- Open Access.

Idioma principal | Segundo idioma

Predicting and steering performance in architectural materials

Predicting and steering performance in architectural materials

Thomsen, Mette; Nicholas, Paul; Tamke, Martin; Gatz, Sebastian; Sinke, Yuliya;

:

This paper presents the prototyping of new methods by which functionally gradedmaterials can be specified and produced. The paper presents a case studyexploring how machine learning can be used to train a model in order to predictfabrication files from formalised design requirements. By using knit as a modelfor material fabrication, the paper outlines the making of new cyclical designmethods employing machine learning in which simpler prototypical materials actsas input for more complex graded materials. A case study - Ombre - showcasesthe implementation of this workflow and results and perspectives are discussed.

:

This paper presents the prototyping of new methods by which functionally gradedmaterials can be specified and produced. The paper presents a case studyexploring how machine learning can be used to train a model in order to predictfabrication files from formalised design requirements. By using knit as a modelfor material fabrication, the paper outlines the making of new cyclical designmethods employing machine learning in which simpler prototypical materials actsas input for more complex graded materials. A case study - Ombre - showcasesthe implementation of this workflow and results and perspectives are discussed.

Palavras-chave: ,

Palavras-chave: ,

DOI: 10.5151/proceedings-ecaadesigradi2019_150

Referências bibliográficas
  • [1] .
Como citar:

Thomsen, Mette; Nicholas, Paul; Tamke, Martin; Gatz, Sebastian; Sinke, Yuliya; "Predicting and steering performance in architectural materials", p. 485-494 . In: Proceedings of 37 eCAADe and XXIII SIGraDi Joint Conference, “Architecture in the Age of the 4Th Industrial Revolution”, Porto 2019, Sousa, José Pedro; Henriques, Gonçalo Castro; Xavier, João Pedro (eds.). São Paulo: Blucher, 2019.
ISSN 2318-6968, DOI 10.5151/proceedings-ecaadesigradi2019_150

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações