A comparative evaluation of Vat-photopolymerization and PolyJet 3D printers using multiple measurement methods.

OBJECTIVE

Given the widespread adoption of additive manufacturing in dentistry, assessing the dimensional accuracy of printed objects is critical. This study aimed to evaluate the three-dimensional (3D) deviations produced by four printers utilizing SLA, mSLA, and PolyJet technologies, employing three distinct measurement methodologies.

MATERIALS AND METHOD: Sixty cube specimens with a 15 mm edge length were fabricated (n = 15 per group) using four different 3D printers: Form 3B+, Prusa SL1, Objet 30 Orthodesk and Connex 260. The distance between opposing cube faces was assessed at twelve predefined locations using three measurement methodologies: manual measurement with a digital micrometer, a micro-computed tomography (micro-CT) system, and an optical scanner.

RESULTS

Micro-CT and micrometer measurements yielded reliable dimensional data, whereas the optical scanner consistently yielded underestimated values. Micro-CT yielded superior definition of the specimen surface topography Mean micro-CT measurements differed significantly from the nominal reference value (15,000 µm) in 15 of 16 instances for X coordinates (p < 0.01), 15 instances for Y coordinates (p < 0.015), and in 12 instances for Z coordinates (p < 0.03). Deviations were significantly higher in the X and Y axes compared to the Z axis.

CONCLUSIONS

Micro-CT was identified as the most precise measurement method among those evaluated. Of the printers tested, the Connex260 demonstrated superior overall performance, exhibiting the lowest dimensional deviation. Across all devices, the highest accuracy was consistently observed along the Z-axis.

CLINICAL SIGNIFICANCE: Even minor dimensional inaccuracies in additively manufactured components can compromise the fit, marginal integrity, and function of restorations, directly impacting patient comfort and treatment longevity. Consequently, the careful selection of appropriate printing technologies, validated by precise measurement protocols, is critical for ensuring predictable clinical outcomes.