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EXPLORING THE POTENTIAL FOR A FEA-BASED DESIGN OF EXPERIMENTS TO DEVELOP DESIGN TOOLS FOR BULK-METAL JOINING PROCESSES

Published online by Cambridge University Press:  19 June 2023

Jacob Hatherell*
Affiliation:
University of the West of England
Arnaud Marmier
Affiliation:
University of the West of England
Grant Dennis
Affiliation:
SKF (U.K) Ltd
Will Curry
Affiliation:
SKF (U.K) Ltd
Jason Matthews
Affiliation:
University of the West of England
*
Hatherell, Jacob, University of the West of England, United Kingdom, [email protected]

Abstract

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Over the last 20 years, finite element analysis (FEA) has become a standard analysis tool for metal joining processes. When FEA tools are combined with design of experiments (DOE) methodologies, academic research has shown the potential for virtual DOE to allow for the rapid analysis of manufacturing parameters and their influence on final formed products. However, within the domain of bulk-metal joining, FEA tools are rarely used in industrial applications and limit DOE trails to physical testing which are therefore constrained by financial costs and time.

This research explores the suitability of an FEA-based DOE to predict the complex behaviour during bulk-metal joining processes through a case study on the staking of spherical bearings. For the two DOE outputs of pushout strength and post-stake torque, the FEA-based DOE error did not exceed ±1.2% and ± 1.5 Nm respectively which far surpasses what was previously capable from analytically derived closed-form solutions. The outcomes of this case study demonstration the potential for FEA-based DOE to provide an inexpensive, methodical, and scalable solution for modelling bulk-metal joining process

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Al-Momani, E. and Rawabdeh, I. (2008) “An Application of Finite Element Method and Design of Experiments in the Optimization of Sheet Metal Blanking Process”, Jordan Journal of Mechanical and Industrial Engineering, Vol. 2, pp. 5363.Google Scholar
ANSYS (2021) ANSYS Workbench 2021R1. Available from: www.ansys.com. [Accessed 11 April 2022].Google Scholar
Coleman, P. (2012) CRESCENDO – Collaborative and Robust Engineering using Simulation Capability Enabling Next Design Optimisation. TRIMIS – Transport Research and Innovation Monitoring and Information System. Available from: https://cordis.europa.eu/project/id/234344. [Accessed 10 November 2022].Google Scholar
Eckert, C., Isaksson, O. and Earl, C. (2019) “Design margins: a hidden issue in industry”, Design Science, Vol. 5(9), pp. 124. https://doi.org/10.1017/dsj.2019.7CrossRefGoogle Scholar
Groche, P., Wohletz, S., Brenneis, M., Pabst, C. and Resch, F. (2014) “Joining by forming. A review on joint mechanisms, applications, and future trends”, Journal of Materials Processing Technology, Vol. 214(10), pp. 19721994.CrossRefGoogle Scholar
Hicks, B. J. and Matthews, J. (2010) The barriers to realising sustainable process improvement: a root cause analysis of paradigms for manufacturing systems improvement. International Journal of Computer. Integrated. Manufacture., 23(7), 585602. https://doi.org/10.1080/0951192X.2010.485754.CrossRefGoogle Scholar
Hoo, J. J. C., and Green, W.B. (1998) Bearing Steels: Into the 21st Century. Pennsylvania: ASTM International. https://doi.org/10.1520/stp1327-ebCrossRefGoogle Scholar
Jin, R., Chen, W. and Sudjianto, A. (2003) “An efficient algorithm for constructing optimal design of computer experiments”. In Volume 2: 29th Design Automation Conference, Parts A and B, Vol. 2, pp. 545554.Google Scholar
Joseph, V. R., Gu, L., Ba, S. and Myers, W. R. (2019) “Space-filling designs for robustness experiments”, Technometrics, Vol. 61, pp. 2437. https://doi.org/10.1080/00401706.2018.1451390CrossRefGoogle Scholar
Kalpajian, S. and Schmid, S.R. (2008) Manufacturing Processes for Engineering Materials, 5th ed. California: Pearson Education.Google Scholar
Kim, H.S. (2010) “A combined FEA and design of experiments approach for the design and analysis of warm forming of aluminium sheet alloys”. International Journal of Advanced Manufacturing Technology, Vol. 51, pp. 114. https://doi.org/10.1007/s00170-010-2620-8CrossRefGoogle Scholar
Kim, B.C., Park, D.C., and Kim, H.S. (2006) “Development of composite spherical bearing”, Composite Structures, Vol. 75, pp. 231240. https://doi.org/10.1016/j.compstruct.2006.04.027CrossRefGoogle Scholar
Lehman, J. S., Santner, T. J. and Notz, W. I. (2004) “Designing computer experiments to determine robust control variables”. Statistica Sinica, Vol. 14(2), pp. 571590.Google Scholar
Mori, K.I., Bay, N., Fratini, L., Micari, F. and Tekkaya, A.E. (2013) “Joining by Plastic Deformation”. CIRP Annals, Manufacturing Technology. Vol. 62(2), pp. 673694. https://doi.org/10.1016/j.cirp.2013.05.004CrossRefGoogle Scholar
Nerenst, T.B, Ebro, M., Nielsen, M. Eifler, T. and Nielsen, K.L. (2021) “Exploring barriers for the use of FEA-based variation simulation in industrial development practice”, Design Science, Vol. 7(21), pp. 122. https://doi.org/10.1017/dsj.2021.21CrossRefGoogle Scholar
Oudjene, M. and Ben-Ayed, L. (2008) . “On the parametrical study of clinch joining of metallic sheets using the Taguchi method”, Engineering Structures, Vol. 30(6), pp. 17821788. https://doi.org/10.1016/j.engstruct.2007.10.017CrossRefGoogle Scholar
Sarema, B., Matope, S. and Sterzing, A. (2022) “Design of experiments procedure for evaluating the formability of sheet metals components in forming processesSouthern African Institute of Industrial Engineers 33rd Annual Conference, KwaZulu-Natal, 3-5 October 2022.CrossRefGoogle Scholar
Taguchi, G., Chowdhury, S. & Wu, Y. (2007) Taguchi's Quality Engineering Handbook. Michigan: John Wiley & Sons, Inc. https://doi.org/10.1002/9780470258354.ch4Google Scholar
Will, J. (2015) “Robust design optimization in virtual prototyping – promises and challenges”, In NAFEMS - International Association Engineering Modelling. https://www.dynardo.de/en/library/methodology/robust-design-optimization.htmlGoogle Scholar
Woodhead, J., Truman, C.E. and Booker, J.D. (2015) “Modelling of dynamic friction in the cold forming of plain spherical bearings”, Contact and Surface 2015. Valencia, 21-23 April 2015. Southampton: WIT Press, pp. 141152. https://doi.org/10.2495/secm150131Google Scholar
Zhang, Q., Hu, Z., Su, W., Zhou, H., Qi, X. and Yang, Y. (2018) “Investigation on housing chamfer parameters in roller swaging for self-lubricating spherical plain bearings assembly”, International Journal of Advanced Manufacturing Technology, Vol. 95, pp. 10891099. https://doi.org/10.1007/s00170-017-1280-3CrossRefGoogle Scholar