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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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This content will become publicly available on December 1, 2025
Heterogeneous Transfer Learning of Electrohydrodynamic Printing Under Zero-Gravity Toward In-Space Manufacturing
Abstract As we continue to commercialize space and mature in-space manufacturing (ISM) processes, there is a strong need to transfer the knowledge we learn from experiments on the ground to zero-gravity environments. Physics-motivated manufacturing processes, like additive manufacturing, experience a shift in fabrication parameters due to the absence of gravity and the change of environments. Thus, we found traditional machine learning methods are not capable of addressing this domain shift and present a transfer learning scheme as a solution in this paper. We tested a kernel ridge regression model built for heterogeneous transfer learning (KRR-HeITL) on data from the electrohydrodynamic inkjet printing (EHD printing) process. EHD printing is a process that uses electrical force to control material flows, thus achieving the fabrication of electronics without requiring gravity. Our team has successfully conducted three rounds of parabolic flights to validate this technology for ISM. We trained on multiple datasets built from on-ground experiments and tested using zero-gravity printing data obtained from parabolic flight tests. Measurements of the Taylor cone both on-ground and in zero-gravity were taken and exploited as a part of the training data. We found that our method obtains good interpolation accuracy (MAPE 3.85%) compared to traditional machine learning methods (MAPE 16.84%) for predicting the printed line width. We concluded that the KRR-HeITL method is well suited for zero-gravity domain shifts of EHD printing parameters. This study paves the way for future predictions of ISM parameters when there are only on-ground experiments or very limited zero-gravity datasets for a given process.
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- Award ID(s):
- 2436024
- PAR ID:
- 10609849
- Publisher / Repository:
- Journal of Manufacturing Science and Engineering
- Date Published:
- Journal Name:
- Journal of Manufacturing Science and Engineering
- Volume:
- 146
- Issue:
- 12
- ISSN:
- 1087-1357
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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