Abstract
The ability to obtain useful information to support decision-making from big data sets delivered by sensors can significantly contribute to enhance smart manufacturing initiatives. This paper presents the results of a study performed in an automotive electronics assembly line. An approach that uses Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) was applied to early detect undesirable changes in the screwing processes performance by extracting relevant information from the torque-angle curve data. Since the data of different torque-angle curves are not aligned, the proposed approach includes the linear interpolation of the original data to enable Principal Component Analysis (PCA). PCA proved to be an appropriate technique to obtain significant information from the process variables, which consist of the successive value of the torque at constant angular intervals. Score plots and multivariate control charts were used to detect defective tightening and identify behaviors that represent inefficient tightening. This is a new approach that can be applied to effectively monitor screwing processes in the assembly of different products either periodically or in real-time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Foehr, M., Lüder, A., Wagner, T., Jäger, T., Fay, A.: Development of a method to analyze the impact of manufacturing systems engineering on product quality. In: 2011 IEEE 16th Conference on Emerging Technologies & Factory Automation (ETFA), Toulouse, France, pp. 1–4. IEEE (2011)
Wolter, B., Dobmann, G., Boller, C.: NDT based process monitoring and control. Strojniški Vestn. – J. Mech. Eng. 57, 218–226 (2011)
Tsarouchi, P., et al.: Robotized assembly process using dual arm robot. Procedia CIRP 23, 47–52 (2014)
Longwic, R., Nieoczym, A.: Control of the process of screwing in the industrial screwdrivers. Adv. Sci. Technol. 10, 202–206 (2016)
Yuliya, L., Ulrich, B.: Development of an information system with maturity degree management for automated screwing processes. In: Nunes, M.B., Isaías, P., Powell, P. (eds.) Proceedings of the IADIS International Conference Information Systems 2012, pp. 49–56. IADIS Press, Berlin (2012)
Chumakov, R.: Optimal control of screwing speed in assembly with thread-forming screws. Int. J. Adv. Manuf. Technol. 36, 395–400 (2008)
Deters, C., Lam, H.K., Secco, E.L., Wurdemann, H.A., Seneviratne, L.D., Althoefer, K.: Accurate bolt tightening using model-free fuzzy control for wind turbine hub bearing assembly. IEEE Trans. Control Syst. Technol. 23, 1–12 (2015)
Lebedynska, Y., Berger, U.: Intelligent knowledge-based system for the automated screwing process control. In: Proceedings of the 11th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED 2012), Cambridge, pp. 175–180 (2012)
Saygin, C., Mohan, D., Sarangapani, J.: Real-time detection of grip length during fastening of bolted joints: a Mahalanobis-Taguchi system (MTS) based approach. J. Intell. Manuf. 21, 377–392 (2010)
Cristalli, C., et al.: Integration of process and quality control using multi-agent technology. In: 2013 IEEE International Symposium on Industrial Electronics, Taipei, Taiwan, pp. 1–6 (2013)
He, Q.P., Wang, J.: Statistical process monitoring as a big data analytics tool for smart manufacturing. J. Process Control. 67, 35–43 (2018)
Yeh, A.B., Li, B., Wang, K.: Monitoring multivariate process variability with individual observations via penalised likelihood estimation. Int. J. Prod. Res. 50, 6624–6638 (2012)
Siddiqui, Y.A., Saif, A.-W., Cheded, L., Elshafei, M., Rahim, A.: Integration of multivariate statistical process control and engineering process control: a novel framework. Int. J. Adv. Manuf. Technol. 78(1–4), 259–268 (2014). https://doi.org/10.1007/s00170-014-6641-6
Lin, W., Qian, Y., Li, X.: Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis. Comput. Chem. Eng. 24, 423–429 (2000)
Babamoradi, H., van den Berg, F., Rinnan, A.: Confidence limits for contribution plots in multivariate statistical process control using bootstrap estimates. Anal. Chim. Acta. 908, 75–84 (2016)
Mura, M.D., Dini, G., Failli, F.: An integrated environment based on augmented reality and sensing device for manual assembly workstations. Procedia CIRP 41, 340–345 (2016)
Shoberg, R.S.: Engineering Fundamentals of Threaded Fastener Design and Analysis. RS Technologies, a Division of PCB Load & Torque, Inc., Farmington Hills (2000)
Acknowledgements
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Teixeira, H.N., Lopes, I., Braga, A.C., Delgado, P., Martins, C. (2022). Screwing Process Monitoring Using MSPC in Large Scale Smart Manufacturing. In: Machado, J., Soares, F., Trojanowska, J., Yildirim, S. (eds) Innovations in Mechatronics Engineering. icieng 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-79168-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-79168-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79167-4
Online ISBN: 978-3-030-79168-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)