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Data-driven modeling for build angle optimization to improve accuracy of 3D-printed resin crowns
Kaibin Wu1, Chen Zhu1, Qinyang Yan1
1Department of Prosthodontics, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, China.
Objective:
To optimize build angles to improve the accuracy of 3D-printed resin crowns using a quadratic regression model.
Methods:
Resin crown specimens (n = 6) were fabricated using a digital light processing (DLP) printer at four build angles (0°, 30°, 60°, 90°) and two layer thicknesses (50 μm, 100 μm). The dimensional accuracy was quantified by 3D scanning and calculating root mean square error (RMSE). A quadratic regression model was developed and trained on experimental data to establish predictive relationships between build angle and dimensional accuracy. The optimal build angle was further verified.
Results:
Mid-range angles (37° for 50 μm layers, 45° for 100 μm layers) yielded the lowest RMSE values, indicating optimal trueness. Extreme angles (0° and 90°) demonstrated statistically significant deviations, probably due to anisotropic shrinkage stress (0°) and gravitational effect (90°). The quadratic model effectively captured the nonlinear relationship between build angle and geometric accuracy (p < 0.0001).
Significance:
Mid-range build angles balance structural integrity and dimensional accuracy, mitigating distortion mechanisms while preserving print efficiency. The proposed data-driven method enables evidence-based parameter selection, offering a potential approach to enhance precision in DLP-fabricated dental restorations.
