Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

Abstract
1. Introduction
2. Experimental
3. Results and Discussion
    3.1 Measurement data on the main factors of theVIGA process and the characteristics of the manufacturedpowder
    3.2 Data conversion of the main factors of theVIGA process and the characteristics of the manufacturedpowder
    3.3 Sensitivity of process parameters of VIGA processto the properties of Fe-Si-Al-P alloy powder
    3.4 Prediction model comparison
4. Conclusions
Acknowledgments
References

• Sung-Min Kim(Smart liquid processing R&D department, Korea institute of Industrial Technology, Department of Materials Science and Engineering, Inha University)
• Eun-Ji Cha(Smart liquid processing R&D department, Korea institute of Industrial Technology)
• Do-Hun Kwon(Smart liquid processing R&D department, Korea institute of Industrial Technology, Department of Materials Science and Engineering, Inha University)
• Sung-Uk Hong(Division of Advanced Materials Engineering, Jeonbuk National University)
• Yeon-Joo Lee(Smart liquid processing R&D department, Korea institute of Industrial Technology, Department of Advanced Materials Engineering, Kookmin University)
• Seok-Jae Lee(Division of Advanced Materials Engineering, Jeonbuk National University)
• Kee-Ahn Lee(Department of Materials Science and Engineering, Inha University)
• Hwi-Jun Kim(Smart liquid processing R&D department, Korea institute of Industrial Technology) Corresponding author