Data-driven composition design and property optimization of solid solution and precipitation simultaneously strengthened non-oriented silicon steel

Abstract

Limitations on data volume and quality are key bottlenecks in using machine learning for property prediction and property-oriented composition design of non-oriented silicon steel. In response, this study employed a Gaussian Mixture Model (GMM) to generate virtual samples for enhancing the in-house experimental data, by which the generated virtual data well captured the distribution of the raw experimental data. As a result, compared with the model without data augmentation, the enhanced prediction model (composition → property) fitted by virtual samples improved its R2 value from 0.52 to 0.86. Based on this model, a multi-properties-oriented (yield strength σs, magnetic induction B50, and iron loss P10/400) composition prediction model (property → composition) was established to rapidly discover high-performance non-oriented silicon steel. Experimental characterization and theory calculation of the explored candidate alloys exhibited that their yield strength was above 750 MPa, which primarily results from solid solution strengthening (50 %) and precipitation strengthening (36 %). Moreover, the alloys possessed magnetic induction B50 of over 1.72 T and low core losses with P10/400 of 12.1 W/kg (0.2 mm) and 17.0 W/kg (0.35 mm). Further microstructural characterization exhibited that such satisfactory performance is associated with copper (Cu)-rich nanoprecipitates that dramatically improved the yield strength without deteriorating the magnetic properties.

Keywords

• Non-oriented silicon steel
• Machine learning
• Strength
• Magnetic properties