Hostname: page-component-848d4c4894-cjp7w Total loading time: 0 Render date: 2024-07-04T20:19:58.393Z Has data issue: false hasContentIssue false
  • English
  • Français

OBSERVATIONS ON THE IMPLICATIONS OF GENERATIVE DESIGN TOOLS ON DESIGN PROCESS AND DESIGNER BEHAVIOUR

Published online by Cambridge University Press:  19 June 2023

Jana Saadi  and
Maria Yang
Jana Saadi*
Affiliation:
Massachusetts Institute of Technology
Maria Yang
Affiliation:
Massachusetts Institute of Technology
*
Saadi, Jana, Massachusetts Institute of Technology, United States of America, [email protected]

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.

Developments in artificial intelligence (AI) are opening the possibilities for the development of more advanced design tools. An example of these innovations are generative design tools, in which the generation of complex and high performing products is possible. This study investigates the use of generative design tools and how they may influence the design process and designer behaviour. Six interviews of interdisciplinary designers were conducted to understand the implications of using generative design tools. It was observed that generative design tools primarily allow for quantitative inputs to the tool while qualitative metrics, such as aesthetics, are considered indirectly by designers. The subjectivity of the designer and how they incorporate the quantitative and qualitative metrics in the generative design tool can lead to differing outcomes between designers. Notable differences in tool usage are also observed between expert and novice computational designers. Additional studies should be conducted to further understand the extent generative design tools impact the design process, designer behaviour, and design outcomes.

Keywords

Design processArtificial intelligenceGenerative DesignHuman behaviour in designDesign Tools
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2805 - 2814
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), 2023. Published by Cambridge University Press

References

Alcaide-Marzal, J., Diego-Mas, J.A. and Acosta-Zazueta, G. (2020), “A 3D Shape Generative Method for Aesthetic Product Design”, Design Studies, Vol. 66, pp. 144–176, https://dx.doi.org/10.1016/j.destud.2019.11.003.CrossRefGoogle Scholar
Bansal, G., Nushi, B., Kamar, E., Weld, D.S., Lasecki, W.S. and Horvitz, E. (2019), “Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff”, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33 No. 01, pp. 2429–2437, https://dx.doi.org/10.1609/aaai.v33i01.33012429.CrossRefGoogle Scholar
Buonamici, F., Carfagni, M., Furferi, R., Volpe, Y. and Governi, L. (2020), “Generative Design: An Explorative Study”, Computer-Aided Design and Applications, Vol. 18, pp. 144–155, https://dx.doi.org/10.14733/cadaps.2021.144-155.CrossRefGoogle Scholar
Caetano, I., Santos, L. and Leitão, A. (2020), “Computational design in architecture: Defining parametric, generative, and algorithmic design”, Frontiers of Architectural Research, Vol. 9 No. 2, pp. 287–300, https://dx.doi.org/10.1016/j.foar.2019.12.008.CrossRefGoogle Scholar
Charmaz, K. (2008), “Grounded Theory as an Emergent Method”, Handbook of Emergent Methods, The Guilford Press, New York, NY, US, pp. 155–170.Google Scholar
Crang, M. and Cook, I. (2007), Doing Ethnographies, SAGE Publications Ltd, London.CrossRefGoogle Scholar
Creswell, J., Clark, V., Gutmann, M. and Hanson, W. (2003), “Advance Mixed methods Research Designs”, Handbook of Mixed Methods in Social and Behavioral Research, pp. 209–240.Google Scholar
Cui, J. and Tang, M. (2017), “Towards Generative Systems for Supporting Product Design”, International Journal of Design Engineering, Vol. 7, p. 1, https://dx.doi.org/10.1504/IJDE.2017.085639.CrossRefGoogle Scholar
Friedman, K. (2003), “Theory Construction in Design Research: Criteria: Approaches, and Methods”, Design Studies, Vol. 24 No. 6, pp. 507–522, https://dx.doi.org/10.1016/S0142-694X(03)00039-5.CrossRefGoogle Scholar
Gascón Alvarez, E., Stamler, N.L., Mueller, C.T. and Norford, L.K. (2022), “Shape optimization of chilled concrete ceilings – Reduced embodied carbon and enhanced operational performance”, Building and Environment, Vol. 221, p. 109330, https://dx.doi.org/10.1016/j.buildenv.2022.109330.CrossRefGoogle Scholar
“Generative Design at Airbus | Customer Stories | Autodesk”. (n.d.)., available at: https://www.autodesk.com/customer-stories/airbus (accessed 14 November 2022).Google Scholar
Gyory, J.T., Song, B., Cagan, J. and McComb, C. (2021), “Communication in AI-Assisted Teams During an Interdisciplinary Drone Design Problem”, Proceedings of the Design Society, Cambridge University Press, Vol. 1, pp. 651–660, https://dx.doi.org/10.1017/pds.2021.65.CrossRefGoogle Scholar
Holzer, D., Dominik, Hough, Richard, Burry, and Mark, M.. (2007), “Parametric Design and Structural Optimisation for Early Design Exploration”, International Journal of Architectural Computing Vol. 5 - No. 4, Pp. 625–643, Vol. 5, https://dx.doi.org/10.1260/147807707783600780.CrossRefGoogle Scholar
Krish, S. (2011), “A Practical Generative Design Method”, Computer-Aided Design, Vol. 43 No. 1, pp. 88–100, https://dx.doi.org/10.1016/j.cad.2010.09.009.CrossRefGoogle Scholar
Lauff, C., Kotys-Schwartz, D. and Rentschler, M. (2018), “What is a Prototype? What are the Roles of Prototypes in Companies?”, Journal of Mechanical Design, Vol. 140, https://dx.doi.org/10.1115/1.4039340.CrossRefGoogle Scholar
Li, Z. and Seering, W. (2019), “Build Your Firm With Strangers?: Longitudinal Studies on Open Source Hardware Firm Growth”, Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, presented at the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Anaheim CA, USA, https://dx.doi.org/10.1115/DETC2019-97618.CrossRefGoogle Scholar
Lo, C.-H. (2018), “Application of Aesthetic Principles to the Study of Consumer Preference Models for Vase Forms”, Applied Sciences, Multidisciplinary Digital Publishing Institute, Vol. 8 No. 7, p. 1199, https://dx.doi.org/10.3390/app8071199.Google Scholar
Lopez, C.E., Miller, S.R. and Tucker, C.S. (2018), “Exploring Biases Between Human and Machine Generated Designs”, Journal of Mechanical Design, Vol. 141 No. 2, https://dx.doi.org/10.1115/1.4041857.Google Scholar
Noor, A.K. (2017), “AI and the Future of the Machine Design”, Mechanical Engineering, Vol. 139 No. 10, pp. 38–43, https://dx.doi.org/10.1115/1.2017-Oct-2.CrossRefGoogle Scholar
Oh, S., Jung, Y., Kim, S., Lee, I. and Kang, N. (2019), “Deep Generative Design: Integration of Topology Optimization and Generative Models”, Journal of Mechanical Design, Vol. 141 No. 11, https://dx.doi.org/10.1115/1.4044229.CrossRefGoogle Scholar
Pillai, P.P., Burnell, E., Wang, X. and Yang, M.C. (2020), “Effects of Robust Convex Optimization on Early-Stage Design Space Exploratory Behavior”, Journal of Mechanical Design, Vol. 142 No. 12, https://dx.doi.org/10.1115/1.4048580.CrossRefGoogle Scholar
Saadi, J.I. and Yang, M.C. (2023), “Generative Design: Reframing the Role of the Designer in Early-Stage Design Process”, Journal of Mechanical Design, Vol. 145 No. 4, https://dx.doi.org/10.1115/1.4056799.CrossRefGoogle Scholar
Saldana, J. (2015), The Coding Manual for Qualitative Researchers, 3rd ed., SAGE Publications Ltd.Google Scholar
Simpson, T.W., Jiao, J. (Roger), Siddique, Z. and Hölttä-Otto, K. (2014), Advances in Product Family and Product Platform Design: Methods & Applications, 1st ed., Springer New York, NY.CrossRefGoogle Scholar
Song, B., Soria Zurita, N., Zhang, G., Stump, G., Balon, C., Miller, S., Yukish, M., et al. (2020), “Toward Hybrid Teams: A Platform to Understand Human-computer Collaboration during the Design of Complex Engineered Systems”, Proceedings of the Design Society: DESIGN Conference, Vol. 1, presented at the International Design Conferenece, pp. 1551–1560, https://dx.doi.org/10.1017/dsd.2020.68.CrossRefGoogle Scholar
Thomas, D.R. (2006), “A General Inductive Approach for Analyzing Qualitative Evaluation Data”, American Journal of Evaluation, Vol. 27 No. 2, pp. 237–246, https://dx.doi.org/10.1177/1098214005283748.CrossRefGoogle Scholar
Ulrich, K.T., Eppinger, S.D. and Yang, M.C. (2019), Product Design and Development, McGraw Hill Education.Google Scholar
Vlah, D., Žavbi, R. and Vukašinović, N. (2020), “Evaluation of Topology Optimization and Generative Design Tools as Support for Conceptual Design”, Proceedings of the Design Society: DESIGN Conference, Cambridge University Press, Vol. 1, pp. 451–460, https://dx.doi.org/10.1017/dsd.2020.165.Google Scholar
Yümer, M.E., Chaudhuri, S., Hodgins, J. and Kara, L. (2015), “Semantic Shape Editing Using Deformation Handles”, ACM Trans. Graph., Vol. 34 No. 4, p. 12, https://dx.doi.org/10.1145/2766908.CrossRefGoogle Scholar
Zhang, G., Raina, A., Cagan, J. and McComb, C. (2021), “A Cautionary Tale about the Impact of AI on Human Design Teams”, Design Studies, Vol. 72, p. 100990, https://dx.doi.org/10.1016/j.destud.2021.100990.CrossRefGoogle Scholar