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A Study on Real-Time Defect Detection System Using CNN Algorithm During Scaffold 3D Printing  

Lee, Song Yeon (Mechatronics Engineering, Graduate School of Korea University of Technology and Education)
Huh, Yong Jeong (School of Mechatronics Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Semiconductor & Display Technology / v.20, no.3, 2021 , pp. 125-130 More about this Journal
Abstract
Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.
Keywords
3D Printing Scaffold; Defect Shape Comparison; Densenet Algorithm; Real-Time Detection; Scaffold Defect Detection;
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