# Augmented Reality for PCB Component Identification and Localization
## 6 Discussion
At this angle, error distances and variability were minimized, demonstrating the system's effectiveness under conditions of optimal visibility and stability [28; 29]. At 60\({}^{\circ}\), performance declined, with increased variability in indicated positions, reflecting challenges in user interaction and marker alignment. The 45\({}^{\circ}\) condition presented the most substantial limitations, with elevated error rates and pronounced variability in both augmented and indicated positions. Similar findings have been reported in marker-based AR research, where oblique angles impair tracking performance due to surface visibility and occlusion issues [27; 29]. Notably, despite these limitations, participants maintained strong identification performance at challenging angles, suggesting they compensated using contextual spatial cues beyond the augmented markers, consistent with established spatial cognition theories [28]. Nonetheless, it is important to acknowledge that the current system relies solely on \(\text{Vuforia}^{\text{TM}}\)-based marker tracking using a texture image of the PCB. While this approach enabled markerless deployment, it proved sensitive to visual complexity and lighting. Densely packed or miniaturized component regions reduced marker stability due to insufficient distinctive texture features, as noted in Vuforia's best practices [27]. These limitations were particularly evident under oblique viewing angles, where loss of tracking precision was reported by users. This represents a technical limitation of the current study, as alternative tracking modalities--such as spatial anchors, depth sensing, or SLAM-based tracking--were not explored. Future work may compare these hybrid approaches to assess whether they can improve robustness and reduce angular sensitivity, ultimately enhancing AR performance in real-world electronics manufacturing environments [30]. In Experiment 1, traditional methods outperformed AR in task completion times, particularly among expert users, likely due to their familiarity with established workflows and reliance on cognitive shortcuts [20; 29]. In contrast, the AR system introduced additional interaction complexity, which may have contributed to slower performance [16]. Prior studies have confirmed that AR interfaces, while supportive for novices, impose higher cognitive loads during initial use, especially under conditions of marker tracking variability [17; 31; 32]. Moreover, participants performed tasks faster during their initial sequence, irrespective of method, indicating possible attention decline or cognitive fatigue in subsequent phases [32; 33]. Component size was a significant factor influencing performance in Experiment 2. Medium-sized components yielded the highest identification confidence across all viewing angles, likely due to their optimal balance between visibility and spatial footprint. In contrast, small components consistently posed challenges, particularly at 45\({}^{\circ}\), where confidence was lowest, aligning with prior findings on visual resolution and perception limits in AR environments [32]. Confidence for the single Large component was comparatively high at steeper angles; however, this result is based on a single data point and cannot support general conclusions. The limited and unbalanced representation of component sizes, especially the small number of Medium components (\(n=4\)) and only one large component, further restricts the validity of size-based trends. Additionally, the use of a binary correctness metric based on point-in-polygon inclusion may have introduced geometric bias, disproportionately benefiting larger or convex-shaped components. This metric does not account for footprint area or shape complexity, potentially confounding component-wise comparisons. To improve statistical rigor and interpretation in future work, studies should incorporate a more balanced distribution of component geometries and adopt alternative or shape-adjusted performance measures.