Multi-Site evaluation of a novel point-of-care 3D printing quality assurance protocol for a material jetting 3D printer.

Background: The maturation of 3D printing technologies has opened up a new space for patient advancements in healthcare from trainee education to patient specific medical devices. Point-of-care (POC) manufacturing, where model production is done on-site, includes multiple benefits such as enhanced communication, reduced lead time, and lower costs. However, the small scale of many POC manufacturing operations complicates their ability to establish quality assurance practices. This study presents a novel low-cost quality assurance protocol for POC 3D printing.

Methods: Four hundred specially designed quality assurance cubes were printed across four material jetting printers (J5 Medijet, Stratasys, Eden Prairie, Minnesota, USA) at two large medical centers. Three inner dimension and three outer dimension measurements as well as edge angles were measured for every cube by trained research personnel. The delta and absolute error was calculated for each cube and then compared across variables (axis, material, inner vs. outer dimension, swath and machine/site/personnel) using ANOVA analysis.

Results: Print axis and inner vs. outer dimension of the model produced statistically significant differences in error while there was no statistically significant difference in the error for material, print swath, or machine/site/personnel. For the print axes, the printers produced an average error of 26, 53, and 57 μm and the error at three sigma was found to be 100, 158, and 198 μm for the Z, R, and Theta axes, respectively.

Conclusion: This study demonstrates that this novel protocol is both feasible and reliable for quality assurance in POC 3D printing across multiple sites. This protocol offers an adaptable framework that allows users to tailor the QA process to their specific needs. Through the comprehensive method, users can measure and identify all relevant factors that might introduce error into their printed product and then follow the most critical aspects for their situation across every print. The QA cubes produced via this protocol can provide guidance on print quality and alert users to unsatisfactory machine operation which could cause prints to fall outside of engineering and clinical tolerances.