Accuracy Comparisons of Castable Patterns Fabricated Through Different CAD/CAM Technologies

Abstract

Introduction: Digital manufacturing has become increasingly prevalent in restorative dentistry with a majority of restorations most typically fabricated through subtractive manufacturing (SM). Recent innovations in additive manufacturing (AM) have led to a paradigm shift in its applicability for the fabrication of dental restorations and appliances. However, the accuracy of AM in the production of indirect restorations is not well documented in literature. Objective: The purpose of this study was to compare the internal accuracy of castable patterns fabricated through additive and subtractive manufacturing techniques. Methods: A typodont tooth was manually prepared for a full-coverage restoration with a circumferential shoulder margin and subsequently digitized. Using CAD software (exocad; GmbH, Hessen, Germany) a full coverage restoration was designed and used to produce three groups of 20 castable patterns - one group fabricated through SM (CORiTEC350i; imes-icore, Hessen, Germany), and two through AM (Form2 series SLA printer and Form3 series SLA printer; Formlabs, Somerville, MA). The resulting patterns were digitized and their internal surfaces were superimposed using best-fit alignment in a surface matching software (Materialise 3-matic; Materialise, NV Leuven, Belgium). After alignment, the dimensional differences were calculated through a global mean difference analysis. A mixed-model ANOVA was used to assess differences in internal discrepancies across groups followed by pair-wise comparisons using Tukey's method. Results: There was a significant difference (P less than 0.001) in global mean differences between the castable patterns fabricated through subtractive manufacturing when compared to those additively manufactured. However, there was no statistical difference (P=0.677) between the two AM techniques.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2023
Accession Number
AD1222326

Entities

People

  • Ian P. Colling

Organizations

  • Uniformed Services University of the Health Sciences

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  • Battery Technology and Engineering
  • Computer Vision.
  • Manufacturing Engineering.