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A Study on Manufacturing Problem Solving of Scaffold with Pore Using 3SC Practical TRIZ and Machine Learning  

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.18, no.3, 2019 , pp. 25-30 More about this Journal
Abstract
In this paper, we have analyzed manufacturing problems of the scaffold with pores using FDM 3D printer and PLGA. We suggested the solutions using 3SC practical TRIZ. We selected the final solution used machine learning. We reduced number of experiments using most influential factor after analysis print factors. We printed the scaffold and measured pore size. We created the regression model using python tensorflow. The print condition data of measured pore size was used as training data. We predicted the pore size of printed condition using regression model. We printed the scaffold using the predicted the print condition data. We quantitatively compare the predicted scaffold pore size data and the measured scaffold pore size data. We got satisfactory result.
Keywords
3D Printer; Machine Learning; Regression; Scaffold; TRIZ;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
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6 Yeon-Ho Chu and Young-Kyu Choi, "A Deep Learning based IOT Device Recognition System", J. of The Korean Society of Semiconductor & Display Technology, Vol.18, pp. 01-05, 2019.