| id |
ecaade2020_515 |
| authors |
Chadha, Kunaljit, Dubor, Alexandre, Puigpinos, Laura and Rafols, Irene |
| year |
2020 |
| title |
Space Filling Curves for Optimising Single Point Incremental Sheet Forming using Supervised Learning Algorithms |
| source |
Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 555-562 |
| doi |
https://doi.org/10.52842/conf.ecaade.2020.1.555
|
| summary |
Increasing use of computational design tools have led to an increase in the demand for mass customised fabrication, rendering decades old industrial CAD-CAM protocols limiting for such fabrication processes. This bespoke demand of components has led to a unified workflow between design strategies and production techniques. Recent advances in computation have allowed us to predict and register the tolerances of fabrication before and while being fabricated. Procedural algorithms are a set of novel problem-solving methods and have been attracting considerable attention for their good performance.They follow a procedural way of iteration with an established way of behavior.In the particular case of Incremental Sheet forming (ISF), these algorithms can realize several functions such as edge detection and segmentation required for optimizing machining time and accuracy.In this context, this paper presents a methodology to optimize long-drawn-out ISF operation by using geometrical intervention informed by supervised machine learning algorithms. |
| keywords |
Procedural Algorithms; Incremental Sheet Forming; Robotic Cold forming; Mass Customization |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (4,013,236 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:55 |
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