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Abstract Electrohydrodynamic (EHD) printing is a versatile process that can be used to pattern high-resolution droplets and fibers through the deposition of an electrified jet. This highly complex process utilizes a coupled hydrodynamic and electrostatic mechanism to drive the fluid flow. While it has many biomedical, electronic, and filtration applications, its widescale usage is hampered by a lack of detailed understanding of the jetting physics that enables this process. In this paper, a numerical model is developed and validated to explore the design space of the EHD jetting process, from Taylor cone formation to jet impingement onto the substrate, and analyze the key geometrical and process parameters that yield high-resolution structures. This numerical model applies to various process parameters, material properties, and environmental factors and can accurately capture jet evolution, radius, and flight time. It can be used to better inform design decisions when using EHD processes with distinct resolution requirements.
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Prediction of electrohydrodynamic printing behavior using machine learning approaches
Abstract Electrohydrodynamic (EHD) printing has been used in various applications (e.g., sensors, batteries, photonic crystals). Currently, research on studying the relationships between EHD jetting behaviors, material properties, and processing conditions is still challenging due to a large number of parameters, cost, time, and the complex nature of experiments. In this research, we investigated EHD printing behavior using a machine learning (ML)-guided approach to overcome limitations in the experiments. Specifically, we investigated two jetting modes and the size of printed material with a broader range of material properties and processing parameters. We used samples from both literature and our own experiment results with different type of materials. Different ML models have been developed and applied to the data. Our results have shown that ML can navigate a vast parameter search space to predict printing behavior with an accuracy of higher than 95% during EHD printing. Moreover, the results showed that ML models can be used to predict the printing behavior and feather size for new materials. The ML models can guide the investigation of EHD printing and helped us understand the printing behavior in a systematic manner with reduced time, cost, and required experiments.
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- Award ID(s):
- 2150816
- PAR ID:
- 10570905
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- The International Journal of Advanced Manufacturing Technology
- Volume:
- 136
- Issue:
- 10
- ISSN:
- 0268-3768
- Format(s):
- Medium: X Size: p. 4439-4454
- Size(s):
- p. 4439-4454
- Sponsoring Org:
- National Science Foundation
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