Artificial intelligence operated with machine learning was performed to optimize the amount of metalloid elements (Si, B, and P) subjected to be added to a Fe-based amorphous alloy for enhancement of soft magnetic properties. The effect of metalloid elements on magnetic properties was investigated through correlation analysis. Si and P were investigated as elements that affect saturation magnetization while B was investigated as an element that affect coercivity. The coefficient of determination R2 (coefficient of determination) obtained from regression analysis by learning with the Random Forest Algorithm (RFR) was 0.95 In particular, the R2 value measured after including phase information of the Fe-Si-B-P ribbon increased to 0.98. The optimal range of metalloid addition was predicted through correlation analysis method and machine learning.

The soft magnetic properties of Fe-based amorphous alloys can be controlled by their compositions through alloy design. Experimental data on these alloys show some discrepancy, however, with predicted values. For further improvement of the soft magnetic properties, machine learning processes such as random forest regression, k-nearest neighbors regression and support vector regression can be helpful to optimize the composition. In this study, the random forest regression method was used to find the optimum compositions of Fe-Si-B-C alloys. As a result, the lowest coercivity was observed in Fe80.5Si3.63B13.54C2.33 at.% and the highest saturation magnetization was obtained Fe81.83Si3.63B12.63C1.91 at.% with R2 values of 0.74 and 0.878, respectively.

Directed energy deposition (DED) is an additive manufacturing process wherein an energy source is focused on a substrate on which a feedstock material is simultaneously delivered, thereby forming a small melt pool. Melting, solidification, and subsequent cooling occur at high rates with considerable thermal gradients compared with traditional metallurgical processes. Hence, it is important to examine the effects of cooling rates on the microstructures and properties of the additive manufactured materials. In this study, after performing DED with various energy densities, we investigated the changes in the microstructures and Vickers hardness of cast Al-33 wt.% Cu alloy, which is widely used to estimate the cooling rate during processing by measuring the lamellar spacing of the microstructure after solidification. The effects of the energy density on the cooling rate and resultant mechanical properties are discussed, which suggests a simple way to estimate the cooling rate indirectly. This study corresponds to the basic stage of the current study, and will continue to apply DED in the future.