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Selected Mechanical and Rheological Properties of Medical Resin MED610 in PolyJet Matrix Three-Dimensional Printing Technology in Quality Aspects.

Jerzy BochniaTomasz KoziorWiktor SzotMateusz RudnikPaweł ZmarzłyDamian GogolewskiPaweł SzczygiełMateusz Musiałek
Published in: 3D printing and additive manufacturing (2024)
In connection with the growing demand of the medical and medicine-related industry for materials exhibiting biocompatible properties used as part of three-dimensional (3D) printing additive technologies. The article presents research results concerning rheological and selected mechanical properties of a modern, photocurable MED610 resin, which is also used mainly in medicine, as well as dentistry. The article also shows extensive results of testing bending stress relaxation and creep, as well as the tensile strength of samples created with the PolyJet Matrix (PJM) technology. The authors used various sample types, including ones of unique shape and a hexagonal cellular structure. The analysis of the impact of element orientation on the working platform of the machine (3D printer) on the obtained test results (so-called printing direction-Pd) was also taken into account as a key technological parameter of the 3D printing process. Experimental rheological curves were matched with theoretical curves resulting from the application of a five-parameter Maxwell-Wiechert (M-W) model in the case of stress relaxation and a five-parameter Kelvin-Voigt model for creep. Very good matches were achieved, mean coefficients Chi 2  = 0.0014 and R 2  = 0.9956 for matching the five-parameter M-W model and mean coefficients Chi 2  = 0.000006 and R 2  = 0.9992 enable recommending the obtained results to be used for various engineering calculations, especially computer simulations. Moreover, the use of relaxation curves can significantly increase the construction capabilities within the design process, which includes the MED610 material.
Keyphrases
  • healthcare
  • deep learning
  • molecular dynamics
  • high throughput
  • stress induced
  • machine learning
  • monte carlo
  • heat stress
  • high density
  • neural network