Abstract
As we continue to 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 presented 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 physics-dominated electrohydrodynamic inkjet printing (EHD printing). EHD printing is a process that uses electrical force to control material flows, thus achieving fabrication of electronics under zero-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 exploited. We found our method obtains good interpolation accuracy (MAPE 2.88%) compared to traditional machine learning methods (MAPE 16.84%) for predicting the printed line width. We concluded that KRR-HeITL method is well suited for zero-gravity domain shifts of EHD printing parameters. This study paved the way for future predictions of ISM parameters when there are only on-ground experiments or limited zero-gravity datasets for a given process.
