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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 ,
Arnaud Marmier ,
Grant Dennis ,
Will Curry  and
Jason Matthews
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, Jacob2.Hatherell@live.uwe.ac.uk

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

Keywords

SimulationComputational design methodsCase studyOptimisation
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1815 - 1824
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

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