Description: For further space exploration, NASA hopes to establish a long-term presence on the Moon and Mars. Artemis Base Camp is a planned establishment where astronauts can live and work on the Moon. It will be home to a lunar lodge, where NASA plans to house astronauts for month-long stays to conduct experiments and research. Additionally, the spaceship, Gateway, will be placed in lunar orbit to act as a 'pit-stop' between the Earth and Artemis. Both structures will require flexible manufacturing solutions to support critical life-supporting technologies. Electrohydrodynamic inkjet printing (EHD printing) has tremendous potential to advance In- Space Manufacturing (ISM) and address the need for flexible on-demand fabrication, repair, and recycling capabilities for electronic components in space. I plan to work in a research team in Industrial and Systems Engineering at UW-Madison for the proposed work. Our team has been teaming with NASA to work towards On-Demand Manufacturing of Electronics (ODME). Our goal is to realize thin-film fabrication for the International Space Station (ISS) and other On-orbital Services, Assembly and Manufacturing (OSAM) need. Upon success, we can provide manufacturing capabilities for ISM of all different types of sensors, actuators, and other electronic devices. This will be an essential tool to make sure America can maintain long-term missions and human presence in low earth orbital with a strong commercial market considering sensors/actuators are most likely to be consumables for in-space uses. The research objective is to develop a method to continuously monitor, adjust, and optimize EHD printing parameters autonomously to achieve the necessary quality required of micro and nano scale conductive patterns. This closed-loop, in-situ system can be applied to biomedical, electrical, and aerospace applications of EHD printing, and needs to be created to advance its potential. My central hypothesis is that physic-model-assisted machine learning reduces the prediction time of droplet behavior 10-50 times faster than traditional imaging processing based machine learning algorithms; thus allowing near real-time system monitoring and process control. Three research objectives are: 1) establish a machine learning framework fast and ‘smart’ enough to predict future droplet behavior in EHD printing; 2) create a closed-loop control system to monitor, adjust, and optimize the process parameters in real-time; 3) test quality control of the system in different atmospheric environments. Upon success, EHD printing will allow for sensors, displays, transistors, and optical devices to be manufactured in space, ensuring repair and replacement capabilities, and enabling further space exploration. In addition to the aerospace industry, EHD printing has tremendous potential in the biomedical industry. EHD printing can mimic the microarchitecture of tissues, cartilage, tendons, and blood vessels. EHD printing creates conductive scaffolds that guide specific cellular alignments and have conductive properties that allow for muscle contraction in cardiac tissues. These discoveries tug on personal heartstrings, as they could have helped my mother's heart condition and complications if they were advanced enough. It is my mission to apply the system I develop for EHD printing to conductive tissue engineering as well. I plan to further my breadth of knowledge by pursuing my Ph.D. in advanced manufacturing systems. Through the rigorous study of EHD printing and 3 additional years of being a Lab Instructor, I can confidently step into academia to become a successful professor or NASA research scientist. This experience will allow me to become an accredited public school consultant to enhance STEM education in rural underfunded schools. This NSTGRO fellowship is a remarkable opportunity to further my future research and charity endeavors, and it would enable me to pursue these passions and build a successful future NASA career.