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Functional Generative Design: An Evolutionary Approach to 3D-Printing (2018)
Cem C. Tutum
, Supawit Chockchowwat, Etienne Vouga and
Risto Miikkulainen
Consumer-grade printers are widely available, but their ability to print complex objects is limited. Therefore, new designs need to be discovered that serve the same function, but are printable. A representative such problem is to produce a working, reliable mechanical spring. The proposed methodology for discovering solutions to this problem consists of three components: First, an e ective search space is learned through a variational autoencoder (VAE); second, a surrogate model for functional designs is built; and third, a genetic algorithm is used to simultaneously update the hyperparameters of the surrogate and to optimize the designs using the updated surrogate. Using a car-launcher mechanism as a test domain, spring designs were 3D-printed and evaluated to update the surrogate model. Two experiments were then performed: First, the initial set of designs for the surrogate-based optimizer was selected randomly from the training set that was used for training the VAE model, which resulted in an exploitative search behavior. On the other hand, in the second experiment, the initial set was composed of more uniformly selected designs from the same training set and a more explorative search behavior was observed. Both of the experiments showed that the methodology generates interesting, successful, and reliable spring geometries robust to the noise inherent in the 3D printing process. The methodology can be generalized to other functional design problems, thus making consumer-grade 3D printing more versatile.
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Citation:
To Appear In
Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2018)
, 8, Kyoto, Japan, July 2018.
Bibtex:
@inproceedings{tutum:gecco18, title={Functional Generative Design: An Evolutionary Approach to 3D-Printing}, author={Cem C. Tutum and Supawit Chockchowwat and Etienne Vouga and Risto Miikkulainen}, booktitle={Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2018)}, month={July}, address={Kyoto, Japan}, pages={8}, url="http://nn.cs.utexas.edu/?tutum:gecco18", year={2018} }
Presentation:
Slides (PDF)
People
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Cem C Tutum
Former Research Scientist
tutum [at] cs utexas edu
Areas of Interest
Evolutionary Computation
Applications