Augmented Reality for PCB Component Identification and Localization
Source abstract
Abstract
This study evaluates the effectiveness of augmented reality (AR), using the Microsoft™ HoloLens™™ 2, for identifying and localizing PCB components compared to traditional PDF-based methods. Two experiments examined the influence of user expertise, viewing angles, and component sizes on accuracy and usability. The results indicate that AR improved identification accuracy and user experience for non-experts, although it was slower than traditional methods for experienced users. Optimal performance was achieved at 90° viewing angles, while accuracy declined significantly at oblique angles. Medium-sized components received the highest confidence scores, suggesting favorable visibility and recognition characteristics within this group, though further evaluation with a broader component distribution is warranted. Participant feedback highlighted the system’s intuitive interface and effective guidance, but also noted challenges with marker stability, visual discomfort, and ergonomic limitations. These findings suggest that AR can enhance training and reduce errors in electronics manufacturing, although refinements in marker rendering and user onboarding are necessary to support broader adoption. This research provides empirical evidence on the role of AR in supporting user-centered design and improving task performance in industrial electronics workflows.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Augmented Reality Applications · Interactive and Immersive Displays · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer and Geospatial Sciences, University of Gävle, 80176 Gävle, Sweden
Hellman Dynamic Gävle AB, 80310 Gävle, Sweden
Funding
European Regional Development Fund
References (51)
Show source-provided references
1. Printed Circuit Board Defect Detection Using Machine Learning
2. The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly10.28991/HIJ-2022-03-01-01 ↗
3. Soldering defect detection in automatic optical inspection10.1016/j.aei.2019.101004 ↗
4. A Review of Various Defects in PCB10.1007/s10836-022-06026-7 ↗
5. Radamson, H.H., He, X., Zhang, Q., Liu, J., Cui, H., Xiang, J., Kong, Z., Xiong, W., Li, J., and Gao, J. (2019). Miniaturization of CMOS. Micromachines, 10.10.3390/mi10050293 ↗
6. A Contribution to Printed Circuit Boards’ Miniaturization by the Vertical Embedding of Passive Components10.1115/1.4062470 ↗
7. Gattullo, M., Laviola, E., Evangelista, A., Fiorentino, M., and Uva, A.E. (2022). Towards the Evaluation of Augmented Reality in the Metaverse: Information Presentation Modes. Appl. Sci., 12.10.3390/app122412600 ↗
8. Morales Méndez, G., and del Cerro Velázquez, F. (2024). Augmented Reality in Industry 4.0 Assistance and Training Areas: A Systematic Literature Review and Bibliometric Analysis. Electronics, 13.10.3390/electronics13061147 ↗
9. Vidal-Balea, A., Blanco-Novoa, O., Fraga-Lamas, P., Vilar-Montesinos, M., and Fernández-Caramés, T.M. (2020). Creating Collaborative Augmented Reality Experiences for Industry 4.0 Training and Assistance Applications: Performance Evaluation in the Shipyard of the Future. Appl. Sci., 10.10.3390/app10249073 ↗
10. Ergonomics improvements of the visual inspection process in a printed circuit assembly factory10.1080/10803548.2004.11076622 ↗
11. Park, S., Bokijonov, S., and Choi, Y. (2021). Review of microsoft hololens applications over the past five years. Appl. Sci., 11.10.3390/app11167259 ↗
12. Vidal-Balea, A., Blanco-Novoa, O., Fraga-Lamas, P., Vilar-Montesinos, M., and Fernández-Caramés, T.M. (2020). A Collaborative Augmented Reality Application for Training and Assistance during Shipbuilding Assembly Processes. Proceedings, 54.10.3390/proceedings2020054004 ↗
13. Chatterjee, I., Pforte, T., Tng, A., Salemi Parizi, F., Chen, C., and Patel, S. (November, January 29). ARDW: An Augmented Reality Workbench for Printed Circuit Board Debugging. Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, Bend, OR, USA. UIST 2022.10.1145/3526113.3545684 ↗
14. Hahn, J., Ludwig, B., and Wolff, C. (2015). Augmented reality-based training of the PCB assembly process. ACM Int. Conf. Proceeding Ser., 395–399.10.1145/2836041.2841215 ↗
15. A Hybrid 3D Registration Method of Augmented Reality for Intelligent Manufacturing10.1109/ACCESS.2019.2959809 ↗
16. Comparative analysis of augmented reality in an engineering drawing course: Assessing spatial visualisation and cognitive load with marker-based, markerless and Web-based approaches
17. Alessa, F.M., Alhaag, M.H., Al-harkan, I.M., Ramadan, M.Z., and Alqahtani, F.M. (2023). A Neurophysiological Evaluation of Cognitive Load during Augmented Reality Interactions in Various Industrial Maintenance and Assembly Tasks. Sensors, 23.10.3390/s23187698 ↗
18. Park, S., Maliphol, S., Woo, J., and Fan, L. (2024). Digital Twins in Industry 4. Electronics, 13.10.3390/electronics13122258 ↗
19. Voinea, G.D., Gîrbacia, F., Duguleană, M., Boboc, R.G., and Gheorghe, C. (2023). Mapping the Emergent Trends in Industrial Augmented Reality. Electronics, 12.10.3390/electronics12071719 ↗
20. A systematic review of augmented reality applications in maintenance10.1016/j.rcim.2017.06.002 ↗
21. Chatterjee, I., Khvan, O., Pforte, T., Li, R., and Patel, S. (July, January 28). Augmented Silkscreen: Designing AR Interactions for Debugging Printed Circuit Boards. Proceedings of the 2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere, Virtual. DIS 2021.10.1145/3461778.3462091 ↗
22. A systematic review of Augmented Reality content-related techniques for knowledge transfer in maintenance applications10.1016/j.compind.2018.08.007 ↗
23. Haraguchi, D., and Miyahara, R. (2023). High Accuracy and Wide Range Recognition of Micro AR Markers with Dynamic Camera Parameter Control †. Electronics, 12.10.20944/preprints202308.0850.v1 ↗
24. Augmented Reality Smart Glasses in the Smart Factory: Product Evaluation Guidelines and Review of Available Products10.1109/ACCESS.2017.2703952 ↗
25. User Comfort in VR/AR Headsets: A Mathematical Investigation into Ergonomic and Functional Limitations of Eye Tracking Technology10.1177/10711813241263510 ↗
26. Ariwa, M., Itamiya, T., Koizumi, S., and Yamaguchi, T. (2021). Comparison of the observation errors of augmented and spatial reality systems. Appl. Sci., 11.10.3390/app112412076 ↗
27. Vuforia Developer Library (2024, May 20). Best Practices for Designing and Developing Image-Based Targets. Vuforia. Available online: https://developer.vuforia.com/library/vuforia-engine/images-and-objects/image-targets/best-practices/best-practices-designing-and-developing-image-based-targets/.
28. The effects of augmented reality on improving spatial problem solving for object assembly10.1016/j.aei.2018.10.004 ↗
29. Using Augmented Reality to Facilitate Piping Assembly: An Experiment-Based Evaluation10.1061/(ASCE)CP.1943-5487.0000344 ↗
30. Xu, M., Shu, Q., Huang, Z., Chen, G., and Poslad, S. (2025). ARLO: Augmented Reality Localization Optimization for Real-Time Pose Estimation and Human–Computer Interaction. Electronics, 14.10.3390/electronics14071478 ↗
31. Seeliger, A., Merz, G., Holz, C., and Feuerriegel, S. (2021, January 4–8). Exploring the Effect of Visual Cues on Eye Gaze during AR-Guided Picking and Assembly Tasks. Proceedings of the 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2021, Virtual.10.1109/ISMAR-Adjunct54149.2021.00041 ↗
32. Yang, Z., Shi, J., Jiang, W., Sui, Y., Wu, Y., Ma, S., Kang, C., and Li, H. (2019). Influences of augmented reality assistance on performance and cognitive loads in different stages of assembly task. Front. Psychol., 10.10.3389/fpsyg.2019.01703 ↗
33. Devos, H., Gustafson, K., Ahmadnezhad, P., Liao, K., Mahnken, J.D., Brooks, W.M., and Burns, J.M. (2020). Psychometric properties of NASA-TLX and index of cognitive activity as measures of cognitive workload in older adults. Brain Sci., 10.10.1101/2020.10.26.20219881 ↗
34. Kim, J.Y., and Choi, J.K. (2025). Effects of AR on Cognitive Processes: An Experimental Study on Object Manipulation, Eye-Tracking, and Behavior Observation in Design Education. Sensors, 25.10.3390/s25061882 ↗
35. Qian, X., He, F., Hu, X., Wang, T., Ipsita, A., and Ramani, K. (May, January 29). ScalAR: Authoring Semantically Adaptive Augmented Reality Experiences in Virtual Reality. Proceedings of the Conference on Human Factors in Computing Systems, New Orleans, LA, USA.
36. Ito, K., Tada, M., Ujike, H., and Hyodo, K. (2021). Effects of the weight and balance of head-mounted displays on physical load. Appl. Sci., 11.10.3390/app11156802 ↗
37. A Discussion of Cybersickness in Virtual Environments10.1145/333329.333344 ↗
38. (2012). Ergonomics of Human-System Interaction—Part 411: Evaluation Methods for the Design of Physical Input Devices (Standard No. ISO/TS 9241-411:2012).
39. Augmented Reality for Machine Setups: Task Performance and Usability Evaluation in a Field Test10.1016/j.procir.2022.05.027 ↗
40. Chen, L., Zhao, H., Shi, C., Wu, Y., Yu, X., Ren, W., Zhang, Z., and Shi, X. (2024). Enhancing Multi-Modal Perception and Interaction: An Augmented Reality Visualization System for Complex Decision Making. Systems, 12.10.3390/systems12010007 ↗
41. Evaluation of Augmented Reality instructions based on initial and dynamic assembly tolerance allocation schemes in precise manual assembly10.1016/j.aei.2024.102954 ↗
42. Bachmann, M., Subramaniam, A., Born, J., and Weibel, D. (2023). Virtual reality public speaking training: Effectiveness and user technology acceptance. Front. Virtual Real., 4.10.3389/frvir.2023.1242544 ↗
43. Montgomery, D.C. (2013). Design and Analysis of Experiments, John Wiley & Sons, Inc.
44. Jones, B., and Kenward, M.G. (2014). Design and Analysis of Cross-Over Trials, Chapman and Hall/CRC. [3rd ed.].10.1201/b17537 ↗
45. Toutenburg, H. (2009). Statistical Analysis of Designed Experiments, Springer. [3rd ed.]. Springer Texts in Statistics.10.1007/978-1-4419-1148-3 ↗
46. Augmented reality in support of Industry 4.0—Implementation challenges and success factors10.1016/j.rcim.2019.02.003 ↗
47. Augmented reality applications in design and manufacturing10.1016/j.cirp.2012.05.010 ↗
48. Comparing head-mounted and handheld augmented reality for guided assembly10.1007/s12193-024-00440-1 ↗
49. Hololens 2 Spatial Mapping Capabilities in Vast Monumental Heritage Environments10.5194/isprs-archives-XLVI-2-W1-2022-489-2022 ↗
50. Saeed, F., Mohammed, F., and Fazea, Y. (2024). The Era of Industry 5.0: An Overview of Technologies, Applications, and Challenges. Advances in Intelligent Computing Techniques and Applications, Springer Nature.10.1007/978-3-031-59707-7 ↗
51. Stadler, S., Cornet, H., and Frenkler, F. (2023). Assessing Heuristic Evaluation in Immersive Virtual Reality—A Case Study on Future Guidance Systems. Multimodal Technol. Interact., 7.10.3390/mti7020019 ↗