Predicting and steering performance in architectural materials

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

Predicting and steering performance in architectural materials

Publications: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

214 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	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
Editors	José Pedro Sousa, Joäo Pedro Xavier, Goncalo Castro Henriques
Number of pages	10
Place of Publication	Porto
Publication date	2019
Pages	485-494
Article number	150
Publication status	Published - 2019
Event	eCAADe: Architecture in the Age of the 4th Industrial Revolution - University of Porto (FAUP), Faculty of Architecture, Porto, Portugal Duration: 11 Sept 2019 → 13 Sept 2019 https://ecaadesigradi2019.arq.up.pt/

Conference

Conference	eCAADe
Location	University of Porto (FAUP), Faculty of Architecture
Country/Territory	Portugal
City	Porto
Period	11/09/2019 → 13/09/2019
Internet address	https://ecaadesigradi2019.arq.up.pt/

Series	ECAADE SIGRADI 2019 Architecture in the age of the 4th Industrial revolution

Keywords

computational design
material specification
machine learning
convolution algorithm
knit

Artistic research

No

Access to Document

Predicting and steering performance in architectural materialsFinal published version, 15.4 MB

http://papers.cumincad.org/cgi-bin/works/BrowseTree=series=AZ/Show?ecaadesigradi2019_150

Cite this

Ramsgaard Thomsen, M., Nicholas, P., Tamke, M., Gatz, S., & Sinke, Y. (2019). Predicting and steering performance in architectural materials. In J. P. Sousa, J. P. Xavier, & G. Castro Henriques (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). Article 150. http://papers.cumincad.org/cgi-bin/works/BrowseTree=series=AZ/Show?ecaadesigradi2019_150

Ramsgaard Thomsen, Mette ; Nicholas, Paul ; Tamke, Martin et al. / Predicting and steering performance in architectural materials. 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. editor / José Pedro Sousa ; Joäo Pedro Xavier ; Goncalo Castro Henriques. Porto, 2019. pp. 485-494 (ECAADE SIGRADI 2019 Architecture in the age of the 4th Industrial revolution).

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title = "Predicting and steering performance in architectural materials",

abstract = "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.",

keywords = "Computation, CNC Knit, Machine learning, Textile, Material behaviour, Digital fabrication, computational design, material specification, machine learning, convolution algorithm, knit",

author = "\{Ramsgaard Thomsen\}, Mette and Paul Nicholas and Martin Tamke and Sebastian Gatz and Yuliya Sinke",

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pages = "485--494",

editor = "Sousa, \{Jos{\'e} Pedro\} and Xavier, \{Jo{\"a}o Pedro\} and \{Castro Henriques\}, Goncalo",

booktitle = "Architecture in the Age of the 4th Industrial Revolution",

note = "eCAADe : Architecture in the Age of the 4th Industrial Revolution ; Conference date: 11-09-2019 Through 13-09-2019",

url = "https://ecaadesigradi2019.arq.up.pt/",

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Ramsgaard Thomsen, M , Nicholas, P , Tamke, M , Gatz, S & Sinke, Y 2019, Predicting and steering performance in architectural materials. in JP Sousa, JP Xavier & G Castro Henriques (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., 150, Porto, ECAADE SIGRADI 2019 Architecture in the age of the 4th Industrial revolution, pp. 485-494, eCAADe, Porto, Portugal, 11/09/2019. <http://papers.cumincad.org/cgi-bin/works/BrowseTree=series=AZ/Show?ecaadesigradi2019_150>

Predicting and steering performance in architectural materials. / Ramsgaard Thomsen, Mette ; Nicholas, Paul ; Tamke, Martin et al.
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. ed. / José Pedro Sousa; Joäo Pedro Xavier; Goncalo Castro Henriques. Porto, 2019. p. 485-494 150 (ECAADE SIGRADI 2019 Architecture in the age of the 4th Industrial revolution).

Publications: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AB - 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.

KW - Computation

KW - CNC Knit

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KW - Material behaviour

KW - Digital fabrication

KW - computational design

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KW - machine learning

KW - convolution algorithm

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Ramsgaard Thomsen M , Nicholas P , Tamke M , Gatz S , Sinke Y. Predicting and steering performance in architectural materials. In Sousa JP, Xavier JP, Castro Henriques G, editors, 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. Porto. 2019. p. 485-494. 150. (ECAADE SIGRADI 2019 Architecture in the age of the 4th Industrial revolution).