  Electrohydrodynamic-jet (e-jet) printing technique enables the
high-resolution printing of complex soft electronic devices. As such, it has an
unmatched potential for becoming the conventional technique for printing soft
electronic devices. In this study, the electrical conductivity of the e-jet
printed circuits was studied as a function of key printing parameters (nozzle
speed, ink flow rate, and voltage). The collected experimental dataset was then
used to train a machine learning algorithm to establish models capable of
predicting the characteristics of the printed circuits in real-time. Precision
parameters were compared to evaluate the supervised classification models.
Since decision tree methods could not increase the accuracy higher than 71%,
more advanced algorithms are performed on our dataset to improve the precision
of model. According to F-measure values, the K-NN model (k=10) and random
forest are the best methods to classify the conductivity of electrodes. The
highest accuracy of AdaBoost ensemble learning has resulted in the range of
10-15 trees (87%).
