Hostname: page-component-7f64f4797f-6fdxz Total loading time: 0 Render date: 2025-11-04T13:11:03.189Z Has data issue: false hasContentIssue false
  • English
  • Français

Procedure to Create an Automated Design Environment for Functional Assemblies

Published online by Cambridge University Press:  26 May 2022

A. Osman ,
Y. Kutay ,
I. Mozgova  and
R. Lachmayer
A. Osman*
Affiliation:
Leibniz Universität Hannover, Germany
Y. Kutay
Affiliation:
Leibniz Universität Hannover, Germany
I. Mozgova
Affiliation:
Leibniz Universität Hannover, Germany
R. Lachmayer
Affiliation:
Leibniz Universität Hannover, Germany
*
altun@ipeg.uni-hannover.de

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.

Manually exploring the solution space for different variants of a product for a given set of requirements is ineffective regarding product development time and adaptation to dynamic customer requirements. Variant generation coupled to optimization algorithms offers possibilities to search the solution space in an automated way. This paper provides a framework to build a generative parametric design environment for functional assemblies by implementing analysis as well as synthesis methods in computer-aided tools. The procedure is presented using the example of a coffee machine.

Keywords

generative designknowledge-based engineering (KBE)design optimisationcomputer-aided design (CAD)

Information

Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 553 - 562
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), 2022.

References

Amadori, K. et al. . (2012), “Flexible and robust CAD models for design automation”, Advanced Engineering Informatics, Vol. 26 No. 1, pp. 180–195. 10.1016/j.aei.2012.01.004CrossRefGoogle Scholar
Bellemare, J.; Carrier, S.; Nielsen, K.; Piller, F. T. (2017), “Managing Complexity”, Springer International Publishing, Cham.Google Scholar
Boyle, I., Rong, Y. and Brown, D. (2011), “A review and analysis of current computer aided fixture design approaches”, Robotics and Computer-Integrated Manufacturing, Vol. 27 No. 1, pp. 1–12. 10.1016/j.rcim.2010.05.008Google Scholar
Bursać, N. (2016), Model Based Systems Engineering as a support for the Modular Design in the Context of the Early Stages of Product Generation Engineering, Karlsruhe, PhD-Thesis, 2016.Google Scholar
Chakrabarti, A. et al. . (2011), “Computer-based design synthesis research: an overview”, Journal of Computing and Information Science in Engineering, Vol. 11 No. 2, p. 021003. 10.1115/1.3593409Google Scholar
Cui, J. and Tang, M. (2017), “Towards generative systems for supporting product design”, International Journal of Design Engineering, Vol. 7 No. 1, pp. 1–16. 10.1504/IJDE.2017.085639Google Scholar
Eigner, M., Roubanov, D., Zafirov, R., Modellbasierte virtuelle Produktentwicklung. 1. Aufl. Berlin, Heidelberg: Springer-Verlag, 2014.CrossRefGoogle Scholar
Gembarski, P.C. , Bibani, Mehdi, and Lachmayer, Roland (2016) “Design Catalogues: Knowledge Repositories for Knowledge-Based-Engineering Applications.”, in Marjanović, Dorian, Štorga, Mario, Pavković, Neven, Bojčetić, Nenad and Stanko, Škec (eds) Proceedings of the DESIGN 2016 14th International Design Conference, Glasgow, The Design SocietyGoogle Scholar
Gembarski, P.C. (2020), “On the conception of a Multi-Agent Analysis and Optimization Tool for Mechanical Engineering Parts”. Agents and Multi-Agent Systems: Technologies and Applications, Smart Innovation, Systems and Technologies, vol. 186.2020, pp. 93–102, 2020. https://dx.doi.org/10.1007/978-981-15-5764-4_9Google Scholar
Herrmann, K., Altun, O., Wolniak, P., Mozgova, I., Lachmayer, R., “Methodischer Aufbau von Entwicklungsumgebungen nach dem Generative Parametric Design Approach.” Proceedings of the 32nd Symposium Design for X (DFX2021). 10.35199/dfx2021.14Google Scholar
Hirz, M. et al. . (2013), Integrated computer-aided design in automotive development, Springer, Graz. 10.1007/978-3-642-11940-8Google Scholar
Hoffmann, C. (2005), “Constraint-based computer-aided design”, Journal of Computing and Information Science in Engineering, Vol. 5 No. 3, pp. 182–187. 10.1115/1.1979508Google Scholar
Holder, Kevin; Rudolph, Stephan; Stetter, Ralf; et al: Automated requirements-driven design synthesis of gearboxes with graph-based design languages using state of the art tools. Forsch Ingenieurwes 83, 655–668, 2019. 10.1007/s10010-019-00322-z.Google Scholar
Kim, B. and Han, S. (2007), “Integration of history-based parametric translators using the automation APIs”, International Journal of Product Lifecycle Management, Vol. 2 No. 1, pp. 18–29. 10.1504/IJPLM.2007.012872Google Scholar
Krish, S. (2011), “A practical generative design method”, Computer Aided Design, Vol. 43 No. 1, pp. 88–100. 10.1016/j.cad.2010.09.009CrossRefGoogle Scholar
La Rocca, G., Van Tooren, M., “A Knowledge Based Engineering Approach to Support Automatic Generation of FE Models in Aircraft Design,” In: 45th AIAA Aerospace Sciences Meeting and Exhibit, 2007Google Scholar
Li, H. and Lachmayer, R. (2019), “Automated Exploration of Design Solution Space Applying the Generative Design Approach”, Proceedings of the Design Society: International Conference on Engineering Design, Cambridge University Press, Vol. 1, No. 1, pp. 1085–1094. 10.1017/dsi.2019.114Google Scholar
Liddicoat, D. E.: An Automated Interface for CATIA and MATLAB with an Op-timisation Capability. University of New South Wales at the Australian De-fence Force Academy; 2016.Google Scholar
Milton, Nick.R. (2008), Knowledge technologies. Monza, Polimetrica sas.Google Scholar
Oh, S., Jung, Y., Kim, S., Lee, I., and Kang, N. (2019). “Deep Generative Design: Integration of Topology Optimization and Generative Models.” ASME. J. Mech. Des. November 2019; 141(11): 111405. 10.1115/1.4044229Google Scholar
Sabin, Daniel, and Weigel, Rainer (1998) “Product configuration frameworks - a survey.” IEEE intelligent systems 13(4): 42–49.Google Scholar
Schreiber, G., Wielinga, B., De Hoog, R., Akkermans, H., Van de Velde, W. (1994) “CommonKADS: A comprehensive methodology for KBS development.” IEEE expert 9(6): 28–37.Google Scholar
Schleich, B., Wartzack, S., “A generic approach to sensitivity analysis in geometric variations management.” DS 80-4 Proceedings of the 20th International Conference on Engineering Design (ICED 15 Vol 4: Design for X, Design to X, Milan, Italy, 27-30.07, 2015.Google Scholar
Stokes, M. (2001), Managing Engineering Knowledge - MOKA: Methodology for Knowledge Based Engineering Applications, Professional Engineering Publishing Limited, London.Google Scholar
Vajna, S., Weber, C., Zeman, K., Hehenberger, P., Gerhard, D., Wartzack, S. (2018), CAx für Ingenieure – Eine praxisbezogene Einführung. 3. Aufl. Berlin: Springer-Verlag, 2018.CrossRefGoogle Scholar
Verhagen, Wim .J.C., Bermell-Garcia, Pablo, van Dijk, Reinier E.C., and Curran, Richard (2012) “A critical review of Knowledge-Based Engineering: An identification of research challenges.” Advanced Engineering Informatics 26(1): 5–15.Google Scholar
Wolniak, P.; Klookschreiber, D.; Sauthoff, B.; Lachmayer, R., “Integrating Architectural Design Changes in Computer-Aided Design Optimization.” Internation Conference on Mass Customization and Personalization - Community of Europe (MCP-CE 2020).Google Scholar
Yin, C. and Ma, Y. (2012), “Parametric feature constraint modeling and mapping in product development”, Advanced Engineering Informatics, Vol. 26 No. 3, pp. 539–552. 10.1016/j.aei.2012.02.010CrossRefGoogle Scholar