Hostname: page-component-89b8bd64d-ksp62 Total loading time: 0 Render date: 2026-05-09T20:59:20.620Z Has data issue: false hasContentIssue false
  • English
  • Français

MULTI-DOMAIN DESIGN ASSESSMENT FOR AEROSPACE COMPONENTS INCLUDING WELD ACCESSIBILITY

Published online by Cambridge University Press:  27 July 2021

Ola Isaksson ,
Timos Kipouros ,
Julian Martinsson ,
Massimo Panarotto ,
Jonas Kressin ,
Petter Andersson  and
John P. Clarkson
Ola Isaksson*
Affiliation:
Chalmers university of technology
Timos Kipouros
Affiliation:
University of Cambridge
Julian Martinsson
Affiliation:
Chalmers university of technology
Massimo Panarotto
Affiliation:
Chalmers university of technology
Jonas Kressin
Affiliation:
Fraunhofer-Chalmers Research Centre for Industrial Mathematics
Petter Andersson
Affiliation:
GKN Aerospace Engines
John P. Clarkson
Affiliation:
University of Cambridge
*
Isaksson, Ola, Chalmers University of Technology, Product and Production Development, Sweden, ola.isaksson@chalmers.se

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Aeroengine manufacturers need to better include assessment of risk and cost for realising the novel products needed to meet the ambitions sustainability driven targets for air transport. Radical technologies are needed that simultaneously require critical manufacturing processes to be assessed already in conceptual design.

In this paper, a multi-domain framework for conceptual design and evaluation is proposed that provide the ability to interactively explore the concepts that simultaneously allow a wider range of architectures can be assessed and still include weldability of the concepts.

It has been demonstrated how high level, and function driven conceptual design alternatives can be modelled and evaluated to analyse risk and resilience of architectures. Geometrical concepts generated for the most interesting regimes using design of experiments covering a desired design space. For each CAD-model the welding process can be simulated to assess feasibility and lead time for welding, and return quantified results to be included in an integrated results data set for interactive decision making. The paper is the first report from a research project that improve concurrent design of product and production concepts.

Keywords

Design for X (DfX)Conceptual designMulti- / Cross- / Trans-disciplinary processesEarly design phases

Information

Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2217 - 2226
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), 2021. Published by Cambridge University Press

References

Amadori, K., Tarkian, M., Ölvander, J. and Krus, P. (2012), “Flexible and robust CAD models for design automation”, Advanced Engineering Informatics, Vol. 26 No. 2, pp. 180–195.CrossRefGoogle Scholar
Clarkson, P.J., Simons, C. and Eckert, C. (2004), “Predicting Change Propagation in Complex Design”, Journal of Mechanical Design, Vol. 126 No. 5, pp. 788–797, 10.1115/1.1765117.CrossRefGoogle Scholar
Clean sky (2020), “Sustainable and Green Engines (SAGE)”. https://www.cleansky.eu/sustainable-and-green-engines-sageGoogle Scholar
Eisenbart, B., Gericke, K., Blessing, L.T. and McAloone, T.C. (2017), “A dsm-based framework for integrated function modelling: concept, application and evaluation”, Research in Engineering Design, Vol. 28 No. 1, pp. 25–51.CrossRefGoogle Scholar
Jiao, J.R., Simpson, T.W. and Siddique, Z. (2007), “Product family design and platform-based product development: a state-of-the-art review”, Journal of Intelligent Manufacturing, Vol. 18 No. 1, pp. 5–29.Google Scholar
Keller, R., Eckert, C. and Clarkson, P.J. (2009), “Using an engineering change methodology to support conceptual design”, Journal of Engineering Design, Vol. 20 No. 6, pp. 571–587.CrossRefGoogle Scholar
Landahl, J., Levandowski, C., Johannesson, H., Söderberg, R., Wärmefjord, K., Carlson, J.S., Kressin, J., Isaksson, O. and Vallhagen, J. (2016), “Using product and manufacturing system platforms to generate producible product variants”, Procedia CIRP, Vol. 44, pp. 61–66.CrossRefGoogle Scholar
Madrid, J., Lorin, S., Söderberg, R., Hammersberg, P., Wärmefjord, K. and Lööf, J. (2019), “A virtual design of experiments method to evaluate the effect of design and welding parameters on weld quality in aerospace applications”, Aerospace, Vol. 6 No. 6, p. 74.CrossRefGoogle Scholar
Mark, A., Bohlin, R., Segerdahl, D., Edelvik, F. and Carlson, J.S. (2014), “Optimisation of robotised sealing stations in paint shops by process simulation and automatic path planning”, International Journal of Manufacturing Research, Vol. 9 No. 1, pp. 4–26, 10.1504/IJMR.2014.059597.CrossRefGoogle Scholar
Müller, J.R., Isaksson, O., Landahl, J., Raja, V., Panarotto, M., Levandowski, C. and Raudberget, D. (2019), “Enhanced function-means modeling supporting design space exploration”, AI EDAM, Vol. 33 No. 4, pp. 502–516.Google Scholar
Müller, J.R., Panarotto, M. and Isaksson, O. (2020), “Design space exploration of a jet engine component using a combined object model for function and geometry”, Aerospace, Vol. 7 No. 12, 10.3390/aerospace7120173.CrossRefGoogle Scholar
Otto, K., Levandowski, C., Forslund, A., Johannesson, H. and Söderberg, R. (2013), “Uncertainty modeling to enable software development platforms that can automate complex mechanical systems design”, Proceedings of the International Conference on Engineering Design, ICED, Vol. 9 DS75-09, pp. 247–256.Google Scholar
Pagliuca, G., Kipouros, T. and Savill, M. (2019), “Surrogate modelling for wing planform multidisciplinary optimisation using model-based engineering”, International Journal of Aerospace Engineering, Vol. 2019.CrossRefGoogle Scholar
Piotrowski, W., Kipouros, T. and Clarkson, P.J. (2019), “Enhanced interactive parallel coordinates using machine learning and uncertainty propagation for engineering design”, in: 2019 15th International Conference on eScience (eScience), pp. 339–348, 10.1109/eScience.2019.00045.CrossRefGoogle Scholar
Raja, V., Kokkolaras, M. and Isaksson, O. (2019), “A simulation-assisted complexity metric for design optimization of integrated architecture aero-engine structures”, Structural and Multidisciplinary Optimization, Vol. 60 No. 1, pp. 287–300.CrossRefGoogle Scholar
Söderberg, R., Wärmefjord, K., Madrid, J., Lorin, S., Forslund, A. and Lindkvist, L. (2018), “An information and simulation framework for increased quality in welded components”, CIRP Annals, Vol. 67 No. 1, pp. 165–168, https://doi.org/10.1016/j.cirp.2018.04.118.CrossRefGoogle Scholar
Segeborn, J., Segerdahl, D., Ekstedt, F., Carlson, J.S., Andersson, M., Carlsson, A. and Söderberg, R. (2013), “An Industrially Validated Method for Weld Load Balancing in Multi Station Sheet Metal Assembly Lines”, Journal of Manufacturing Science and Engineering, Vol. 136 No. 1, 10.1115/1.4025393. 011002.Google Scholar
Straßer, W. and Seidel, H.P. (1989), Theory and Practice of Geometric Modeling, Springer, Berlin Heidelberg, 10.1007/978-3-642-61542-9.CrossRefGoogle Scholar
Verhagen, W.J., Bermell-Garcia, P., Van Dijk, R.E. and Curran, R. (2012), “A critical review of Knowledge-Based Engineering: An identification of research challenges”, Advanced Engineering Informatics, Vol. 26 No. 1, pp. 5–15, 10.1016/j.aei.2011.06.004.CrossRefGoogle Scholar