.png){kind=icon}
.png){kind=icon}
Return
A machine learning-assisted structural optimization scheme for fast-tracking topology optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s00158-022-03181-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this work, we propose to accelerate the computational speed of structural optimization by using a machine learning-assisted structural optimization (MLaSO) scheme. A new machine learning model is proposed for integrating within structural optimization using one mesh, training globally during structural optimization based on the selected historic results of a chosen optimization quantity in previous iterations. At selected iterations, the trained neural network predicts the update of the chosen optimization quantity so that the solution can be updated without conducting finite element analysis and sensitivity analysis. The proposed MLaSO scheme can be easily integrated into different structural optimization methods and used to solve many design problems without preparing additional training datasets. As a demonstration, MLaSO is integrated within the solid isotropic material with penalization topology optimization algorithm to solve four 2D design problems. The performance and benefits of MLaSO, in terms of prediction accuracy and computational efficiency, are demonstrated based on the present numerical results.
Keywords:
Neural networks
Machine learning
Topology optimization
Journal
IF:
4
Papers:
4.8K
Citations:
1.7W