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CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
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_id ecaadesigradi2019_150
id ecaadesigradi2019_150
authors Thomsen, Mette, Nicholas, Paul, Tamke, Martin, Gatz, Sebastian and Sinke, Yuliya
year 2019
title Predicting and steering performance in architectural materials
doi https://doi.org/10.52842/conf.ecaade.2019.2.485
source Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 485-494
summary This paper presents the prototyping of new methods by which functionally graded materials can be specified and produced. The paper presents a case study exploring how machine learning can be used to train a model in order to predict fabrication files from formalised design requirements. By using knit as a model for material fabrication, the paper outlines the making of new cyclical design methods employing machine learning in which simpler prototypical materials acts as input for more complex graded materials. A case study - Ombre - showcases the implementation of this workflow and results and perspectives are discussed.
keywords computational design; material specification; machine learning; convolution algorithm; knit
series eCAADeSIGraDi
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last changed 2022/06/07 07:56

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