Journal of Guangdong University of Technology ›› 2018, Vol. 35 ›› Issue (01): 1-5.doi: 10.12052/gdutxb.170091

    Next Articles

Internal Defects Detection and Their Features Statistical Analysis of Porcelain Teeth

Zhong Ying-chun1, Lyu Shuai1, Luo Peng2, Jian Yu-tao3, Chu Qian-kun4   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. Department of Bone and Joint Surgery, Sixth People's Hospital of Shenzhen, Shenzhen 518000, China;
    3. Institute of Stomatological Research, Sun Yat-sen University, Guangzhou 510080, China;
    4. Department of Imaging, Sixth People's Hospital of Shenzhen, Shenzhen 518000, China
  • Received:2017-05-03 Online:2018-01-09 Published:2017-12-22
  • Supported by:
     

Abstract: Porcelain tooth is an ideal prosthesis of oral diseases. Limited by the fabrication process, there are always some internal defects in porcelain teeth, such as stomas, shrinkages and bubbles. These defects affect the service life and strength of porcelain teeth. It is proposed to scan the porcelain teeth by MicroCT. Then the defects can be displayed clearly through image processing. Subsequently, some features of defect are extracted, including areas and positions. Finally, the statistics of these features are analyzed in order to giveadviceto improve the fabrication process of porcelain teeth. The analysis results show that the distribution of area of internal defect obeys Weibull distribution, that is, the number of internal defects with large area is small but the number of internal defects with small areas is quite large. It reveals that most of defects are caused by porosity and shrinkage, which often result in small area defects. Additionally, the defects are mainly located at crown and joint surface, where the stress concentration is often located. Therefore, there are two methods to reduce the defects. One is reduce the stress concentration and the otherto improve the gas exit.

Key words: MicroCT, image process, internal flaws of porcelain, statistical analysis

CLC Number: 

  • TP391.41
[1] Zou Heng, Gao Jun-li, Zhang Shu-wen, Song Hai-tao. Design and Implementation of a Dropping Guidance Device for Go Robot [J]. Journal of Guangdong University of Technology, 2023, 40(01): 77-82,91.
[2] Liu Xin-hong, Su Cheng-yue, Chen Jing, Xu Sheng, Luo Wen-jun, Li Yi-hong, Liu Ba. Real Time Detection of High Resolution Bridge Crack Image [J]. Journal of Guangdong University of Technology, 2022, 39(06): 73-79.
[3] Qiu Zhan-chun, Fei Lun-ke, Teng Shao-hua, Zhang Wei. Palmprint Recognition Based on Cosine Similarity [J]. Journal of Guangdong University of Technology, 2022, 39(03): 55-62.
[4] Jie Yun-fei, Everett Wang, Zhong You-dong, Zhi Kai-xuan, Xiong Chao-wei. An Indoor Positioning Method of Monocular Vision Robot Based on Floor Features [J]. Journal of Guangdong University of Technology, 2020, 37(05): 31-37.
[5] Ma Fei, Li Juan. A Research on the Classification of Learners and Patterns of Learning Behavior Based on Cluster Algorithms under MOOCs’ Environment [J]. Journal of Guangdong University of Technology, 2018, 35(03): 18-23.
[6] LIANG Shi-Hua, ZHOU Shi-Zong, ZHANG Lang, WANG Meng. Statistical Analysis of Physical and Mechanical Indexes of Granite Residual Soil in Eastern Guangzhou [J]. Journal of Guangdong University of Technology, 2015, 32(1): 29-33.
[7] ZOU Qing-Sheng, WANG Ren-Huang, MING Jun-Feng. Design of Multi-parameter Classifying System in Ceramic Tiles Based on M achine Vision [J]. Journal of Guangdong University of Technology, 2010, 27(4): 46-49.
[8] YUAN Xi-xia,YUE Jian-hua,ZHAO Xian-ren. Application of MATLAB in Improved Median Filtering [J]. Journal of Guangdong University of Technology, 2007, 24(1): 33-35.
[9] XUE Lan-yan,ZHENG Sheng-lin,PAN Bao-chang,CHEN Xiao-feng . A Thresholding Method for Grey Image Segmentation Based on Neural Network [J]. Journal of Guangdong University of Technology, 2005, 22(4): 67-72.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!