Hwi-Jun Kim

Amorphous soft magnetic materials are ideal for high-frequency applications due to their low coercivity, high permeability, and reduced core loss. However, optimizing packing density while maintaining superior magnetic properties remains a challenge, particularly for high-efficiency magnetic cores in electric vehicles and renewable energy systems. This study addresses this challenge by integrating discrete element method simulations, machine learning, and experimental validations to optimize powder packing density and evaluate its impact on magnetic properties. Tri-modal amorphous powders were mixed at various ratios, achieving over 90% relative density with derived ratios by machine learning, significantly outperforming conventional trail-and-error methods. Enhanced core density improved magnetic properties, including a 49.7% reduction in coercivity and an 8.72% increase in saturation magnetization. These improvements were attributed to reduced porosity and optimized compaction strategies. Core loss analysis further demonstrated lower hysteresis and eddy current losses in high-density cores.

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.

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.

Ti-2.5Zr-5.0Hf-37.5Cu-7.5Ni-1.0Si-5.0Sn (at.%) BMG has been successfully manufactured in amorphous powder with a size of about 25 μm (D₅₀). Using this amorphous powder, a Ti-based BMG was manufactured by an additive manufacturing process based on a laser powder bed fusion (LPBF) technique. In 3D printing processes using amorphous powders, it is necessary and important to understand the crystallization behavior due to the difference in energy density applied to the powders. An LPBF process has been carried out with various energy density conditions to minimize the inner defects and identify the sound mechanical properties of 3D-printed BMG parts. At the lowest energy density condition (3.0 J/mm³), the most pores were generated. Even if the same energy density (3.0 J/mm³) was applied, the rapid laser movement caused many pores to form inside the material. The relatively sound 3D-printed Ti-based BMG was successfully fabricated with a size of about 5 mm × 5 mm × 3 mm. Peaks at 41° and 44° showing crystallization were observed in all conditions. The higher the laser power was, the greater each peak intensity and the more crystallization (CuTi, Ti₃Cu₄, etc.) was present in the BMG, and the higher the scan speed, the more the internal defects were found.

Additive manufacturing has many process variables and requires additive process optimization. Line energy and scan speed are the main process variables. The objective of this work aims to investigate the effect of changes in line energy and scan speed among the process variables on the mechanical and fatigue properties of the Ti6Al4V specimens fabricated by electron beam additive manufacturing method. The size of the pore inside the specimen was 40~60 μm with the exception of the condition of 0.2 kJ/m, and the specimen with poor fusion of more than 100 μm and gas pore was found to have lower room temperature tensile and fatigue properties compared to the optimal process conditions. As line energy increased, strain hardening occurred, and yield strength and tensile strength increased. The E_L:0.3 kJ/m and 800 mm/s condition is a process condition that shows no defects such as unmelted powder and poor fusion, and it represents the best fatigue strength of 400 MPa. The fatigue strength of the specimen performed with hot isostatic pressing after additive manufacturing was measured at 550 MPa, an increase of 150 MPa, which resulted in high fatigue strength enhancement. The crack initiation site and propagation behavior were analyzed by observing the fatigue fracture section of the specimen according to the line energy.

Fe-based bulk metallic glasses (BMGs) are a unique class of materials that are attracting attention in a wide variety of applications owing to their physical properties. Several studies have investigated and designed the relationships between alloy composition and thermal properties of BMGs using an artificial neural network (ANN). The limitation of the wide-scale use of these models is that the required composition is yet to be found despite numerous case studies. To address this issue, we trained an ANN to design Fe-based BMGs that predict the thermal properties. Models were trained using only the composition of the alloy as input and were created from a database of more than 150 experimental data of Fe-based BMGs from relevant literature. We adopted these ANN models to design BMGs with thermal properties to satisfy the intended purpose using particle swarm optimization. A melt spinner was employed to fabricate the designed alloys. X-ray diffraction and differential thermal analysis tests were used to evaluate the specimens.

This study demonstrates the feasibility of a novel casting process called tailored additive casting (TAC). The TAC process involves injecting the melt several times to fabricate a single component, with a few seconds of holding between successive injections. Using TAC, we can successfully produce commercial-grade automotive steering knuckles with a tensile strength of 383 ± 3 MPa and an elongation percentage of 10.7 ± 1.1%, from Al 6061 alloys. To produce steering knuckles with sufficient mechanical strength, the composition of an Al 6061 alloy is optimized with the addition of Zr, Zn, and Cu as minor elements. These minor elements influence the thermal properties of the melt and alloy, such as their thermal stress, strain rate, shrinkage volume, and porosity. Optimal conditions for heat treatment before and after forging further improve the mechanical strength of the steering knuckles produced by TAC followed by forging.