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<p>Emerging electrohydrodynamic (EHD) jet printing processes have opened new pathways for manufacturing medical devices and sensors, offering the ability to fabricate complex geometries with jet diameter resolutions on the order of only a few micrometers. Despite this promise, EHD printing remains highly dynamic and strongly dependent on multiple process parameters that directly influence printing quality. Current calibration practices rely heavily on inefficient trial-and-error approaches, which hinder the broader adoption of these techniques as reliable manufacturing processes. In this work, we introduce <b>JetCal</b>, a data-driven optimization pipeline that integrates process physics to calibrate melt electrowriting (MEW) machines in a time- and cost-efficient manner. The framework combines a multi-physics model, capable of predicting the jet diameter profile, with a geometrical model that captures jet trajectory and lag distance. These models are coupled with a Bayesian optimization algorithm to establish a pipeline capable of pre-calibrating MEW machines virtually. Compared to conventional, labor-intensive calibration, JetCal significantly accelerates the identification of process parameters while improving printing quality. By embedding physics into a data-driven optimization framework, JetCal represents a step toward autonomous and intelligent MEW systems.</p>

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JetCal: a physics-based optimization framework for calibrating melt electrowriting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Athanasios Oikonomou,
  • Theodoros Loutas

摘要

Emerging electrohydrodynamic (EHD) jet printing processes have opened new pathways for manufacturing medical devices and sensors, offering the ability to fabricate complex geometries with jet diameter resolutions on the order of only a few micrometers. Despite this promise, EHD printing remains highly dynamic and strongly dependent on multiple process parameters that directly influence printing quality. Current calibration practices rely heavily on inefficient trial-and-error approaches, which hinder the broader adoption of these techniques as reliable manufacturing processes. In this work, we introduce JetCal, a data-driven optimization pipeline that integrates process physics to calibrate melt electrowriting (MEW) machines in a time- and cost-efficient manner. The framework combines a multi-physics model, capable of predicting the jet diameter profile, with a geometrical model that captures jet trajectory and lag distance. These models are coupled with a Bayesian optimization algorithm to establish a pipeline capable of pre-calibrating MEW machines virtually. Compared to conventional, labor-intensive calibration, JetCal significantly accelerates the identification of process parameters while improving printing quality. By embedding physics into a data-driven optimization framework, JetCal represents a step toward autonomous and intelligent MEW systems.