| Title |
Efficient Magnetic Property Prediction and Process Optimization by Combining Generative Adversarial
Networks and Auto Machine Learning in Thin-Gauge Fe-Si Alloy Sheet |
| Authors |
오지은(Jieun Oh) ; 박세민(SeMin Park) |
| DOI |
https://doi.org/10.3365/KJMM.2025.63.9.743 |
| ISSN |
1738-8228(ISSN), 2288-8241(eISSN) |
| Keywords |
Si steels; Rolling; Magnetic properties; Computer simulation; Machine learning |
| Abstract |
In order to greatly increase the energy efficiency of the motor core of electric vehicles, commercial
0.5 mm non-oriented Si steel was re-rolled to a thin-gauge of 0.1 mm, which cannot be accomplished with
commercial rolling. To maximize the flux density, a key factor affecting energy efficiency, the hysteresis AI
model was optimized based on the heat treatment conditions. GAN, a data augmentation technology, and
deepfake technology were used. The effectiveness of GAN and AutoML were explored as a way of increasing
the consistency of the hysteresis AI model with fewer experiments. The AutoML model with FLAML based
on the tree model was effectively improved, and the model performance was effectively improved on 4000 data
per condition, similar to one condition of the existing data without data augmentation. The R2 score was
0.9827 and the MSE was 0.0213 for various temperature and time conditions, which is less than half of the
MSE of 49% compared to the MSE before applying GAN. The heatmap analysis of the flux density model
showed that the maximum flux density of B8 was 1.50T under these conditions, which was about 25% better
than the lowest flux density (1.14T). The value was then confirmed by actual experiment. A maximum (1.50T)
of B8 in 0.1 mm re-rolled Si steel was obtained, demonstrating that the combination of GAN and FLAML
can improve the consistency of continuous data material models such as hysteresis curves when experiments
are insufficient. This has sufficient potential for developing magnetic data GAN technology. |