| id |
ecaade2018_108 |
| authors |
Luo, Dan, Wang, Jingsong and Xu, Weiguo |
| year |
2018 |
| title |
Applied Automatic Machine Learning Process for Material Computation |
| source |
Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 109-118 |
| doi |
https://doi.org/10.52842/conf.ecaade.2018.1.109
|
| summary |
Machine learning enables computers to learn without being explicitly programmed. This paper outlines state-of-the-art implementations of machine learning approaches to the study of physical material properties based on Elastomer we developed, which combines with robotic automation and image recognition to generate a computable material model for non-uniform linear Elastomer material. The development of the neural network includes a few preliminary experiments to confirm the feasibility and the influential parameters used to define the final RNN neural network, the study of the inputs and the quality of the testing samples influencing the accuracy of the output model, and the evaluation of the generated material model as well as the method itself. To conclude, this paper expands such methods to the possible architectural implications on other non-uniform materials, such as the performance of wood sheets with different grains and tensile material made from composite materials. |
| keywords |
neural network; robotic; material computation; automation |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (5,191,715 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:59 |
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