머신 러닝을 이용한 인공지지체 기공 크기 예측 모델에 관한 연구

A Study on Prediction Model of Scaffold Pore Size 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 : 2019.11.22
  • 심사 : 2019.12.12
  • 발행 : 2019.12.31

초록

In this paper, We used the regression model of machine learning for improve the print quantity problem when which print scaffold with 400 ㎛ pore using FDM 3d printer. We have difficult to experiment with changing all factors in the field. So we reduced print quantity by selected two factors that most impact the pore size. We printed and measured scaffold 5 times under same conditions. We created regression model using scaffold pore size and print conditions. We predicted pore size of untested print condition using the regression model. After print scaffold with 400 ㎛ pore, we printed scaffold 5 times under same conditions. We compare the predicted scaffold pore size and the measured scaffold pore size. We confirmed that error is less than 1 % and we verified the results quantitatively.

키워드

참고문헌

  1. Yang-Chang Lee, Chung-Heon Yoo, Jin-Kyu Yoo and Sang-Jin Kim, "3D Printer, Production and Application", Jinsaem Media, 2015.
  2. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Manufacturing Condition of PLGA Scaffold Using 3SC Practical TRIZ and Design of Experiment", J. of The Korean Society of Semiconductor & Display Technology, Vol. 17, pp. 70-75, 2018.
  3. Young-Woo Park and Sang-Won You, "Direction of Improvement of Reproducibility through Shape Distortion of Fused Deposition 3D Printing", J. of Basic Design & Art, Vol. 19, pp. 195-204, 2018. https://doi.org/10.47294/KSBDA.19.3.14
  4. Ji-Eun Lee, Young-Eun Im and Keun Park, "Finite Element Analysis of a Customized Eyeglass Frame Fabricated by 3D Printing", Tran. of The Korean Society of Mechanical Engineers, Vol. 40(1), pp. 65-71, 2016. https://doi.org/10.3795/KSME-A.2016.40.1.065
  5. 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.
  6. 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.