Abstract
There are new CAD software tools emerging that will likely pave the way to next generation engineering design. They are labelled Generative Engineering, Generative Design, Algorithm Based Design, Simulation Based Design or similar. Those new tools have in common that they make intensive use of Artificial Intelligence methods and extensive computing power. The focus of these algorithm-driven approaches to developing products and finding solutions is not the explicit creation of geometry but rather the definition of constraints, boundary conditions, rules and procedures that allow the computation of feasible solutions including the implicit generation of geometric models. One major idea is to initially open up wide solution spaces that are goal-oriented based on given requirements and provide engineers with all relevant information to perform trade-off studies and create new and innovative solutions instead of perpetuating existing solutions. The new tools are often associated with topology optimization and generative manufacturing, but the concepts go far beyond and lead to a complete workflow in the creation of products.
The paper systematically analyses different software tools and shows that Generative Design and Engineering is interpreted and implemented differently by various software vendors, all pursuing different goals. In addition to the potentials, the paper mainly shows the current limitations of the implemented approaches in the different CAD tools. The focus is not only on the design phase, but also on how the tools take into account different aspects such as the automation of the entire development process, cost evaluation and manufacturing processes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For the sake of simplicity, we will only use the abbreviation GD in the rest of the text.
References
Briard, T., Segonds, F., Zamariola, N.: G-DfAM: a methodological proposal of generative design for additive manufacturing in the automotive industry. Int. J. Interact. Des. Manuf. (IJIDeM) 14(3), 875–886 (2020). https://doi.org/10.1007/s12008-020-00669-6
Jaisawal, R., Agrawal, V.: Generative Design Method (GDM) – a state of art. In: IOP Conference Series: Materials Science and Engineering, vol. 1104 (2021)
Pollák, M., Kočiško, M., Dobránsky, J.: Analysis of software solutions for creating models by a generative design approach. In: IOP Conference Series: Materials Science and Engineering, vol. 1199 (2021)
Harvard Business Review. https://hbr.org/resources/pdfs/comm/autodesk/The.Next.Wave.of.Intelligent.Design.Automation.pdf. Accessed 28 Mar 2022
Caetano, I., Santos, L., Leitão, A.: Computational design in architecture: defining parametric, generative, and algorithmic design. Front. Architectural Res. 9(2), 287–300 (2020)
Babel, N., Metzger, M.: Untersuchung künstlicher Intelligenz im Bereich der Konstruktion mit Generativer Design Software. Hochschule für Angewandte Wissenschaften Landshut (2021)
Autodesk - Generative Design for Manufacturing. https://d1.awsstatic.com/partner-network/partner_marketing_web_team/Manufacturing-partner-solutions_Autodesk_brochure.pdf. Accessed 28 Mar 2022
Autodesk, KUKA: Deciphering Industry 4.0 – Part IV Generative Design (2018)
Generative Design: The Engineering Guide | nTopology. https://ntopology.com/generative-design-guide/. Accessed 08 Mar 2022
ELISE Portal. https://portal.elise.de/#/docs/2012250159/2012250203. Accessed 18 Mar 2022
Autodesk News - How GM and Autodesk are using generative design for vehicles of the future. https://adsknews.autodesk.com/news/gm-autodesk-using-generative-design-vehicles-future. Accessed 25 May 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 IFIP International Federation for Information Processing
About this paper
Cite this paper
Gerhard, D., Köring, T., Neges, M. (2023). Generative Engineering and Design – A Comparison of Different Approaches to Utilize Artificial Intelligence in CAD Software Tools. In: Noël, F., Nyffenegger, F., Rivest, L., Bouras, A. (eds) Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies. PLM 2022. IFIP Advances in Information and Communication Technology, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-031-25182-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-25182-5_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25181-8
Online ISBN: 978-3-031-25182-5
eBook Packages: Computer ScienceComputer Science (R0)