & Component Identification Correct/Incorrect identification, Position & Correctness highest at 90\({}^{\circ}\), lowest at 45\({}^{\circ}\). Larger components and edge placement improved results. Geometric bias noted for smaller components. \\ \cline{2-4}  & & & Medium components identified with highest confidence. Small components most affected by viewing angle. Limited generalizability for large components. \\ \cline{2-4}  & & Task Completion Time at 3 Angles & No significant difference. Slight variability across angles. Highest SD at 90\({}^{\circ}\) likely due to learning curve. \\ \hline \multirow{4}{*}{Usability Feedback} & Physical, visual, interface, learning curve & Participants cited headset discomfort, visual strain, and usability limitations. Training and ergonomic improvements recommended. \\ \cline{2-4}  & & & Participants cited headset discomfort, visual strain, and usability limitations. Training and ergonomic improvements recommended. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of Experimental Results, Analyzed Variables, and Key Findings. racy in industrial contexts [27; 28]. The study underscores AR's potential to support PCB localization tasks, especially for users with limited domain expertise, if improvements are made in tracking stability, user interface design, and marker visibility [19]. The analysis of viewing angles in Experiment 2 identified 90\({}^{\circ}\) as the optimal condition for AR performance, with both augmented and indicated positions closely aligned to the reference positions.