Enhancing performance in bolt torque tightening using a connected torque wrench and augmented reality
Adeline FAU, Mina GHOBRIAL, Philippe SEITIER, Pierre LAGARRIGUE, Michel GALAUP, Alain DAIDIE, Patrick GILLES
Abstract. Modern production rates and the increasing complexity of mechanical systems require efficient and effective manufacturing and assembly processes. The transition to Industry 4.0, supported by the deployment of innovative tools such as Augmented Reality (AR), equips the industry to tackle future challenges. Among critical processes, the assembly and tightening of bolted joints stand out due to their significant safety and economic implications across various industrial sectors. This study proposes an innovative tightening method designed to enhance the reliability of bolted assembly tightening through the use of Augmented Reality and connected tools. A 6-Degrees-of-Freedom (6-DoF) tracked connected torque wrench assists the operator during tightening, ensuring each screw is tightened to the correct torque. The effectiveness of this method is compared with the conventional tightening method using paper instructions. Participants in the study carried out tightening sequences on two simple parts with multiple screws. The study evaluates the impact of the proposed method on task performance and its acceptability to operators. The tracked connected torque wrench provides considerable assistance to the operators, including wrench control and automatic generation of tightening reports. The results suggest that the AR-based method has the potential to ensure reliable torque tightening of bolted joints.
Keywords
Bolted Assembly, Connected Tools, Augmented Reality, Tracking, Operator Performance
Published online 5/7/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Adeline FAU, Mina GHOBRIAL, Philippe SEITIER, Pierre LAGARRIGUE, Michel GALAUP, Alain DAIDIE, Patrick GILLES, Enhancing performance in bolt torque tightening using a connected torque wrench and augmented reality, Materials Research Proceedings, Vol. 54, pp 1982-1991, 2025
DOI: https://doi.org/10.21741/9781644903599-213
The article was published as article 213 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] E. Gandolfi, Virtual Reality and Augmented Reality, Handbook of Research on K-12 Online and Blended Learning, 2nd ed., ETC Press, pp. 545–561, 2018.
[2] M. Ghobrial, P. Seitier, P. Lagarrigue, M. Galaup, and P. Gilles, Effectiveness of machining equipment user guides: A comparative study of augmented reality and traditional media, presented at the Material Forming. (2024) 2320-2328. https://doi.org/10.21741/9781644903131-255
[3] B. Wang, L. Zheng, Y. Wang, W. Fang, and L. Wang, Towards the industry 5.0 frontier: Review and prospect of XR in product assembly, Journal of Manufacturing Systems. 74 (2024) 777-811. https://doi.org/10.1016/j.jmsy.2024.05.002
[4] W. Li, J. Wang, S. Jiao, M. Wang, and S. Li, Research on the visual elements of augmented reality assembly processes, Virtual Reality & Intelligent Hardware. 1 (2019) 622-634. https://doi.org/10.1016/j.vrih.2019.09.006
[5] M. Quandt and M. Freitag, A Systematic Review of User Acceptance in Industrial Augmented Reality, Front. Educ. 6 (2021) 700760. https://doi.org/10.3389/feduc.2021.700760
[6] A. Claeys, S. Hoedt, E.-H. Aghezzaf, and J. Cottyn, Assessing assembly instructions quality using operator behavior, Int J Adv Manuf Technol. 135 (2024) 4531-4548. https://doi.org/10.1007/s00170-024-14740-z
[7] Q. Su, L. Liu, and D. E. Whitney, A Systematic Study of the Prediction Model for Operator-Induced Assembly Defects Based on Assembly Complexity Factors, IEEE Trans. Syst., Man, Cybern. A. 40 (2010) 107-120. https://doi.org/10.1109/TSMCA.2009.2033030
[8] P.-E. Sarlin et al., LaMAR: Benchmarking Localization and Mapping for Augmented Reality, in Computer Vision – ECCV 2022, S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, and T. Hassner, Eds., in Lecture Notes in Computer Science, Cham: Springer Nature Switzerland. 13667 (2022) 686-704. https://doi.org/10.1007/978-3-031-20071-7_40
[9] T. Hodaň et al., “BOP: Benchmark for 6D Object Pose Estimation,” in Computer Vision – ECCV 2018, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds., in Lecture Notes in Computer Science, Cham: Springer International Publishing. 11214 (2018) 19-35. https://doi.org/10.1007/978-3-030-01249-6_2
[10] S. G. Hart and L. E. Staveland, Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research, Advances in Psychology. Elsevier. (1988) 139‑183. https://doi.org/10.1016/S0166-4115(08)62386-9
[11] A. Bangor, P. Kortum, and J. Miller, Determining What Individual SUS Scores Mean: Adding an Adjective Rating Scale, J. Usability Studies. 4 (2009) 114‑123.
