4th International Conference on Computational Design and Robotic Fabrication, 2022
This article presents the design process for generating a shell-like structure from an activated ... more This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.
Un diálogo permanente con la arquitectura. Emilio de la Cerda, Francisco Díaz, Francisco Quintana,editors. Santiago de Chile: Ediciones ARQ; 2017. p. 132-133., 2017
Esta es una exploración en torno a cómo los procesos de modelación y producción digital de compon... more Esta es una exploración en torno a cómo los procesos de modelación y producción digital de componentes constructivos pueden generar una "línea de producción arquitectónica" que es, al mismo tiempo, personalizada y masiva.
4th International Conference on Computational Design and Robotic Fabrication, 2022
This article presents the design process for generating a shell-like structure from an activated ... more This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.
Un diálogo permanente con la arquitectura. Emilio de la Cerda, Francisco Díaz, Francisco Quintana,editors. Santiago de Chile: Ediciones ARQ; 2017. p. 132-133., 2017
Esta es una exploración en torno a cómo los procesos de modelación y producción digital de compon... more Esta es una exploración en torno a cómo los procesos de modelación y producción digital de componentes constructivos pueden generar una "línea de producción arquitectónica" que es, al mismo tiempo, personalizada y masiva.
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Papers by Juan Ojeda