| id |
cdrf2022_488 |
| authors |
Tomás Vivanco, Juan Eduardo Ojeda, Philip Yuan |
| year |
2022 |
| title |
Regression-Based Inductive Reconstruction of Shell Auxetic Structures |
| source |
Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022) |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_42
|
| summary |
This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes. |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (464,901 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/05/29 14:03 |
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