| id |
cdrf2019_309 |
| authors |
Yuliya Sinke, Sebastian Gatz, Martin Tamke, and Mette Ramsgaard Thomsen |
| year |
2020 |
| title |
Machine Learning for Fabrication of Graded Knitted Membranes |
| source |
Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020) |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_29
|
| summary |
This paper examines the use of machine learning in creating digitally integrated design-to-fabrication workflows. As computational design allows for new methods of material specification and fabrication, it enables direct functional grading of material at high detail thereby tuning the design performance in response to performance criteria. However, the generation of fabrication data is often cumbersome and relies on in-depth knowledge of the fabrication processes. Parametric models that set up for automatic detailing of incremental changes, unfortunately, do not accommodate the larger topological changes to the material set up. The paper presents the speculative case study KnitVault. Based on earlier research projects Isoropia and Ombre, the study examines the use of machine learning to train models for fabrication data generation in response to desired performance criteria. KnitVault demonstrates and validates methods for shortcutting parametric interfacing and explores how the trained model can be employed in design cases that exceed the topology of the training examples. |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (7,950,992 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/09/29 07:51 |
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