| 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 |
| 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 |
| doi |
https://doi.org/10.52842/conf.ecaade.2019.2.485
|
| 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
|
Domingos, P (2012)
 A Few Useful Things to Know about Machine Learning
, Communications of the ACM, 55(Issue 10), pp. . 78-87
|
|
|
|
|
Gero, J (1991)
Ten problems for AI in design
, IJCAI-91, Workshop on AI in Design
|
|
|
|
|
Goodfellow, I, Pouget-Abadie, J, Mirza, M, Xu, B, Warde-Farley, D, Ozair, S, Courville, A and Bengio, Y (2014)
Generative Adversarial Nets, in Advances
, Neural Information Processing Systems, pp. 2672-2680
|
|
|
|
|
Han, C, Murao, K., Satoh, S and Nakayama, H (2019)
Learning More with Less: GAN-based Medical Image Augmentation
, arXiv:1904.00838
|
|
|
|
|
Huang, H, Yu, P and Wang, C (2018)
An Introduction to Image Synthesis with Generative Adversarial Nets
, arXiv:1803.04469 Search... Help | Advanced Search
|
|
|
|
|
Huang, W and Zheng, H (2018)
Architectural Drawings Recognition and Generation through Machine Learning
, ACADIA 2018: Recalibration On imprecision and infidelity, pp. 156-165
|
|
|
|
|
Isola, P, Zhu, J, Zhou, T and Efros, A A (2017)
Image-to-image Translation with Conditional Adversarial Network
, arXiv preprint. arXiv:1611.07004
|
|
|
|
|
Nicholas, P, Zwierzycki, M, N?rgaard, D, Ramsgaard Thomsen, M and Hutchinson, C (2017)
Adaptive Robotic Fabrication For Conditions Of Material Inconsistency: Increasing The Geometric Accuracy Of Incrementally Formed Metal Panels
, Fabricate 2017, pp. 114-121
|
|
|
|
|
Ramsgaard Thomsen, M and Hicks, T (2008)
To Knit a Wall, Knit as Matrix for Composite Materials for Architecture
, Proceedings Ambience 2008, Bor?s, Sweden, p. 107-114.
|
|
|
|
|
Ramsgaard Thomsen, M and Karmon, A (2012)
Informing Material Specification
, Digital Aptitudes + Other Openings: ACSA 100th Annual Meeting, Boston, p. 677-683
|
|
|
|
|
Ramsgaard Thomsen, M, Nicholas, P, Tamke, M and Svilans, T (2019)
A New Material Vision
, Diniz, N (eds), Data and Matter, Routledge
|
|
|
|
|
Ramsgaard Thomsen, M, Sinke, Y, Montero, F, Lienhard, J, La Magna, R and Tamke, M (2019)
Systems for Transformative Structure in CNC Knitted Fabrics - Isoropia
, Proceedings Tensinet: Softening the Habitats
|
|
|
|
|
Ramsgaard Thomsen, M, Tamke, M, Holden Deleuran, A, Friis Tinning, I K, Leander Evers, H, Gengnagel, C and Schmeck, M (2015)
Hybrid Tower, Designing Soft Structures
, Design Modelling Symposium: Modelling Behaviour, Copenhagen, p. 87-100
|
|
|
|
|
Ramsgaard Thomsen, M, Tamke, M, Karmon, A, Underwood, J, Gengnagel, C, Stranghöner, N and Uhlemann, J (2016)
Knit as bespoke material practice for architecture
, ACADIA 2016 Proceedings
|
|
|
|
|
Rossi, G and Nicholas, P (2018)
Re/Learning the Wheel: Methods to utilize neural networks as design tools for doubly curved metal surfaces
, Acadia 2018 Re/calibration: on imprecision and infidelity, pp. 146-155
|
|
|
|
|
Schmidhuber, J (2015)
Deep Learning in Neural Networks: An Overview
, Neural Networks, 61, pp. 85-117
|
|
|
|
|
Spencer, D (eds) (1989)
Knitting Technology: A comprehensive handbook and practical guide
, Woodhead Publishing Limited, Cambridge, England
|
|
|
|
|
Spencer, D (2001)
Knitting technology: a comprehensive handbook and practical guide. 3. ed.
, Technomic Publishing, Lancaster
|
|
|
|
|
Valero, A and Valero, A (2010)
Physical geonomics: Combining the exergy and Hubbert peak analysis for predicting mineral resources depletion
, Resources, Conservation and Recycling, 54(12), p. 1074-1083
|
|
|
|
|
Zwierzycki, M, Nicholas, P and Ramsgaard Thomsen, M (2017)
Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming
, Proceedings of Design Modelling Symposium 2017, pp. 373-382
|
|
|
|
| last changed |
2022/06/07 07:56 |
|
