| 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 |
| email |
|
| full text |
 file.pdf (16,129,718 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:56 |
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