Abstract
Melt electrowriting (MEW) is a solvent-free (i.e., no volatile
chemicals), high-resolution 3D printing method that enables the
fabrication of semi-flexible structures with rigid polymers. Despite its
advantages, the MEW process is sensitive to changes in printing
parameters (e.g., voltage, printing pressure, and temperature), which
can cause fluid column breakage, jet lag, and/or fiber pulsing,
ultimately deteriorating the resolution and printing quality. In spite
of the commonly used error-and-trial method to determine the most
suitable parameters, here, we present a machine learning (ML)-enabled
image analysis-based method for determining the optimum MEW printing
parameters through an easy-to-use graphical user interface (GUI). We
trained 5 different ML algorithms using 168 MEW 3D print samples, among
which the gaussian process regression ML model yielded 93% accuracy of
the variability in the dependent variable, 0.12329 on root mean square
error for the validation set and 0.015201 mean square error in
predicting line thickness. Integration of ML with control feedback loop
and MEW can reduce the error-and-trial steps prior to the 3D printing
process, decreasing the printing time (i.e., increasing the overall
throughput of MEW) and material waste (i.e., improving the
cost-effectiveness of MEW). Moreover, embedding trained ML model with
the feedback control system in a GUI facilitates a more straightforward
use of ML-based optimization techniques in the industrial section (i.e.,
for users with no ML skills).