Sandia Instrumented 3D Printer Ryan Thorna, Mike Muldoon, Jonathan Eames, Maseo Browning, Justin Rueb, Jacob Lowe Advisors: Edward Hanzlik, Nicholas Leathe Background and Motivation 3D printers are on the rise for both the commercial end-user and manufacturers. Manufacturers who utilize this technology see a decrease in engineering time, and a decrease in the physical design constraints. However, quality control of a 3D printed part is a major drawback that restricts these parts from use in high consequence applications. The Sandia Instrumented 3D Printer aims to implement a sensor system to learn about print failures and avoid them in future prints. Selection Criteria and Benchmarking The team spent a significant amount of time determining the needs of the 3D printer and the equipment to outfit it with. The team was interested in acquiring a system that would be open to modification and has great reliability. The BCN3D Sigma was chosen due to its open-source software and high quality resolution. Common Print Errors and Failures Testing Process Bed adhesion failure occurs when the bed is too cold. Complicated test part that has several specifics. Simplistic test part that has a few specifics. Part to test the tensile strength capability. Stringing occurs when the extruder is too hot. In-order to perform an accurate test and gain useful data, the team set up tests that would hold certain variables as constant while others were changed to see the effect on the printed objects. This was done primarily with the Benchy part, and to a limited extent, some NIST parts. Layer adhesion failure occurs when the part temperature is not uniform. Warping occurs when the temperature is too cold or a draft is present. Data Collection and Results Future Outlook While the team got a significant amount of work done, more progress can still be made. With the team’s remaining budget, a second printer will be bought to allow a future team to carry on the team’s work and design a closed loop feedback system to control the print. Future work could include ease of data representation, ease of use, and implementation of an improved PID controller, among other improvements. The collected data is comprised of three aspects: analysis of the print, heat bed, and ambient temperature. A data logging subsystem implements sensors to measure the thermal gradient of the heat bed and the printer’s ambient temperature. Object temperature is evaluated with a thermal camera run by a Raspberry Pi. References and Acknowledgements The data collected from each print is sent to a CSV (Comma Separated Value) file which can be used to further extrapolate points of failure for the print. The team would like to acknowledge our guide, Edward Hanzlik as well as our customer, Nicholas Leathe of Sandia National Laboratories for providing feedback and guidance throughout the project. The team would also like to acknowledge the RIT construct lab and Dr. Cormier for insight into 3D printing.