Digital Twin Development and Validation for a Tapered Roller Bearing Multi-Stage Production Line

Abstract:

The objective of this work is to develop and validate a Digital Twin (DT) for a multistage production line of tapered roller bearings. The manufacturing process consists of ring machining and component assembly, including intensive quality controls. This work proposes the integration of machine learning models associated with the manufacture of the double outer ring and the two inner rings in the DT. The models are trained with real data, so that the DT can predict the behavior of the production process under changing conditions of ongoing processes, machines or materials, and optimal operating conditions can be predicted. The DT has been developed and integrated with the aim of guiding production by proposing optimal machine configurations. To this end, different stations have been modeled and integrated into the DT as independent modules: grinding machines, inner and outer rings pairing module, and a module for calculating the optimal family of rings to be ground. After integrating the DT in the line, results show not only a raise in the line efficiency but also a decrease in the overall scrap ratio.

• Periodical: Advances in Science and Technology (Volume 132)
• Pages: 184-193
• DOI: https://doi.org/10.4028/p-H9Xqxe
• Online since: October 2023
• Authors: Javier Domínguez*, Alvaro Esteban, José Antonio Romeo, Fernando Cebrián, Sergio Santo Domingo, Juan José Aguilar Martín
• Keywords: Digital Twin, Industry 4.0, Machine Learning, Tapered Roller Bearing Production

References

[1] Prakash M. Jadhav, Surajkumar G. Kumbhar, R.G. Desavale, Shubham B. Patil, Distributed fault diagnosis of rotor-bearing system using dimensional analysis and experimental methods, Measurement, Volume 166, 2020, 108239.

DOI: 10.1016/j.measurement.2020.108239

[2] Sebastian Schwendemann, Zubair Amjad, Axel Sikora, A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines, Computers in Industry, Volume 125, 2021, 103380.

DOI: 10.1016/j.compind.2020.103380

[3] Brosed, F.J.; Victor Zaera, A.; Padilla, E.; Cebrián, F.; Aguilar, J.J. In-process measurement for the process control of the real-time manufacturing of tapered roller bearings. MATERIALS. 11 - 8, p.1371. 2018. ISSN 1996-1944.

DOI: 10.3390/ma11081371

[4] Sadeghi F, Jalalahmadi B, Slack T S, Raje N, Arakere N K. A review of rolling contact fatigue. ASME Transactions on Tribology 2009; 131:041403-1-15.

DOI: 10.1115/1.3209132

[5] Bertolini, M.; Mezzogori, D.; Neroni, M.; Zammori, F. Machine Learning for industrial applications: A comprehensive literature review. Expert Syst. Appl. 2021, 175, 114820.

DOI: 10.1016/j.eswa.2021.114820

[6] Chen, T.; Sampath, V.; May, M.C.; Shan, S.; Jorg, O.J.; Aguilar Martín, J.J.; Stamer, F.; Fantoni, G.; Tosello, G.; Calaon, M. Machine Learning in Manufacturing towards Industry 4.0: From 'For Now' to 'Four-Know'. Appl. Sci. 2023, 13, 1903.

DOI: 10.3390/app13031903

[7] Shan Ren, Yingfeng Zhang, Yang Liu, Tomohiko Sakao, Donald Huisingh, Cecilia M.V.B. Almeida, A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions, Journal of Cleaner Production, Volume 210, 2019, Pages 1343-1365.

DOI: 10.1016/j.jclepro.2018.11.025

[8] Wan J, Li X, Dai H-N, Kusiak A, Martinez-García M and Li D 2021 Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges in Proceedings of the IEEE 109 377.

DOI: 10.1109/JPROC.2020.3034808

[9] Kritzinger W, Karner M, Traar G, Henjes J and Sihn W 2018 Digital Twin in manufact.: A categorical literature review and classification IFAC-PapersOnLine 51 1016-1022.

DOI: 10.1016/j.ifacol.2018.08.474

[10] Domínguez, J., Esteban, A., Romeo, J. A., Cebrián, F., Santo Domingo, S., and Aguilar, J. J. Development of Machine Learning prediction models for their integration in a Digital Twin for a tapered roller bearing production line. IOP Conference Series: Materials Science and Engineering. 2021. Vol. 1193, No. 1, p.012108.

DOI: 10.1088/1757-899X/1193/1/012108