4.3 Article

Accuracy of digital light processing printing of 3-dimensional dental models

出版社

MOSBY-ELSEVIER
DOI: 10.1016/j.ajodo.2019.10.012

关键词

-

向作者/读者索取更多资源

Introduction: This study aimed to investigate whether a digital light processing (DLP) printer could perform efficiently and with adequate accuracy for clinical applications when used with different settings and variations in the orientation of models on the build plate. Methods: Digital impressions of the oral environment were collected from 15 patients. Subsequently, digital impressions were used to make 3-dimensional printed models using the DLP printing technique. Three variables of the printing technique were tested: placement on the build plate (middle vs corner), thickness in the z-axis (50 microns vs 100 microns), and hollow vs solid shell. After being printed with different printing techniques and orientations on the same printer, a total of 240 maxillary and mandibular arches were measured. These variables generated 8 printing combinations. Tooth and arch measurements on each model type were compared with each other. Intraobserver reliability of the repeated measurement error was assessed using intraclass correlation coefficient. Results: All mean differences among the printing variations were statistically insignificant. The Bland-Altman plots verified a high degree of agreement among all model sets and printing variations. In addition, the measurements were highly reproducible; this was demonstrated by the high intraclass correlation coefficient for all measurements recorded. Conclusions: The DLP printer produced clinically acceptable models in all areas of the build plate, with hollow and solid model shells, and at its high-speed setting of 100 microns. The applications of the DLP printer tested should be a viable option for printing in a clinical environment at a high-speed setting while filling the build plate and printing with less resin.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据