Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T21:41:40.577Z Has data issue: false hasContentIssue false
  • English
  • Français

A 3D, performance-driven generative design framework: automating the link from a 3D spatial grammar interpreter to structural finite element analysis and stochastic optimization

Published online by Cambridge University Press:  09 May 2018

Luca Zimmermann ,
Tian Chen  and
Kristina Shea
Luca Zimmermann
Affiliation:
Engineering Design and Computing Laboratory, EDAC, ETH Zurich, Switzerland
Tian Chen
Affiliation:
Engineering Design and Computing Laboratory, EDAC, ETH Zurich, Switzerland
Kristina Shea*
Affiliation:
Engineering Design and Computing Laboratory, EDAC, ETH Zurich, Switzerland
*
Author for correspondence: Kristina Shea, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Since the introduction of spatial grammars 45 years ago, numerous grammars have been developed in a variety of fields from architecture to engineering design. Their benefits for solution space exploration when computationally implemented and combined with optimization have been demonstrated. However, there has been limited adoption of spatial grammars in engineering applications for various reasons. One main reason is the missing, automated, generalized link between the designs generated by the spatial grammar and their evaluation through finite-element analysis (FEA). However, the combination of spatial grammars with optimization and simulation has the advantage over continuous structural topology optimization in that explicit constraints, for example, modeling style and fabrication processes, can be included in the spatial grammar. This paper discusses the challenges in providing a generalized approach by demonstrating the implementation of a framework that combines a three-dimensional spatial grammar interpreter with automated FEA and stochastic optimization using simulated annealing (SA). Guidelines are provided for users to design spatial grammars in conjunction with FEA and integrate automatic application of boundary conditions. A simulated annealing method for use with spatial grammars is also presented including a new method to select rules through a neighborhood definition. To demonstrate the benefits of the framework, it is applied to the automated design and optimization of spokes for inline skate wheels. This example highlights the advantage of spatial grammars for modeling style and additive manufacturing (AM) constraints within the generative system combined with FEA and optimization to carry out topology and shape optimization. The results verify that the framework can generate structurally optimized designs within the style and AM constraints defined in the spatial grammar, and produce a set of topologically diverse, yet valid design solutions.

Keywords

Automated FEAperformance-driven generative designsimulated annealingspatial grammar3D spatial grammar interpreter
Type
Regular Articles
Information
AI EDAM , Volume 32 , Special Issue 2: Advances in Implemented Shape Grammars: Solutions and Applications , May 2018 , pp. 189 - 199
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agarwal, M and Cagan, J (1996) A blend of different tastes: the language of coffee makers. Environment and Planning B: Planning and Design 25(2), 205–226.CrossRefGoogle Scholar
Ang, MC, Chau, HH, McKay, A and De Pennington, A (2006) Combining evolutionary algorithms and shape grammars to generate branded product design. In Design Computing and Cognition ’06, pp. 521–539.Google Scholar
Antonsson, EK and Cagan, J (2001) Formal Engineering Design Synthesis, 1st edn. New York: Cambridge University Press.Google Scholar
Barros, M, Duarte, JP and Chaparro, BM (2015) A grammar-based model for the mass customisation of chairs: modelling the optimisation part. Nexus Newt Journal 875–898.CrossRefGoogle Scholar
Bar-Yam, Y (2003) When systems engineering fails-toward complex systems engineering. In IEEE International Conference on Systems, Man and Cybernetics, 2003, Vol. 2, pp. 2021–2028. doi: 10.1109/ICSMC.2003.1244709.CrossRefGoogle Scholar
Bendsøe, MP and Sigmund, O (2013) Topology Optimization: Theory, Methods, and Applications. 2nd edn. Berlin: Springer Science & Business Media.Google Scholar
Cagan, J and Mitchell, W (1993) Optimally directed shape generation by shape annealing. Environment and Planning B: Planning and Design 20, 5–12.Google Scholar
Chakrabarti, A, Shea, K, Stone, R, Cagan, J, Campbell, M, Hernandez, NV and Wood, KL (2011) Computer-based design synthesis research: an overview. Journal of Computing and Information Science in Engineering 11(2), 021003-021003–10.CrossRefGoogle Scholar
Chase, SC (2002) A model for user interaction in grammar-based design systems. Automation in Construction 11(2), 161–172.CrossRefGoogle Scholar
Chen, T and Shea, K (2015) Computational Design-To-Fabrication Using Spatial Grammars: Automatically Generating Printable Car Wheel Design Variants. Glasgow: Design Society, pp. 1–10.Google Scholar
Chen, T, Stoeckli, F and Shea, K (2015) Design for Mass Customization Using Additive Manufacture: Case-Study of a Balloon-Powered. Glasgow: Design Society, pp. 1–10.Google Scholar
Chouchoulas, O (2003) Shape Evolution: An Algorithmic Method for Conceptual Architectural Design Combining Shape Grammars and Genetic Algorithms. Bath: University of Bath.Google Scholar
de Boer, RRW, Schermerhorn, P, Gademan, J, de Groot, G and van Ingen Schenau, GJ (1986) Characteristic stroke mechanics of elite and trained male speed skaters. International Journal of Sport Biomechanics 2(3), 175–185.CrossRefGoogle Scholar
de Boer, RRW, Vos, E, Hutter, W, de Groot, G and van Ingen Schenau, GJ (1987a) Physiological and biomechanical comparison of roller skating and speed skating on ice. European Journal of Applied Physiology and Occupational Physiology 56(5), 562–569.CrossRefGoogle ScholarPubMed
de Boer, RRW, Ettema, GJC, van Gorkum, H, de Groot, G and van Ingen Schenau, GJ (1987b) Biomechanical aspects of push-off techniques in speed skating the curves. International Journal of Sport Biomechanics 3(1), 69–79.CrossRefGoogle Scholar
de Koning, JJ, De Boer, RRW, de Groot, G and van Ingen Schenau, GJ (1987) Push-off force in speed skating. International Journal of Sport Biomechanics 3(2), 103–109.Google Scholar
Dowsland, KA and Thompson, JM (2012) Simulated annealing. In Rozenberg, G, Bäck, T and Kok, JN (eds). Handbook of Natural Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 1623–1655.Google Scholar
Ehrenstein, GW and Erhard, G (1984) Designing with Plastics: A Report on the State of the Art, 1st edn. Munich: Hanser Publishers.Google Scholar
Gibson, I, Rosen, DW and Stucker, B (2014) Additive Manufacturing Technologies, 2nd edn. New York: Springer.Google Scholar
Giger, M and Ermanni, P (2005) Development of CFRP racing motorcycle rims using a heuristic evolutionary algorithm approach. Structural and Multidisciplinary Optimization 30(1), 54–65.Google Scholar
Hallihan, GM, Cheong, H and Shu, LH (2012) Confirmation and Cognitive Bias in Design Cognition. Chicago, American Society of Mechanical Engineers, pp. 1–12.Google Scholar
Hoisl, F and Shea, K (2011) An interactive, visual approach to developing and applying parametric three-dimensional spatial grammars. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 25(04), 333–356.Google Scholar
Hoisl, F and Shea, K (2013) Three-dimensional labels: a unified approach to labels for a general spatial grammar interpreter. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27(04), 359–375.Google Scholar
Keleny, LG (1999) In-line Skate Wheel. United States of America, Patent No. US 5860707 A.Google Scholar
Knight, TW (1999) Shape grammars: five questions. Environment and Planning B: Planning and Design 26(4), 477–501.Google Scholar
Konig, O (2004) Evolutionary design optimization. PhD diss., ETH Zurich.Google Scholar
Konigseder, C and Shea, K (2016) Comparing strategies for topologic and parametric rule application in automated computational design synthesis. Journal of Mechanical Design, Transactions of the ASME 138(1), 1–12.CrossRefGoogle Scholar
Krishnamachari, SI (2002) Recommended Factors of Safety and Related Considerations. Bethel: Society of Plastics Engineers.Google Scholar
Krishnamurti, R and Stouffs, R (1993) Spatial Grammars: Motivation, Comparison, and New Results. Pittsburgh, USA: CAAD Futures ‘93, pp. 57–74.Google Scholar
Mascarenhas, WN, Ahrens, CH and Ogliari, A (2004) Design criteria and safety factors for plastic components design. Materials and Design 25(3), 257–261.Google Scholar
Mata, MP, Ahmed-Kristensen, S and Shea, K (2015). Spatial Grammar for Design Synthesis Targeting Perceptions: Case Study on Beauty. Boston, ASME, p. V01AT02A013.Google Scholar
McKay, A, Chase, S, Shea, K and Hing Chau, H (2012) Spatial grammar implementation. From theory to useable software. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26(02), 143–159.Google Scholar
Pugliese, MJ and Cagan, J (2002) Capturing a rebel: modeling the Harley-Davidson brand through a motorcycle shape grammar. Research in Engineering Design 13, 139–156.Google Scholar
Purcell, AT and Gero, JS (1996) Design and other types of fixation. Design Studies 17(4 SPEC. ISS.), 363–383.Google Scholar
Shea, K (1997) Essays of Discrete Structures: Purposeful Design of Grammatical Structures by Directed Stochastic Search, 1st edn. Pittsburgh: Carnegie Mellon University.Google Scholar
Shea, K, Aish, R and Gourtovaia, M (2005) Towards integrated performance-driven generative design tools. Automation in Construction 14(2), 253–264.Google Scholar
Shea, K and Cagan, J (1997) Innovative dome design: applying geodesic patterns with shape annealing. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11(5), 379–394.Google Scholar
Shea, K and Cagan, J (1999) The design of novel roof trusses with shape annealing: assessing the ability of a computational method in aiding structural designers with varying design intent. Design Studies 20(1), 3–23.Google Scholar
Shea, K and Smith, IF (2006) Improving full-scale transmission tower design through topology and shape optimization. Journal of Structural Engineering 132(5), 781–790.Google Scholar
Stearns, J, Srivatsan, T, Gao, X and Lam, PC (2006) Understanding the influence of pressure and radial loads on stress and displacement response of a rotating body: the automobile wheel. International Journal of Rotating Machinery 2006, 1–8.Google Scholar
Stiny, G (1980) Introduction to shape and shape grammars. Environment and Planning B 7, 343–351.CrossRefGoogle Scholar
Stiny, G and Gips, J (1972) Shape grammars and the generative specification of painting and sculpture. Information Processing 71, 1460–1465.Google Scholar
Stiny, G and Mitchell, WJ (1978) The Palladian grammar. Environment and Planning B: Planning and Design 5(1), 5–18.Google Scholar
Stroud, I and Nagy, H (2011) Solid Modelling and CAD Systems, 1st edn. London: Springer London.Google Scholar
Woodbury, R (2010) Elements of Parametric Design, 1st edn. s.l.:Taylor and Francis.Google Scholar
Zimmermann, L, Chen, T and Shea, K (2016) Generative shape design using 3D spatial grammars, simulation and optimization. In Gero, J (ed.). Design Computing and Cognition ’16. Springer, Cham.Google Scholar