반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
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기계 학습을 이용한 인공지지체 외형 불량 예측 모델에 관한 연구

A Study on Prediction Model of Scaffold Appearance Defect Using Machine Learning

  • 이송연 (한국기술교육대학교대학원 메카트로닉스공학과) ;
  • 허용정 (한국기술교육대학교 메카트로닉스공학부)
  • Lee, Song-Yeon (Mechatronics Engineering, Graduate School of Korea University of Technology and Education) ;
  • Huh, Yong Jeong (Department of Mechatronics Engineering, Korea University of Technology and Education)
  • 투고 : 2020.05.26
  • 심사 : 2020.06.18
  • 발행 : 2020.06.30
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초록

In this paper, we studied the problem if the experiment number occurring in order to identify defect in scaffold. We need to change each of the 5 print factor to predict defect when printing disk type scaffold using FDM 3d printer. So then the number of scaffold print will be more than 100,000 times. This experiment number is difficult to perform in the field. In order to solve this problem, we have produced a prediction model based on machine learning multiple linear regression using print conditions and defect scaffold data for print conditions. The prediction model produced was verified through experiments. The verification confirmed that the error was less than 0.5 %. We have confirmed that satisfied within the target margin of error 5 %.

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참고문헌

  1. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Prediction Model of Scaffold Pore Size Using Machine Learning", J. of The Korean Society of Semiconductor & Display Technology, Vol.18, pp. 46-50, 2019.
  2. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Prediction Model Performance of Scaffold Pore Size Using Machine Learning Regression Method", J. of The Korean Society of Semiconductor & Display Technology, Vol.19, pp. 36-41, 2020.
  3. Yeon-Ho Chu and Young-Kyu Choi, "A Deep Learning based IOT Device Recognition System" J. of The Korea Society of Semiconductor & Display Technology, Vol.18, pp.01-05, 2019.
  4. Yong-Beom Park, Dong-Bin Choi and In-Soo Cho, "Taxation Analysis Using Machine Learning", J. of The Korean Society of Semiconductor & Display Technology, Vol.18, pp. 73-77, 2019.
  5. Seung-Hyeok Choi, Min-Woo Sa and Jong-Young Kim, "New Fabricatio Method of Bio-Ceramic Scaffolds Based on Mold using a FDM 3D Printer", J. of The Korean Society of Precision Engineering, Vol.18, pp. 957-963, 2018.
  6. Yu-Sun Ahn, Hue-Jin Kim, Sang-Kyu Lee and Byung sean Kim, "Prediction of Heating Energy Consumption Using Machine Learning and Parameters in Combined Heat and Power Generation", J of Air-Conditioning and Refrigeration Engineering, Vol. 31, pp. 352-360, 2019. https://doi.org/10.6110/KJACR.2019.31.8.352
  7. Young-Ho Lee and Seong-Yun Hong, "A Machine Learning Approach to the Prediction of Individual Travel Mode Choices", J of the Koreaa Data and Information Science Society, Vol. 30, pp. 1011-1024, 2019. https://doi.org/10.7465/jkdi.2019.30.5.1011
  8. Bum-Ju Lee, "Prediction Model of Hypercholesterolemia using Body Fat Mass Based on Machine Learning", J of the Convergence on Culture Technology, Vol. 5, pp. 413-420, 2019. https://doi.org/10.17703/JCCT.2019.5.1.413