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Generative Product Design Processes: Humans and Machines Towards a Symbiotic Balance

Published online by Cambridge University Press:  26 May 2022

M. Tufarelli*
Affiliation:
University of Florence, Italy
E. Cianfanelli
Affiliation:
University of Florence, Italy

Abstract

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Design processes managed by algorithms provide solutions and improvements in terms of efficiency, performance, choice of materials, and cost optimization. It is a whole new approach to industrial design in which artificial intelligence participates directly in the design processes. The paper aims to investigate the way we design through algorithms, and consequent changes in thoughts, approaches, and generation of ideas that are rising determining new ways of defining things and their relations.

Type
Article
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

Caetano, I.; Santos, L.; Leitão, A. Computational design in architecture: Defining parametric, generative, and algorithmic design. Front. Arch. Res. 2020, 9, 287300.Google Scholar
Cantamessa, M., Montagna, F., Altavilla, S., & Casagrande-Seretti, A. (2020). Data-driven design: the new challenges of digitalization on product design and development. Design Science, 6.Google Scholar
D.W. Rosen A review of synthesis methods for additive manufacturing Virtual Phys. Prototyp., 11 (4) (2016), pp. 305317Google Scholar
D.W. Rosen Research supporting principles for design for additive manufacturing Virtual Phys. Prototyp., 9 (4) (2014), pp. 225232Google Scholar
Dhokia, V., Essink, W. P., & Flynn, J. M. (2017). A generative multi-agent design methodology for additively manufactured parts inspired by termite nest building. CIRP Annals, 66(1), 153156.Google Scholar
Goldschmidt, G. (2014). Linkography unfolding the design process. Cambridge, Massachusetts: MIT Press.CrossRefGoogle Scholar
Kennedy, E., Fecheyr-Lippens, D., Hsiung, B. K., Niewiarowski, P. H., & Kolodziej, M. (2015). Biomimicry: A path to sustainable innovation. Design Issues, 31(3), 6673.CrossRefGoogle Scholar
La Rocca, G. (2012). Knowledge based engineering: Between AI and CAD. Review of a language based technology to support engineering design. Advanced engineering informatics, 26(2), 159179.CrossRefGoogle Scholar
Loy, J., & Novak, J. I. (2021). The future of product design education Industry 4.0. In Research Anthology on Cross-Industry Challenges of Industry 4.0 (pp. 16661685). IGI Global.Google Scholar
Thompson, M.K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R.I., Gibson, I., Bernard, A., Schulz, J., Graf, P., Ahuja, B., Martina, F. Design for Additive Manufacturing: trends, opportunities, considerations, and constraints CIRP Ann., 65 (2) (2016), pp. 737760Google Scholar
Magistretti, S., Dell'Era, C., & Petruzzelli, A. M. (2019). How intelligent is Watson? Enabling Friedman, K. (2003). Theory construction in design research: Criteria: Approaches and methods. Design Studies, 24, 507522.Google Scholar
Maglic, M. J. (2012). Biomimicry: using nature as a model for design.Google Scholar
McKnight, Matthew, (2017), ”Generative Design: What it is? How is it Being Used? Why it's a Game Changer!,” in The International Conference on Design and Technology, KEG, pages 176181. https://dx.doi.org/10.18502/keg.v2i2.612Google Scholar
Meyer, M., Wiederkehr, I., Koldewey, C., Dumitrescu, R. (2021) ‘Understanding Usage Data-Driven PrIoCdEucDt 2P1lanning: A Systematic Literature Review’, in Proceedings of the International Conference on Engineering 1 Design (ICED21), Gothenburg, Sweden, 16-20 August 2021.https://dx.doi.org/10.1017/pds.2021.590Google Scholar
Lebaal, N., Zhang, Y., Demoly, F., Roth, S., Gomes, S., A. Bernard Optimised lattice structure configuration for additive manufacturing CIRP Ann., 69 (1) (2019), pp. 117120Google Scholar
Rodrigues, E., Soares, N., Fernandes, M. S., Gaspar, A. R., Gomes, Á., & Costa, J. J. (2018). An integrated energy performance-driven generative design methodology to foster modular lightweight steel framed dwellings in hot climates. Energy for sustainable development, 44, 2136.Google Scholar
Oh, S. J., Lee, Yongsu, Ikjin Kang, Namwoo, “Deep Generative Design: Integration of Topology Optimization and Generative Models,” Journal of Mechanical Design, 2018.Google Scholar
Standard, A., 2012. Standard terminology for additive manufacturing technologies, ASTM International F2792-12a.Google Scholar
Vaneker, T., Bernard, A., Moroni, G., Gibson, I., Y. Zhang Design for additive manufacturing: framework and methodology CIRP Ann., 69 (2020), pp. 578599Google Scholar
Tavsan, F., & Sonmez, E. (2015). Biomimicry in furniture design. Procedia-social and behavioral sciences, 197, 22852292.Google Scholar
Teresko, J. (1993). Parametric Technology Corp.: Changing the way Products are Designed. Industry Week, December 20.Google Scholar
Volstad, N. L., & Boks, C. (2012). On the use of Biomimicry as a Useful Tool for the Industrial Designer. Sustainable Development, 20(3), 189199.CrossRefGoogle Scholar
Wang, Z., Zhang, Y., & Bernard, A. (2021). A constructive solid geometry-based generative design method for additive manufacturing. Additive Manufacturing, 41, 101952.CrossRefGoogle Scholar
Xiong, Y., Duong, P.L.T., Wang, D., Park, S.-I., Ge, Q., Raghavan, N., D.W. Rosen Data-driven design space exploration and exploitation for design for additive manufacturing J. Mech. Des., 141 (10) (2019)CrossRefGoogle Scholar