Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-12-01T03:57:18.718Z Has data issue: false hasContentIssue false
  • English
  • Français

Automated Exploration of Design Solution Space Applying the Generative Design Approach

Part of: Design Management

Published online by Cambridge University Press:  26 July 2019

Haibing Li  and
Roland Lachmayer

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.

Design is a complex problem-solving activity that transforms design restrictions and requirements into a set of constraints and explores the feasible solutions to satisfy those constraints. However, design solutions generated by traditional modeling approaches are hardly to deal with such constraints, particularly for the exploration of the possible design solution space to enhance the quality of the design outputs and confront the evolving design requirements. In this regard, the Generative Design Approach (GDA) is considered as an efficient method to explore a large design solution space by transforming the design problem into a configuration problem. Fundamentally, GDA explores and stores all the necessary knowledge through a design skeleton and a set of design elements. Thus, design solution space is easily explored by configuring variable design elements via iterative design processes. Further, the output model is not only a design solution but also a design concept that designers could manipulate to explore unconsidered design configurations. Finally, a crank creation as a running example confirmed that GDA provides concrete aids to enhance the diversity of design solutions.

Keywords

Computer Aided Design (CAD)Product modelling / modelsDesign methodsGenerative design approachDesign solution space exploration
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1085 - 1094
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

Akle, A.A., Yannou, B. and Minel, S. (2017), “Design space visualization for efficiency in knowledge discovery leading to an informed decision”, 21st International Conference on Engineering Design (ICED17).Google Scholar
Alfaris, A.A.F., 2009. Emergence through conflict: the Multi-Disciplinary Design System (MDDS), Doctoral dissertation, Massachusetts Institute of Technology.Google Scholar
Bodein, Y., Rose, B. and Caillaud, E. (2014), “Explicit reference modeling methodology in parametric CAD system”, Computers in Industry, Vol. 65 No. 1, pp. 136–147. https://doi.org/10.1016/j.compind.2013.08.004Google Scholar
Gembarski, P., Li, H., Lachmayer, R. (2015), “KBE-Modeling Techniques in Standard CAD-Systems: Case Study-Autodesk Inventor Professional”, In: Bellemare, J., Carrier, S., Nielsen, K., Piller, F. (Ed.), Managing Complexity. Springer, Cham, pp. 215–233. https://doi.org/10.1007/978-3-319-29058-4_17Google Scholar
Gembarski, P., Li, H. and Lachmayer, R. (2017), “Template-Based Modelling of Structural Components”, International Journal of Mechanical Engineering and Robotics Research, Vol. 6 No. 5, pp. 336–342.Google Scholar
Gembarski, P.C. and Lachmayer, R. (2018), “The Parameter Space Matrix as Planning Tool for Geometry-based Solution Spaces”, Proceedings of the 8th International Conference on Mass Customization and Personalization - Community of Europe (MCP - CE 2018), Novi Sad, Serbien, 19.09.-21.09.2018.Google Scholar
Hoffmann, C. (2005), “Constraint-based computer-aided design”, Journal of Computing and Information Science in Engineering, Vol. 5 No. 3, pp. 182–187. https://doi.org/10.1115/1.1979508Google Scholar
Jubierre, J. and Borrmann, A. (2015), “Knowledge-based engineering for infrastructure facilities: assisted design of railway tunnels based on logic models and advanced procedural geometry dependencies”, Journal of Information Technology in Construction, Vol. 20 No. 26, pp. 421–441.Google Scholar
Kang, E., Jackson, E. and Schulte, W. (2010), “An approach for effective design space exploration”, Monterey Workshop (pp. 33–54). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21292-5_3Google Scholar
Kansakar, P. and Munir, A. (2018), “A Design Space Exploration Methodology for Parameter Optimization in Multicore Processors”, IEEE Transactions on Parallel and Distributed Systems, Vol. 29 No. 1, pp. 2–15. https://doi.org/10.1109/tpds.2017.2745580.Google Scholar
Li, H. and Lachmayer, R. (2018), “Generative Design Approach for Modeling Creative Designs”, In IOP Conference Series: Materials Science and Engineering, Vol. 408 No. 1, pp. 012035. https://doi.org/10.1088/1757-899X/408/1/012035Google Scholar
Li, H., Gembarski, P.C. and Lachmayer, R. (2018), “Template-based design for design co-creation”, In DS 89: Proceedings of The Fifth International Conference on Design Creativity (ICDC 2018), University of Bath, Bath, UK (pp. 387–394).Google Scholar
Lippert, B. (2018), Restriktionsgerechtes Gestalten gewichtsoptimierter Strukturbauteile für das Selektive Laserstrahlschmelzen, PhD thesis, Leibniz Universität Hannover.Google Scholar
Renno, F. and Papa, S. (2015), “Direct Modeling Approach to Improve Virtual Prototyping and FEM Analyses of Bicycle Frames”, Engineering Letters, Vol. 23 No. 4.Google Scholar
Sauthoff, B. (2017), Generative Parametrische Modellierung von Strukturkomponenten für die Technische Vererbung, PhD thesis, Leibniz Universität Hannover.Google Scholar
Shahin, T. (2008), “Feature-based design - an overview”, Computer-Aided Design and Applications, Vol. 5 No. 5, pp. 639–653. https://doi.org/10.3722/cadaps.2008.639-653Google Scholar
Sunnersjo, S. (2016), “Industrial Products and How They Are Developed”, In: Intelligent Computer Systems in Engineering Design, Springer, Cham. https://doi.org/10.1007/978-3-319-28125-4Google Scholar
Thompson, M. and Pimentel, A.D. (2013), “Exploiting domain knowledge in system-level MPSoC design space exploration”, Journal of Systems Architecture, Vol. 59 No. 7, pp. 351–360. https://doi.org/10.1016/j.sysarc.2013.05.023Google Scholar
Turrin, M., Von Buelow, P. and Stouffs, R. (2011), “Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms”, Advanced Engineering Informatics, Vol. 25 No. 4, pp. 656–675. https://doi.org/10.1016/j.aei.2011.07.009Google Scholar
Park, K. and Holt, N. (2010), “Parametric design process of a complex building in practice using programmed code as master model”, International Journal of Architectural Computing, Vol. 8 No. 3, pp. 359–376. https://doi.org/10.1260/1478-0771.8.3.359Google Scholar
Vaillant, M. (2016), Design Space Exploration zur multikriteriellen Optimierung elektrischer Sportwagen-antriebsstränge, PhD thesis, Karlsruher Institut für Technologie.Google Scholar
Vajna, S., Kittel, K. and Bercsey, T. (2010), “Designing the solution space for the autogenetic design theory (ADT)”. In DS 60: Proceedings of DESIGN 2010, Dubrovnik, Croatia (pp. 1441–1450).Google Scholar
VDI 2209 (2009), 3D product modelling. Technical and organizational requirements, Beuth Verlag, Berlin.Google Scholar
VDI 2218 (2003), Information technology in product development: feature technology, Beuth Verlag, Berlin.Google Scholar
VDI 5610-2 (2017), Knowledge management for engineering: knowledge-based engineering, Beuth Verlag, Berlin.Google Scholar
Verhagen, W., Bermell-Garcia, P., van Dijk, R. 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. https://doi.org/10.1016/j.aei.2011.06.004Google Scholar
Weber, C. (2011), “Design Theory and Methodology-Contributions to the Computer Support of Product Development / Design Processes”, In: The Future of Design Methodology (pp. 91–104), Springer, London.Google Scholar