Journal
IEEE TRANSACTIONS ON MAGNETICS
Volume 55, Issue 6, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMAG.2019.2901906
Keywords
Approximate computing; convolutional neural network (CNN); deep learning (DL); interior permanent magnet (IPM) motor; topology optimization
Funding
- JSPS KAKENHI [18K18840]
- Grants-in-Aid for Scientific Research [18K18840] Funding Source: KAKEN
Ask authors/readers for more resources
The computational cost of topology optimization based on the stochastic algorithm is shown to be greatly reduced by deep learning. In the learning phase, the cross-sectional image of an interior permanent magnet motor, represented in RGB, is used to train a convolutional neural network (CNN) to infer the torque properties. In the optimization phase, all the individuals are approximately evaluated by the trained CNN, while finite element analysis for accurate evaluation is performed only for a limited number of individuals. It is numerically shown that the computational cost for the topology optimization can be reduced without the loss of optimization quality.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available