Data-driven analysis of droplet morphology in inkjet systems: toward generating stable single-drop regimes

Abstract

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1140/epjs/s11734-025-01680-2.

The growing demand for new microelectronic devices and pharmaceutical advancements has heightened interest in inkjet printing as a means of high-precision manufacturing technique. This study leverages data-driven analyses to optimize droplet generation processes in a drop-on-demand dispensing system. A three-voltage pulse scheme was employed to produce droplets, with high-resolution images captured and processed to extract geometric features of the principal droplet. This resulted in a comprehensive, openly published dataset, along with a detailed, reproducible image processing pipeline. By analyzing this data, we identified key operational parameters and established correlations between inputs and outputs, providing insights into consistent single-droplet generation. These findings offer practical guidelines for controlling droplet morphology and advancing applications in inkjet printing.

This work was performed in the framework of DIDRO and DECIMA projects with references TED2021-130471B-I00 and PID2022-137472OB-I00 financed by the Agencia Estatal de Investigación of Ministerio de Ciencia, Innovación y Universidades via the following funds, respectively, MICIU/AEI/10.13039/501100011033 and European Union Next GenerationEU/ PRTR, and MICIU/AEI/10.13039/501100011033/ FEDER, UE.

Peer Reviewed

Postprint (author's final draft)