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A Study on Square Pore Shape Discrimination Model of Scaffold Using Machine Learning Based Multiple Linear Regression  

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.4, 2020 , pp. 59-64 More about this Journal
Abstract
In this paper, we found the solution using data based machine learning regression method to check the pore shape, to solve the problem of the experiment quantity occurring when producing scaffold with the 3d printer. Through experiments, we learned secured each print condition and pore shape. We have produced the scaffold from scaffold pore shape defect prediction model using multiple linear regression method. We predicted scaffold pore shapes of unsecured print condition using the manufactured scaffold pore shape defect prediction model. We randomly selected 20 print conditions from various predicted print conditions. We print scaffold five times under same print condition. We measured the pore shape of scaffold. We compared printed average pore shape with predicted pore shape. We have confirmed the prediction model precision is 99 %.
Keywords
3D Printing scaffold; Scaffold pore shape; Defect Prediction Model; Machine Learning; Pore Shape Prediction;
Citations & Related Records
Times Cited By KSCI : 12  (Citation Analysis)
연도 인용수 순위
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 Seung-Hyeok Choi, Min-Woo Sa and Jong-Young Kim, "New Fabrication 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.
3 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.
4 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.
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 Yong-Hwan Lee and Heung-Jun Kim, "Implementation of Fish Detection Based on Convolutional Neural Networks", J. of The Korean Society of Semiconductor & Display Technology, Vol.19, pp. 124-129, 2020.
7 Yong-Hwan Lee and Young-Seop Kim, "Comparison of CNN and YOLO for Object Detection", J. of The Korean Society of Semiconductor & Display Technology, Vol.19, pp. 85-92, 2020.