| id |
caadria2018_083 |
| authors |
Luo, Dan, Wang, Jinsong and Xu, Weiguo |
| year |
2018 |
| title |
Robotic Automatic Generation of Performance Model for Non-Uniform Linear Material via Deep Learning |
| source |
T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 39-48 |
| doi |
https://doi.org/10.52842/conf.caadria.2018.1.039
|
| summary |
In the following research, a systematic approach is developed to generate an experiment-based performance model that computes and customizes properties of non-uniform linear materials to accommodate the form of designated curve under bending and natural force. In this case, the test subject is an elastomer strip of non-uniform sections. A novel solution is provided to obtain sufficient training data required for deep learning with an automatic material testing mechanism combining robotic arm automation and image recognition. The collected training data are fed into a deep combination of neural networks to generate a material performance model. Unlike most traditional performance models that are only able to simulate the final form from the properties and initial conditions of the given materials, the trained neural network offers a two-way performance model that is also able to compute appropriate material properties of non-uniform materials from target curves. This network achieves complex forms with minimal and effective programmed materials with complicated nonlinear properties and behaving under natural forces. |
| keywords |
Material performance model; Deep Learning; Robotic automation; Material computation; Neural network |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (16,001,962 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:59 |
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