Dimensional Accuracy and Adaptation of Monolithic Zirconia Crowns Fabricated by a Novel Material Jetting Technique: An In Vitro Study Based on Clinical Laboratory Data

Introduction and aims: Additive manufacturing technology has the potential to produce accurate restorations in the field of prosthodontics. This in vitro study aimed to investigate the dimensional accuracy and marginal and internal fit of monolithic zirconia crowns fabricated with advanced customized jetting (ACJ) technology, a material jetting method, based on clinical laboratory data. Methods: The three-dimensional (3D) printed tooth preparation casts for ceramic complete crowns of the maxillary central incisor and mandibular first molar (n = 10 for each tth, a total of 20) were collected from a dental laboratory. All casts were digitally scanned, and 40 crowns were fabricated by using ACJ and subtractive manufacturing (SM) methods (n = 10 per manufacturing method for incisors and molars). The dimensional accuracy was determined by superimposing the scanned crown data with the corresponding design data. Marginal and internal fit were evaluated with a dual scanning method. Results: Significant differences were found in the dimensional accuracy and adaptation among the ACJ and SM groups (P < .05). ACJ crowns demonstrated significantly higher accuracy and lower root mean square (RMS) values in the overall, external, marginal, and intaglio regions compared with SM (P < .001). Regarding adaptation, ACJ crowns exhibited smaller marginal gaps, occlusal gaps in molars, and axial gaps in incisors than SM crowns (P < .05). Conclusion: Monolithic zirconia crowns fabricated by using ACJ demonstrated superior dimensional accuracy and adaptation compared with SM based on clinical laboratory data. Clinical Relevance: Advanced customized jetting technology enables the fabrication of monolithic zirconia crowns with favorable dimensional accuracy and marginal and internal fit. This capability demonstrates its significant potential for complete crown production based on clinical laboratory data.