한국분말재료학회지 (Journal of Powder Materials)

  • 제29권6호
  • /
  • Pages.459-467
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  • 2022
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  • 2799-8525(pISSN)
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  • 2799-8681(eISSN)
한국분말재료학회 (*구 분말야금학회) (The Korean Powder Metallurgy & Materials Institute)
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Optimization of VIGA Process Parameters for Power Characteristics of Fe-Si-Al-P Soft Magnetic Alloy using Machine Learning

  • Sung-Min, Kim (Smart liquid processing R&D department, Korea institute of Industrial Technology) ;
  • 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) ;
  • Sung-Uk, Hong (Manufacture of powder materials, Metal Mate) ;
  • Yeon-Joo, Lee (Smart liquid processing R&D department, Korea institute of Industrial Technology) ;
  • 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)
  • 투고 : 2022.12.15
  • 심사 : 2022.12.28
  • 발행 : 2022.12.28
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초록

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.

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과제정보

This research was developed by the Korea Institute of Industrial Technology project (No. JH220003) funded by the Korea Institute of Industrial Technology (KITECH). The project name is Development of platform core technology of diamond reinforced metal matrix composites for heat spreader based on artificial intelligence.

참고문헌

  1. R. Hasegawa: J. Magn Magn Mater., 215-216 (2000) 240. https://doi.org/10.1016/S0304-8853(00)00126-8
  2. H. J. Kim: J. Korean Mang. Soc., 21 (2011) 77.
  3. D. G. Kim and C. J. Choe: Inst. Electr. Eng., 66 (2017) 15.
  4. S. H. Lee, B. T. Kim and J. H. Cho: Korean Inst. Electr. Eng., 63 (2014) 25.
  5. B. S. Kim and M. H. Lee: Korea Ceram. Soc., 18 (2015) 5.
  6. B. Ziebowicz, D. Szewieczek and L. A. Dobrzanski: J. Mater. Manuf. Eng., 20 (2007) 207.
  7. R. Tamura, T. Osada, K. Minagawa, T. Kohata, M. Hirosawa, K. Tsuda and K. Kawagishi: Mater. Des., 198 (2021) 1.
  8. H. J. Liu, H. L. Su, W. B. Geng, Z. G. Sun, T. T. Song, X. C. Tong, Z. Q. Zou, Y. C. Wu and Y. W. Du: J. Supercond. Novel Magn., 29 (2016) 463. https://doi.org/10.1007/s10948-015-3282-4
  9. N. H. Goo and J. H. Lee: Korean J. of Met. Mater., 33 (2020) 23.
  10. S. H. Ryu, B. O. Kong, K. C. Kim, K. W. Lee and Y. S. Lee: Korean J. Met. Mater., 33 (2020) 8.
  11. J. Jeon, D. E. Kim, J. H. Hong, H. J. Kim and S. J. Lee: Korean J. Met. Mater., 60 (2020) 1.