A practical framework for predicting conversion profiles in vat photopolymerizations

Jianwei Tu, Yaser Kashcooli, Nicolas J. Alvarez, Giuseppe R. Palmese

Research output: Contribution to journalArticlepeer-review

Abstract

Monomer fractional conversion is an important indicator of critical physical and thermal properties of thermosetting polymers. Acrylic photo-resins are widely used in light-based 3D printing techniques. The green conversion of acrylate double bonds affects the performance of 3D printed parts before and after post-processing. This work describes a practical framework for predicting conversion profiles of green parts produced by digital light processing (DLP) vat photopolymerization, although the principle should be applicable to all light-based vat photopolymerization techniques. The practical model utilizes accumulated dose profiles and photopolymerization kinetics to make predictions of conversion profiles. Photopolymerization kinetics was determined by real-time photo-infrared spectroscopy, and a phenomenological model was employed to describe the cure kinetics which included oxygen inhibition period and dose rate dependency. A sub-linear dependence of cure kinetics on dose rate showed that effective dose rather than actual dose determined the degree of cure. A mathematical model for accumulated effective dose was developed, and the effects of the ratio of layer thickness to resin depth of penetration on accumulated effective dose profiles were discussed. Predicted conversion profiles from the model were compared to experimental z-direction conversion profiles measured by IR microscope. Further, a Matlab program was developed for predicting and tuning z-direction conversion profiles in DLP vat photopolymerizations with user-input printing parameters. Using the program, the model predicted a relatively flat conversion profile by over-exposing the last layer. The prediction was experimentally verified which showed the effectiveness of this simple model.

Original languageEnglish (US)
Article number103102
JournalAdditive Manufacturing
Volume59
DOIs
StatePublished - Nov 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Materials Science(all)
  • Engineering (miscellaneous)
  • Industrial and Manufacturing Engineering

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