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Session: Multi-Disciplinary General ePoster Viewing [Return to Session]

A Quality Assurance Tests for 3D Printing Lab in Biomedical Physics Department

E Elsaiedy , A Nobah, S Alhzani, B Moftah, E Elsaiedy *, KFSH&RC, Riyadh, 01SA,


PO-GePV-M-300 (Sunday, 7/10/2022)   [Eastern Time (GMT-4)]

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Purpose: To describe the quality assurance QA process for the 3D printing lab in the biomedical physics department using FDM 3D printers.

Methods: A phantom was designed using Autodesk Fusion 360 CAD designing software, PrusaSlicer 2.3 slicing software, along with and Prusa i3 MK3S 3D printer is used for this task. The suggested checks provide tests verifying the performance of the printer in the x, y, and z directions. Four tests used to check the accuracy of the printing process: Spatial resolution Line pair per cm, angled lines, cylinders with varying diameters and heights, and square and circles. The QA evaluation is based on comparing the dimensions of the digital model with the physical measurements obtained by caliber and protractor. The nozzle size used in this abstract is 0.4mm and the material used to print the testing model is PETG. For the cylinders, the height of the cylinder was 2cm and the layer height slicing parameters used were 0.20, 0.25, and 0.30mm to test whether the printer will print the height as per the digital model or will introduce uncertainty related to the layer heights.

Results: The difference in dimensions between the digital model and printed phantom in X Y direction was -0.05mm to 0.13mm for line pairs per cm, -0.03mm to 0.06mm for circles and square dimensions, and 0.07-0.3 degrees for the angled line. For the cylinder model, the difference ranged between 0.01mm and 0.25mm for the height of cylinders.

Conclusion: A QA model has been developed to check the accuracy and reproducibility of objects printed with 3D Printer. If the height of the model is not a multiple of the slice height, then the dimension of the phantom along the Z direction will not be accurate.


CAD, Quality Assurance


IM- Dataset Analysis/Biomathematics: Machine learning

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