Crown fit and dimensional accuracy of zirconia fixed crowns based on the digital light processing technology

Zirconia is extensively used for manufacturing dental prostheses because of its superior mechanical properties. The idea of using 3D printing to manufacture zirconia dental prostheses has gradually become popular, but printing accurate shapes and obtaining excellent dimensional accuracy still remain challenging. In this study, the optimal curing parameters and shrinkage ratios of zirconia slurry were investigated using a multi-angle model and a standard fixed crown model. Then the dimensional accuracy and crown fit were investigated, and the fixed crown and fixed bridge were fabricated by using digital light processing (DLP) technology. Further, the dimensional accuracy and crown fit were investigated. The results showed that the optimal curing parameters and enlargement factors for zirconia fixed crown printing were 80 PWM (light source power), 2s (curing time) and 1.400, 1.400, 1.500 (in X, Y, Z directions), respectively. The internal fit was 239.3 +/- 7.9 mu m, which met the clinical criteria (<= 300 mu m), whereas the marginal fit was 128.1 +/- 7.1 mu m, which was extremely close to the clinical criteria (<= 120 mu m). For dimensional accuracy, the standard error of sintered fixed crown was 0.14 +/- 0.06 mm, and the boundary dimensional accuracy and anatomical landmark points' dimensional accuracy were also investigated. In addition, the density of sintered zirconia was 6.01 +/- 0.02 g/cm(3). From the results of this study, it could be found that the crown fit of zirconia fixed crowns printed using the DLP technique could be close to clinical standards, although there is still a gap with the zirconia fixed crowns from the milling technique. In addition incomplete removal of the support structures have a negative impact on dimensional accuracy, hence it is necessary to further explore the optimization strategies of the support structures for improving the dimensional accuracy of fixed crowns printed by DLP.