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3D Printing of Compositional Gradients Using the Microfluidic Circuit Analogy
Author(s) -
Nguyen Du T.,
Yee Timothy D.,
Dudukovic Nikola A.,
Sasan Koroush,
Jaycox Adam W.,
Golobic Alexandra M.,
Duoss Eric B.,
DyllaSpears Rebecca
Publication year - 2019
Publication title -
advanced materials technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.184
H-Index - 42
ISSN - 2365-709X
DOI - 10.1002/admt.201900784
Subject(s) - computer science , inkwell , polydimethylsiloxane , calibration , 3d printing , microfluidics , session (web analytics) , biological system , materials science , mechanical engineering , nanotechnology , engineering , mathematics , speech recognition , statistics , world wide web , biology
3D printing of structures with compositional gradients requires accurate dispensing control to achieve desired profiles. Here, empirical data are used with a model based on the microfluidic circuit analogy (MCA) to project dispense rate profiles that yield improved compositional accuracy in the printed part. Since minor variation in the experimental setup for each printing session can result in significant changes, a calibration procedure is developed to measure the system response. This calibration enables the extraction of the empirical MCA model parameters specific to each print session. Using the empirical parameters, the MCA model then can be used to predict appropriate dispense rates for the desired composition profile and toolpath of interest. The MCA model is validated experimentally by direct ink write 3D printing of compositional gradients using viscoelastic polydimethylsiloxane inks and is shown to improve accuracy to desired profile by a factor of 2–5. This approach enables a new route to 3D print structures with arbitrarily complex compositional gradients.