Electrohydrodynamic (EHD) inkjet printing has gained widespread attention in electronics, biomedicine, and materials science for its exceptional resolution and printing versatility. However, the droplet formation process is governed by complex interactions between driving waveform parameters and fluid properties, making traditional trial-and-error optimization inefficient. To address this, a hybrid approach combining numerical simulation, machine learning regression, and genetic algorithm optimization is proposed to achieve precise control of droplet diameter. A multiphysics numerical model is...
