Deep learning-based topological optimization for representing a
user-specified design area
- URL: http://arxiv.org/abs/2004.05461v2
- Date: Sun, 19 Apr 2020 13:44:20 GMT
- Title: Deep learning-based topological optimization for representing a
user-specified design area
- Authors: Keigo Nakamura and Yoshiro Suzuki
- Abstract summary: We propose a new deep learning model to generate an optimized structure for a given design domain and other boundary conditions without iteration.
The resolution of the optimized structure is 32 * 32 pixels, and the design conditions are design area, volume fraction, distribution of external forces, and load.
Comparing the performance of our proposed model with a CNN model that does not use BN and SPADE, values for mean absolute error (MAE), mean compliance error, and volume error with the optimized topology structure generated in MAT-LAB code were smaller.
- Score: 0.060917028769172814
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Presently, topology optimization requires multiple iterations to create an
optimized structure for given conditions. Among the conditions for topology
optimization,the design area is one of the most important for structural
design. In this study, we propose a new deep learning model to generate an
optimized structure for a given design domain and other boundary conditions
without iteration. For this purpose, we used open-source topology optimization
MATLAB code to generate a pair of optimized structures under various design
conditions. The resolution of the optimized structure is 32 * 32 pixels, and
the design conditions are design area, volume fraction, distribution of
external forces, and load value. Our deep learning model is primarily composed
of a convolutional neural network (CNN)-based encoder and decoder, trained with
datasets generated with MATLAB code. In the encoder, we use batch normalization
(BN) to increase the stability of the CNN model. In the decoder, we use SPADE
(spatially adaptive denormalization) to reinforce the design area information.
Comparing the performance of our proposed model with a CNN model that does not
use BN and SPADE, values for mean absolute error (MAE), mean compliance error,
and volume error with the optimized topology structure generated in MAT-LAB
code were smaller, and the proposed model was able to represent the design area
more precisely. The proposed method generates near-optimal structures
reflecting the design area in less computational time, compared with the
open-source topology optimization MATLAB code.
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