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Empirical Study of Car Crash Simulation Analysis within the Development Phase

Part of: Mobility

Published online by Cambridge University Press:  26 July 2019

Naouress Fatfouta ,
Julie Stal-Le Cardinal  and
Christine Royer
Naouress Fatfouta*
Affiliation:
Laboratoire Génie Industriel, centralSupélec, Université paris-Saclay, Gif-Sur- Yvette, France; Renault SAS Technocentre, Guyancourt, France
Julie Stal-Le Cardinal
Affiliation:
Laboratoire Génie Industriel, centralSupélec, Université paris-Saclay, Gif-Sur- Yvette, France;
Christine Royer
Affiliation:
Renault SAS Technocentre, Guyancourt, France
*
Contact: Fatfouta, Naouress, Laboratoire Génie Industriel, Design Engineering, France, naouress.fatfouta@centralesupelec.fr

Abstract

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Car crash simulation analysis is an important phase within the vehicle development. It intends to analyse the crashworthiness of the vehicle model and examine the level of passive security. However, this activity is not trivial because of the considerable collaboration within the project, the large amount of analysed and exchanged data and a high exigency. Consequently, a solution to assist, ease and reduce the time of the process is desired.

To study the current practices followed in the car crash simulation analysis an empirical study has been conducted. This study has been applied within the simulation analysis team, in the development phase, within an automotive company. This paper describes a qualitative analysis of the industrial context and diagnoses the dysfunctions in the current practices. This paper also highlights the current challenges encountered in the car crash simulation analysis.

Keywords

Knowledge managementCase studyProcess modelling
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2843 - 2852
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) 2019

References

Brandt, S.C., et al. (2008), “An ontology-based approach to knowledge management in design processes.” Computers & Chemical Engineering, Vol. 32 No. (1-2), pp. 320–342. Available at: https://linkinghub.elsevier.com/retrieve/pii/S0098135407001032Google Scholar
Di Ciccio, C., Marrella, A. and Russo, A., (2015), “Knowledge-Intensive Processes: Characteristics,” Requirements and Analysis of Contemporary Approaches. Journal on Data Semantics, Vol. 4 No. 1, pp. 29–57. Available at: http://link.springer.com/10.1007/s13740-014-0038-4Google Scholar
Kestel, P., et al. (2019), “Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes.” Advanced Engineering Informatics, Vol. 39, pp. 292–305. Available at: https://www.sciencedirect.com/science/article/pii/S1474034618304270Google Scholar
Mazouni, M.-H., Bied-Charreton, D. and Aubry, J.-F., (2007), “Proposal of a generic methodology to harmonize Preliminary Hazard Analyses for guided transport.” In 2007 IEEE International Conference on System of Systems Engineering. IEEE, pp. 1–6. Available at: http://ieeexplore.ieee.org/document/4304278/Google Scholar
Peng, G., et al. (2017), “A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model.” Advanced Engineering Informatics, Vol. 33, pp. 314–329. Available at: https://linkinghub.elsevier.com/retrieve/pii/S1474034616301859.Google Scholar
Rausand, M., (2011), “Risk Assessment: Theory, Methods and Applications H.” Wiley, USA. Available at: http://ebook.rsc.org/?DOI=10.1039/9781847551856.Google Scholar
Sissoko, T.M., et al. (2018), “An Empirical Study of a Decision-Making Process Supported by Simulation in the Automotive Industry.” In Volume Vol. 7: 30th International Conference on Design Theory and Methodology. ASME, p. V007T06A018. Available at: http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/DETC2018-86406.Google Scholar
Wriggers, P., Siplivaya, M. and Irina, Z., (2007), “Integration of a case-based reasoning and an ontological knowledge base in the system of intelligent support of finite element analysis.” Computer Assisted Mechanics and Engineering Sciences, Vol. 14, pp. 753–765.Google Scholar