Browsing by Author "Vivanco Larraín, Tomas"

Now showing 1 - 5 of 5
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    Biomateria Austral
    (Editorial Blucher, 2022) Vivanco Larraín, Tomas; Garmulewicz, Alysia; Pacheco Glen, Carolina; Aguilar, Tania; Bolumburu, Pilar; Pontificia Universidad Católica de Chile. Escuela de Diseño; Tongji University; Universidad de Santiago de Chile
    Este proyecto permitió el desarrollo de un nuevo biomaterial capaz de reemplazar determinados plásticos a partir de desechos de la biomasa de la caparazón de centollas, desarrollando una tecnología de bajo costo para extraer la quitina y con ella crear recetas de nuevos biomateriales. En todo el proceso se trabajó de forma colaborativa con la comunidad local, en estrecha relación con los procesos biológicos del ecosistema de la Isla Navarino. Diseñando una nueva cadena de valor basada en la articulación de las tecnologías, comunidades y ecosistemas.
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    Master's in Distributed Design and Innovation
    (Distributed Design, 2023) Diez, Tomás; Nano, josefina; Vivanco Larraín, Tomas; Fab Lab Barcelona; Institute of Advanced Architecture of Catalonia, Barcelona; Pontificia Universidad Católica de Chile. Escuela de Diseño; Instituto de Arquitectura Avanzada, Cataluña
    MDDI is a practical program based on emergent theoretical approaches founded on the experience of Fab Lab Barcelona, IAAC, the Global Fab Lab Network along with worldwide researchers and practitioners. It connects faculty and students from all over the world within a distributed infrastructure that includes communication and fabrication technologies; a 21st century distributed classroom that nurtures digital-physical relationships, diversity, globalisation and localisation.
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    Regression-Based Inductive Reconstruction of Shell Auxetic Structures
    (2023) Vivanco Larraín, Tomas; Ojeda Valenzuela, Juan Eduardo; Yuan, Philip; Pontificia Universidad Católica de Chile. Escuela de Diseño; Tongji University; Technical University Darmstadt
    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.
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    Restaurante Fidelio
    Vivanco Larraín, Tomas
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    Restaurante Roberta
    Vivanco Larraín, Tomas