Chung Jin-Kyo, Im Jeong-Seon, Park Min-Soo
Department of Mechanical Information Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea.
Department of Mechanical System Design Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea.
Materials (Basel). 2023 Jun 30;16(13):4734. doi: 10.3390/ma16134734.
Due to the high hardness and brittleness of ceramic materials, conventional cutting methods result in poor quality and machining difficulties. Additive manufacturing has also been tried in various ways, but it has many limitations. This study aims to propose a system to monitor surface defects that occur during the printing process based on high-viscosity composite resin that maximizes ceramic powder content in real time using image processing and convolutional neural network (CNN) algorithms. To do so, defects mainly observed on the surface were classified into four types by form: pore, minor, critical, and error, and the effect of each defect on the printed structure was tested. In order to improve the classification efficiency and accuracy of normal and defective states, preprocessing of images obtained based on cropping, dimensionality reduction, and RGB pixel standardization was performed. After training and testing the preprocessed images based on the DenseNet algorithm, a high classification accuracy of 98% was obtained. Additionally, for pore and minor defects, experiments confirmed that the defect surfaces can be improved through the reblading process. Therefore, this study presented a defect detection system as well as a feedback system for process modifications based on classified defects.
由于陶瓷材料具有高硬度和脆性，传统切割方法会导致质量不佳和加工困难。人们也尝试了各种增材制造方法，但存在诸多局限性。本研究旨在提出一种系统，该系统基于高粘度复合树脂，利用图像处理和卷积神经网络（CNN）算法实时最大化陶瓷粉末含量，以监测打印过程中出现的表面缺陷。为此，将主要在表面观察到的缺陷按形式分为四类：气孔、轻微缺陷、严重缺陷和错误，并测试了每种缺陷对打印结构的影响。为提高正常状态和缺陷状态的分类效率与准确性，对基于裁剪、降维和RGB像素标准化获取的图像进行了预处理。基于DenseNet算法对预处理后的图像进行训练和测试后，获得了98%的高分类准确率。此外，对于气孔和轻微缺陷，实验证实可通过重新刮削工艺改善缺陷表面。因此，本研究提出了一种缺陷检测系统以及基于分类缺陷的工艺改进反馈系统。
