This study presents a novel droplet-jet mode of near-field electrospinning which allows microscale control of helix patterns using nanofiber. While the cone-jet mode has been used to generate a printable nanofiber to date, the cone-jet mode requires a high applied voltage with a long jet travel distance to obtain the nanofiber, resulting in a large coiling diameter. In order to integrate the near-field electrospinning into microscale devices, it is important to achieve a comparable nanofiber pattern resolution. Herein, we have demonstrated printing of nanofibers with a coiling diameter of sub-10 µ m, which is produced by the droplet-jet mode of near-field electrospinning. Also, it is found that the coiling diameter, wavelength, and frequency can be controlled by the droplet size as one of the process parameters in the droplet-jet mode. The droplet-jet mode will be a promising near-field electrospinning technique to apply direct-written nanofibers to various micro-scale applications.

Existing studies have successfully identified optimal process parameters to enhance relative density and mechanical properties. However, these optimizations often incur high experimental costs due to the need for repetitive experiments. This research introduces an efficient optimization framework using two design of experiment (DoE) methods and machine learning algorithms, aimed at optimizing the magnetic properties of Fe–4.5Si material. The framework consists of three steps: (1) identification of the process parameter region that ensures high relative density using cubic specimens, (2) development of surrogate models to predict magnetic properties, such as maximum relative permeability and core loss, using toroidal specimens, and (3) multi-objective optimization using the surrogate models and a multi-objective genetic algorithm (MOGA) to enhance magnetic properties. The results demonstrate that this method can efficiently optimize process parameters with a limited number of experiments. Furthermore, the versatility of this framework allows its application to other materials in the early stages of research, even without prior knowledge of specific materials.