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Abstract Direct ink writing (DIW) is an extrusion-based additive manufacturing technology. It has gained wide attentions in both industry and research because of its simple design and versatile platform. In electric-field-assisted Direct Ink Writing (eDIW) processes, an external electric field is added between the nozzle and the printing substrate to manipulate the ink-substrate wetting dynamics and therefore optimize the ink printability. eDIW was found effective in printing liquids that are typically difficult to print in the conventional DIW processes. In this paper, an eDIW process modeling system based on machine learning (ML) algorithms is developed. The system is found effective in predicting eDIW printing geometry under varied process parameter settings. Image processing approaches to collect experiment data are developed. Accuracies of different machine learning algorithms for predicting printing results and trace width are compared and discussed. The capabilities, applications and limitations of the presented machine learning-based modeling approach are presented. 

Abstract Nature has examples of impressive surfaces and interfaces with diverse wettability stemming from superhydrophilicity to superhydrophobicity. The multiscale surface structures found in biological systems generally have high geometric complexity, which makes it challenging to replicate their characteristics, especially using traditional fabrication techniques. It is even more challenging to fabricate such complex microstructures with tunable wettability. In this paper, we propose a method to tune the wettability of a microscale surface by changing the geometrical parameters of embedded microstructures in the surface. By taking inspiration from an insect (springtails), we designed micropillar arrays with different roughness by adjusting geometric parameters such as reentrant angle, pitch distance, and the number of spikes and pillars. This study shows that, by changing geometrical parameters in microscale, the apparent contact angle, and hence the surface wettability can be calibrated. The microscale pillars were fabricated using a precise microdirect light processing (μDLP) three-dimensional (3D) printer. Different printing parameters were studied to optimize the geometric parameters to fabricate 3D hierarchical structures with high accuracy and resolution. The largest apparent contact angle in our experiments is up to 160 deg, with pillars of 0.17 mm height and 0.5 mm diameter, 55 deg reentrant angle, and a spacing of 0.36 mm between pillars. The lowest contact angle is ∼35 deg by reducing the pillar size and spacing. By controlling the size of different features of the pillar, pillar number, and layout of the mushroom-shaped micropillars, the wettability of the surface is possible to be tuned from a highly nonwetting liquid/material combination to highly wetting material. Such wettability tuning capability expands the design space for many biomedical and thermofluidic applications.