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MULTI-OBJECTIVE OPTIMIZATION OF HOSE ASSEMBLY ROUTING FOR VEHICLES

Part of: Design Support Tools

Published online by Cambridge University Press:  11 June 2020

C. Wehlin ,
J. A. Persson  and
J. Ölvander
C. Wehlin*
Affiliation:
Linköping University, Sweden
J. A. Persson
Affiliation:
Linköping University, Sweden
J. Ölvander
Affiliation:
Linköping University, Sweden
*
*camilla.wehlin@liu.se

Abstract

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This paper presents a method for multi-objective optimization of hose assembly routing. Hose routing is a non-trivial task which demand a lot of iterations, especially with the increased complexity in modern vehicles. The proposed method utilizes design automation through multi-objective optimization of routing assemblies containing multiple hoses. The method is intended as a decision support and automation-tool, that reduces the number of iterations needed. The method has been implemented and tested on a case, concerning a set of hoses in an engine compartment, showing credible results.

Keywords

design optimisationdesign automationknowledge-based engineering (KBE)

Information

Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 471 - 480
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), 2020. Published by Cambridge University Press

References

Amadori, K. (2012), Geometry Based Design Automation Applied to Aircraft Modelling and Optimization, [PhD Thesis], Linköping University.Google Scholar
Amadori, K. et al. (2012), “Flexible and robust CAD models for design automation”, Advanced Engineering Informatics, Vol. 26 No. 2, pp. 180–195. https://doi.org/10.1016/j.aei.2012.01.004CrossRefGoogle Scholar
Andersson, J. (2000), A Survey of Multiobjective Optimization in Engineering Design, Department of Mechanical Engineering, Linköping University.Google Scholar
Chang, K.-H. (2015), “Multiobjective Optimization and Advanced Topics”, In: Design Theory and Methods Using CAD/CAE, Elsevier, pp. 325–406. https://doi.org/10.1016/b978-0-12-398512-5.00005-0CrossRefGoogle Scholar
Fraunhofer Chalmers Center (2019), Industrial Path Solutions [online]. Avaliable at: http://www.fcc.chalmers.se/software/ips/ (accessed 31.10.2019).Google Scholar
Ghosh, A. and Dehuri, S. (2004), “Evolutionary Algorithms for Multi-Criterion Optimization: A Survey”, International Journal of Computing & Information Sciences, Vol. 2 No. 1, pp. 38–57.Google Scholar
Kabul, I., Gayle, R. and Lin, M.C. (2007), “Cable Route Planning in Complex Environments Using Constrained Sampling”, Proceedings - SPM 2007: ACM Symposium on Solid and Physical Modeling, pp. 395–402. https://doi.org/10.1145/1236246.1236303CrossRefGoogle Scholar
La Rocca, G. (2012), “Knowledge based engineering: Between AI and CAD. Review of a language based technology to support engineering design”, Advanced Engineering Informatics, Vol. 26 No. 2, pp. 159–179, https://doi.org/10.1016/j.aei.2012.02.002CrossRefGoogle Scholar
Li, H. and Zhang, Q. (2006), “A Multiobjective Differential Evolution based on Decomposition for Multiobjective Optimization with Variable Linkages”, Proceedings of Parallel Problem Solving from Nature, pp. 583–592. https://doi.org/10.1007/11844297_59Google Scholar
Liu, L. and Liu, Q. (2018), “Multi-objective routing of multi-terminal rectilinear pipe in 3D space by MOEA/D and RSMT”, 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), IEEE, pp. 583–592. https://doi.org/10.1109/icarm.2018.8610824CrossRefGoogle Scholar
Lua, (2019), Lua [online]. Availiable at: https://www.lua.org/ (accessed 31.10.2019).Google Scholar
Hermansson, T. et al. (2016), “Automatic Routing of Flexible 1D Components with Functional and Manufacturing Constraints”, Computer-Aided Design, Vol. 79, pp. 27–35. https://doi.org/10.1016/j.cad.2016.05.018CrossRefGoogle Scholar
Hermansson, T. (2017), Computational Methods for Deformable 1D Objects in Virtual Product Realization, [Lic. Thesis], Chalmers University of Technology.Google Scholar
Pahl, G. and Beitz, W. (1996), Engineering Design – A Systematic Approach, Springer-Verlag, London.Google Scholar
Pugh, S. (1991), Total Design – Integrated Methods for Successful Product Engineering, Addison-Wesley Publishing Company Inc., Wokingham.Google Scholar
Qu, Y., Jiang, D. and Yang, Q. (2018), “Branch Pipe Routing based on 3D Connection Graph and Concurrent Ant Colony Optimization Algorithm”, Journal of Intelligent Manufacturing, Vol. 29, pp. 1647–1657. https://doi.org/10.1007/s10845-016-1203-4CrossRefGoogle Scholar
Roozenburg, N.F.M. and Eekels, J. (1995), Product Design: Fundamentals and Methods, John Wiley & Son Ltd., Chichester.Google Scholar
Simpson, T.W. and Martins, J.R.R.A. (2011), “Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop”, Journal of Mechanical Design, Vol. 113. https://doi.org/10.1115/1.4004465Google Scholar
Sobieszczanski-Sobieski, J., Morris, A. and van Tooren, M. (2015), Multidisciplinary Design Optimization supported by Knowledge Based Engineering, John Wiley & Sons, Ltd, West Sussex.CrossRefGoogle Scholar
Stokes, M. (2001), Managing Engineering Knowledge: MOKA: Methodology for Knowledge Based Engineering Applications, Professional Engineering Publishing, London.Google Scholar
Thantulage, G.I.F. (2009), Ant Colony Optimization Based Simulation of 3D Automatic Hose/Pipe Routing, [PhD Thesis], School of Engineering and Design, Brunel University.Google Scholar
Tomiyama, T. (2007), “Intelligent computer-aided design systems: Past 20 years and future 20 years”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, Vol. 21 No. 1, pp. 27–29. https://doi.org/10.1017/S0890060407070114CrossRefGoogle Scholar
Ulrich, K.T. and Eppinger, S.D. (2012), Product Design and Development, Fifth Edition, McGraw-Hill, New York.Google Scholar
Verhagen, W.J.C. et al. (2012), “A critical review of Knowledge-Based Engineering: An identification of research challenges”, Advanced Engineering Informatics, Vol. 26 No. 1, pp. 5–15. https://doi.org/10.1016/j.aei.2011.06.004CrossRefGoogle Scholar
Wynn, D.C. and Clarkson, P.J. (2018), “Process Models in Design and Development”, Research in Engineering Design, Vol. 29, pp. 161–202. https://doi.org/10.1007/s00163-017-0262-7CrossRefGoogle Scholar