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Generative design

From Wikipedia, the free encyclopedia
"Algorithmic design" redirects here. For design of algorithms, see Algorithm § Design.
Schema of generative design as an iterative process
Samba, a piece of furniture created by Guto Requena with generative design

Generative design is an iterative design process that uses software to generate outputs that fulfill a set of constraints iteratively adjusted by a designer. Whether a human, test program, or artificial intelligence, the designer algorithmically or manually refines the feasible region of the program's inputs and outputs with each iteration to fulfill evolving design requirements.[1] By employing computing power to evaluate more design permutations than a human alone is capable of, the process is capable of producing an optimal design that mimics nature's evolutionary approach to design through genetic variation and selection.[citation needed] The output can be images, sounds, architectural models, animation, and much more. It is, therefore, a fast method of exploring design possibilities that is used in various design fields such as art, architecture, communication design, and product design.[2]

Generative design has become more important, largely due to new programming environments or scripting capabilities that have made it relatively easy, even for designers with little programming experience, to implement their ideas.[3] Additionally, this process can create solutions to substantially complex problems that would otherwise be resource-exhaustive with an alternative approach making it a more attractive option for problems with a large or unknown solution set.[4] It is also facilitated with tools in commercially available CAD packages.[5] Not only are implementation tools more accessible, but also tools leveraging generative design as a foundation.[6]

Generative design in architecture

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Generative design in architecture is an iterative design process that enables architects to explore a wider solution space with more possibility and creativity.[7] Architectural design has long been regarded as a wicked problem.[8] Compared with traditional top-down design approach, generative design can address design problems efficiently, by using a bottom-up paradigm that uses parametric defined rules to generate complex solutions. The solution itself then evolves to a good, if not optimal, solution.[9] The advantage of using generative design as a design tool is that it does not construct fixed geometries, but take a set of design rules that can generate an infinite set of possible design solutions. The generated design solutions can be more sensitive, responsive, and adaptive to the problem.

Generative design involves rule definition and result analysis which are integrated with the design process.[10] By defining parameters and rules, the generative approach is able to provide optimized solution for both structural stability and aesthetics. Possible design algorithms include cellular automata, shape grammar, genetic algorithm, space syntax, and most recently, artificial neural network. Due to the high complexity of the solution generated, rule-based computational tools, such as finite element method and topology optimisation, are more preferable to evaluate and optimise the generated solution.[11] The iterative process provided by computer software enables the trial-and-error approach in design, and involves architects interfering with the optimisation process.

Historical precedent work includes Antoni Gaudí's Sagrada Família, which used rule based geometrical forms for structures,[12] and Buckminster Fuller's Montreal Biosphere where the rules to generate individual components is designed, rather than the final product.[13]

More recent generative design cases include Foster and Partners' Queen Elizabeth II Great Court, where the tessellated glass roof was designed using a geometric schema to define hierarchical relationships, and then the generated solution was optimized based on geometrical and structural requirement.[14]

Generative design in sustainable design

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Generative design in sustainable design is an effective approach addressing energy efficiency and climate change at the early design stage, recognizing buildings contribute to approximately one-third of global greenhouse gas emissions and 30%-40% of total building energy use.[15] It integrates environmental principles with algorithms, enabling exploration of countless design alternatives to enhance energy performance, reduce carbon footprints, and minimize waste.

A key feature of generative design in sustainable design is its ability to incorporate Building Performance Simulations (BPS) into the design process. Simulation programs like EnergyPlus, Ladybug Tools, and so on, combined with generative algorithms, can optimize design solutions for cost-effective energy use and zero-carbon building designs. For example, Luisa's GENE_ARCH system used a Pareto algorithm with DOE2.1E building energy simulation for the whole building design optimization.[16] Generative design has improved sustainable facade design, as illustrated by Jieun’s cellular automata and daylight simulations in adaptive facade design.[17] Apart from fenestration design, Faridaddin et al. proposed genetic algorithms and radiation simulations for energy-efficient PV modules on high-rise building facades.[18] In addition to energy use, generative design is also applied to life cycle analysis (LCA), as demonstrated by Sally’s framework using grid search algorithms to optimize exterior wall design for minimum environmental embodied impact. [19]

Multi-objective optimization embraces multiple diverse sustainability goals, such as Seyed’s interactive kinetic louvers using biomimicry and daylight simulations to enhance daylight, visual comfort and electric lighting energy efficiency simultaneously,[20] and Neri’s PV and shading system that can maximize on-site electricity and improve visual quality and daylight performance.[21]

AI and machine learning (ML) further improve computation efficiency in complex climate-responsive sustainable design. Soowon et al. employed reinforcement learning to identify the relationship between design parameters and energy use for sustainable campus, [22] while Zhongqi et al. used the genetic algorithm and GANs to balance daylight illumination and thermal comfort under different roof conditions.[23] Zhen et al. used deep reinforcement learning (DRL) and computer vision (CV) to generate an urban block according to direct sunlight hours and solar heat gains. [24] These AI-driven generative design methods enable faster simulations and design decision making, resulting in designs that are both visually appealing and environmentally responsible.

Generative design in additive manufacturing

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Additive manufacturing (AM) is a process that creates physical models directly from 3D data by joining materials layer by layer. It is used in industries to produce a variety of end-use parts, which are final components designed for direct application in products or systems. AM provides design flexibility and enables material reduction in lightweight applications, such as aerospace, automotive, medical, and portable electronic devices, where minimizing weight is critical for performance. Generative design, one of the four key methods for lightweight design in AM, is commonly applied to optimize structures for specific performance requirements.[25]

Generative design can help create optimized solutions that balance multiple objectives, such as enhancing performance while minimizing cost.[26] In design for additive manufacturing (DfAM), multi-objective topology optimization is used to generate a set of candidate solutions. Designers then assess these options using their expertise and key performance indicators (KPIs) to select the best option for implementation.[25]

However, integrating AM constraints (e.g.,speed of build, materials, build envelope, and accuracy) into generative design remains challenging, as ensuring all solutions are valid is complex.[25] Balancing multiple design objectives while limiting computational costs adds further challenges for designers.[27] To overcome these difficulties, researchers proposed a generative design method with manufacturing validation to improve decision-making efficiency. This method starts with a constructive solid geometry (CSG)-based technique to create smooth topology shapes with precise geometric control. Then, a genetic algorithm is used to optimize these shapes, and the method offers designers a set of top non-dominated solutions on the Pareto front for further evaluation and final decision-making.[27] By combining multiple techniques, this method can generate many high-quality solutions with smooth boundaries at lower computational costs, making it a practical approach for designing lightweight structures in AM.

Building on topology optimization methods, software providers introduced generative design features in their tools, helping designers set criteria and rank solutions.[25] Industry is driving advancements in generative design for AM, highlighting the need for tools that not only offer a range of solution choices but also streamline workflows for industrial use.[26]

See also

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References

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  1. ^ Meintjes, Keith. ""Generative Design" – What's That? - CIMdata". Retrieved 2018-06-15.
  2. ^ ENGINEERING.com. "Generative Design: The Road to Production". www.engineering.com. Retrieved 2019-12-05.
  3. ^ Schwab, Katharine (16 April 2019). "This is the first commercial chair made using generative design". Fast Company. Retrieved 13 August 2019.
  4. ^ Prasanta, Rajamoney, Shankar A. Rosenbloom, Paul S.; Wagner, Chris Bose (2014-09-04). Compositional model-based design: A generative approach to the conceptual design of physical systems. University of Southern California. OCLC 1003551283.{{cite book}}: CS1 maint: multiple names: authors list (link)
  5. ^ Barbieri, Loris; Muzzupappa, Maurizio (2022). "Performance-Driven Engineering Design Approaches Based on Generative Design and Topology Optimization Tools: A Comparative Study". Applied Sciences. 12 (4): 2106. doi:10.3390/app12042106.
  6. ^ Anderson, Fraser; Grossman, Tovi; Fitzmaurice, George (2017-10-20). Trigger-Action-Circuits: Leveraging Generative Design to Enable Novices to Design and Build Circuitry. ACM. pp. 331–342. doi:10.1145/3126594.3126637. ISBN 9781450349819. S2CID 10091635.
  7. ^ Krish, Sivam (2011). "A practical generative design method". Computer-Aided Design. 43 (1): 88–100. doi:10.1016/j.cad.2010.09.009.
  8. ^ Rittel, Horst W. J.; Webber, Melvin M. (1973). "Dilemmas in a General Theory of Planning" (PDF). Policy Sciences. 4 (2): 155–169. doi:10.1007/bf01405730. S2CID 18634229. Archived from the original (PDF) on 30 September 2007.
  9. ^ Mitchell, Melanie; Taylor, Charles E (1999). "Evolutionary computation: an overview". Annual Review of Ecology and Systematics. 30 (1): 593–616. Bibcode:1999AnRES..30..593M. doi:10.1146/annurev.ecolsys.30.1.593.
  10. ^ Shea, Kristina; Aish, Robert; Gourtovaia, Marina (2005). "Towards integrated performance-driven generative design tools". Automation in Construction. 14 (2): 253–264. doi:10.1016/j.autcon.2004.07.002.
  11. ^ Dapogny, Charles; Faure, Alexis; Michailidis, Georgios; Allaire, Grégoire; Couvelas, Agnes; Estevez, Rafael (2017). "Geometric constraints for shape and topology optimization in architectural design" (PDF). Computational Mechanics. 59 (6): 933–965. Bibcode:2017CompM..59..933D. doi:10.1007/s00466-017-1383-6. S2CID 41570887.
  12. ^ Hernandez, Carlos Roberto Barrios (2006). "Thinking parametric design: introducing parametric Gaudi". Design Studies. 27 (3): 309–324. doi:10.1016/j.destud.2005.11.006.
  13. ^ Edmondson, Amy C (2012). "Structure and pattern integrity". A Fuller explanation: The synergetic geometry of R. Buckminster Fuller (PDF). Springer Science & Business Media. pp. 54–60. doi:10.1007/978-1-4684-7485-5. ISBN 978-0-8176-3338-7.
  14. ^ Williams, Chris JK (2001). Burry, Mark; Datta, Sambit; Dawson, Anthony; Rollo, John (eds.). The analytic and numerical definition of the geometry of the British Museum Great Court Roof (PDF). Proceedings of mathematics & design 2001: the third international conference. Vol. 200. Geelong Vic Australia: Deakin University. pp. 434–440. ISBN 0-7300-2526-8.
  15. ^ Suphavarophas, Phattranis; Wongmahasiri, Rungroj; Keonil, Nuchnapang; Bunyarittikit, Suphat (May 2024). "A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings". Buildings. 14 (5): 1311. doi:10.3390/buildings14051311. ISSN 2075-5309.
  16. ^ Caldas, Luisa (2008-01-01). "Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system". Advanced Engineering Informatics. Intelligent computing in engineering and architecture. 22 (1): 59–70. doi:10.1016/j.aei.2007.08.012. ISSN 1474-0346.
  17. ^ Kim, Jieun (2013-04-21). "Adaptive façade design for the daylighting performance in an office building: the investigation of an opening design strategy with cellular automata". International Journal of Low-Carbon Technologies. 10 (3): 313–320. doi:10.1093/ijlct/ctt015. ISSN 1748-1317.
  18. ^ Vahdatikhaki, Faridaddin; Salimzadeh, Negar; Hammad, Amin (2022-03-01). "Optimization of PV modules layout on high-rise building skins using a BIM-based generative design approach". Energy and Buildings. 258: 111787. Bibcode:2022EneBu.25811787V. doi:10.1016/j.enbuild.2021.111787. ISSN 0378-7788.
  19. ^ Hassan, Sally R.; Megahed, Naglaa A.; Abo Eleinen, Osama M.; Hassan, Asmaa M. (2022-07-15). "Toward a national life cycle assessment tool: Generative design for early decision support". Energy and Buildings. 267: 112144. Bibcode:2022EneBu.26712144H. doi:10.1016/j.enbuild.2022.112144. ISSN 0378-7788.
  20. ^ Hosseini, Seyed Morteza; Heiranipour, Milad; Wang, Julian; Hinkle, Laura Elizabeth; Triantafyllidis, Georgios; Attia, Shady (2024-05-19). "Enhancing Visual Comfort and Energy Efficiency in Office Lighting Using Parametric-Generative Design Approach for Interactive Kinetic Louvers". Journal of Daylighting. 11 (1): 69–96. doi:10.15627/jd.2024.5. ISSN 2383-8701.
  21. ^ Banti, Neri; Ciacci, Cecilia; Bazzocchi, Frida; Di Naso, Vincenzo (September 2024). "Enhancing Industrial Buildings' Performance through Informed Decision Making: A Generative Design for Building-Integrated Photovoltaic and Shading System Optimization". Solar. 4 (3): 401–421. doi:10.3390/solar4030018. hdl:2158/1375896. ISSN 2673-9941.
  22. ^ Chang, Soowon; Saha, Nirvik; Castro-Lacouture, Daniel; Yang, Perry Pei-Ju (2019-09-01). "Multivariate relationships between campus design parameters and energy performance using reinforcement learning and parametric modeling". Applied Energy. 249: 253–264. Bibcode:2019ApEn..249..253C. doi:10.1016/j.apenergy.2019.04.109. ISSN 0306-2619.
  23. ^ Yu, Zhongqi; Ge, Xinyi; Fan, Zhaoxiang; Zhou, Yihang; Lin, Dawei (2024-10-15). "Optimization framework for daylight and thermal environment of retractable roof natatoriums based on generative adversarial network and genetic algorithm". Energy and Buildings. 321: 114695. doi:10.1016/j.enbuild.2024.114695. ISSN 0378-7788.
  24. ^ Han, Zhen; Yan, Wei; Liu, Gang (2021). "A Performance-Based Urban Block Generative Design Using Deep Reinforcement Learning and Computer Vision". In Yuan, Philip F.; Yao, Jiawei; Yan, Chao; Wang, Xiang; Leach, Neil (eds.). Proceedings of the 2020 DigitalFUTURES. Singapore: Springer. pp. 134–143. doi:10.1007/978-981-33-4400-6_13. ISBN 978-981-334-400-6.
  25. ^ a b c d Vaneker, Tom; Bernard, Alain; Moroni, Giovanni; Gibson, Ian; Zhang, Yicha (2020-01-01). "Design for additive manufacturing: Framework and methodology". CIRP Annals - Manufacturing Technology. 69 (2): 578–599. doi:10.1016/j.cirp.2020.05.006. ISSN 0007-8506.
  26. ^ a b Plocher, János; Panesar, Ajit (2019-12-05). "Review on design and structural optimisation in additive manufacturing: Towards next-generation lightweight structures". Materials & Design. 183: 108164. doi:10.1016/j.matdes.2019.108164. ISSN 0264-1275.
  27. ^ a b Wang, Zhiping; Zhang, Yicha; Bernard, Alain (2021-05-01). "A constructive solid geometry-based generative design method for additive manufacturing". Additive Manufacturing. 41: 101952. doi:10.1016/j.addma.2021.101952. ISSN 2214-8604.

Further reading

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