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A New Shape Generation Framework Based on Machine Learning and Topology Optimization
Proceedings of International Conference on Engineering, Science and Technology 2021
Nikos Ath. Kallioras, Nikos D. Lagaros
Dr. Mack Shelley, Dr. Valarie Akerson
978-1-952092-24-4
48-59
Lately, an algorithmic tool, known as Generative Design, that supports products’ design has been introduced in several industries with manufacturing procedures. Generative Design can be described as the technology that focuses on the generation of plethora of designs that all respect designer-set criteria such as loading conditions, support conditions, etc. Due to this feature, generative design can be used as an intuition creating tool that actually suggests several rough prototypes to the designer who can use them as inspiration for the final prototype. In this work, a novel shape generation framework based on topology optimization, machine learning and image editing is proposed, aiming at performing generative design in architectural design. In detail, the proposed framework constitutes a combination of Solid Isotropic Material with Penalization (SIMP) (Bendsøe, 1989), Long Short-Term Memory networks (LSTM) (Hochreiter & Schmidhuber, 1997) and various image filters. SIMP is used for optimizing the shape according to the designer’s criteria and constraints while LSTMs and image filtering are used for shape differentiation and process acceleration. The proposed framework is tested over a number of topology optimization problems used as benchmark tests in modern literature.
Kallioras, N. A. & Lagaros, N. D. (2021). A new shape generation framework based on machine learning and topology optimization. In M. Shelley & V. Akerson (Eds.), Proceedings of IConEST 2021-- International Conference on Engineering, Science and Technology (pp. 48-59), Chicago, USA. ISTES Organization.
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