Browse > Article

A Study on Prediction Model Performance of Scaffold Pore Size Using Machine Learning Regression Method  

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)
Publication Information
Journal of the Semiconductor & Display Technology / v.19, no.1, 2020 , pp. 36-41 More about this Journal
Abstract
In this paper, We need to change all print factors when which print scaffold with 400 ㎛ pore using FDM 3d printer. Therefore the print quantity is 10 billion times, So we are difficult to print on workplace. To solve the problem, we used the prediction model based machine learning regression. We preprocessed and learned the securing print condition data, and we produced different kinds of prediction models. We predicted the pore size of scaffolds not securing with new print condition data using prediction models. We have derived the print conditions that satisfy the pore size of 400 ㎛ among the predicted print conditions of pore size. We printed the scaffolds 5 times on the condition. We measured the pore size of the printed scaffold and compared the average pore size with the predicted pore size. We confirmed that error was less than 1%, and we were identify the model with the highest pore size prediction performance of scaffold.
Keywords
3D Printer; Machine Learning; Precision; Regression; Scaffold;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
연도 인용수 순위
1 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.
2 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.
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.   DOI
4 Woong-Sup Lee, Jong-Yeol Ryu, Tae-Won Ban, Seong-Hwan Kim, Sang-Kee Kang, Young-Hwa Ham and Hyun-June Lee, "Estimation of Body Core Temperature of Cow using Neck Sensor based on Machine Learning", J. of Institute of Information and communication Engineering, Vol.22, pp.1611-1617, 2018.
5 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.
6 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.