High-Precision Drop-on-Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine Learning

Abstract

Direct printing methods are widely recognized as efficient techniques for manufacturing printed electronics. However, several challenges arise when printing on nonplanar surfaces, especially using the drop-on-demand (DoD) approach. These challenges include ink flow due to gravity, precise ink deposition, and reproducibility. This study introduces an innovative method for highly accurate DoD material jetting on nonplanar 3D conductive surfaces, enabling precise production and trajectory control of charged droplets. The technique involves using a grounded 3D substrate as the target, where in-flight droplets are subjected to an external electric field generated by gate electrode installed on a piezo activated droplet dispenser. Individual droplets are generated and controlled using a complex trigger system that relays variable-voltage signals to the gate electrode. Moreover, a predictive model for droplet deposition, exhibiting an accuracy of 87%, is developed utilizing supervised machine learning (ML). This approach significantly improves the accuracy and repeatability of droplet deposition. Overall, this study presents an effective method of integrating piezoelectric and electrohydrodynamic printing technologies, complemented by ML. It addresses the challenges associated with printing on nonplanar surfaces using the DoD material jetting technique and shows considerable promise for enhancing efficiency, accuracy, and repeatability in the manufacturing of printed electronics.