inverse-kirigami

This repository contains the code for the paper:

Rapid design of fully soft deployable structures via kirigami cuts and active learning

Here, we propose a rapid design approach for fully soft structures that can achieve targeted 3D shapes through a fabrication process that happens entirely on a 2D plane. We develop a symmetry-constrained active learning approach to learn how to explore the large design space efficiently. The proposed framework can accelerate the adoption of morphing structures in a range of areas including soft robotics, additive manufacturing, and the construction industry.

cnntrain_vae.py performs variational autoencoder to reduce the dimension of candidate kirigami patterns to low-dimension representations.

cnntrainv2_baysesskit.py performs Bayesian optimization. The "gp_minimize" function (a function in scikit-optimize) conducts Bayesian optimization using Gaussian Processes. In other words, the function values are assumed to follow a multivariate Gaussian. The covariance of the function values is given by a GP kernel between the parameters. Then a smart choice to choose the next parameter to evaluate can be made by the acquisition function over the Gaussian prior which is much quicker to evaluate. The "black_box_functionrt" function is used to compare the 3D deformation pattern predicted by finite element simulation and the desired shape.

Requirements

To run the code, you must install the following dependencies first:

PyTorch (1.8.1)
atomai
scikit-optimize

You also need to install softwares:

[ABAQUS (Finite Element Analysis)] (https://www.3ds.com/products-services/simulia/products/abaqus/)
[MATLAB] (https://www.mathworks.com/products/matlab.html)

Files

cnntrain_vae.py performs variational autoencoder to reduce the dimension of candidate kirigami patterns to low dimension representations
cnntrainv2_baysesskit.py performs Bayesian optimization. The python code start and stop MATLAB Engine and ABAQUS during each round of iteration.
Matlab files are used to create the mesh, boundary conditions input to the ABAQUS simulation
'readodb.py' reads the .odb files generated by ABAQUS.

Citation

If you use this code for part of your project or paper, or get inspired by the associated paper, please cite:

@misc{Ma2022Rapid,
    doi = {10.48550/ARXIV.2203.11546},
    url = {https://arxiv.org/abs/2203.11546},
    author = {Ma, Leixin and Mungekar, Mrunmayi and Roychowdhury, Vwani and Jawed, M. Khalid},
    keywords = {Computational Physics (physics.comp-ph), FOS: Physical sciences, FOS: Physical sciences},
    title = {Rapid design of fully soft deployable structures via kirigami cuts and active learning},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}

StructuresComp/inverse-kirigami

inverse-kirigami

Requirements

Files

Citation