UC Santa Cruz

Additive manufacturing (AM) lends itself as a powerful tool which leverages the automated fabrication of digital product definitions through the computer numerical control of physical toolpaths. The process conventionally requires modification of these digital definitions for compatibility with the physics governing that fabrication process; this commonly leads to unoptimized solutions due to design-for-manufacturability constraints. The sensitivity of AM to the variability of feedstock quality, machine calibration, and accuracy drives the need for frequent characterization of fabricated objects for a robust material process. The constant testing is fiscally and logistically intensive, often requiring coupons that are manufactured and tested in independent facilities. By modeling the expected performance of an object and comparing the differences from expected to physical observations of the object through multiple dimensions of sensing, one may start to infer, characterize, validate, and even potentially qualify and standardize a component's actual implemented realization. As a step towards integrating testing and characterization into the AM process while reducing cost, I demonstrate Automated Testing and Characterization of AM (ATCAM). ATCAM is configured for Fused Deposition Modeling (FDM) and introduces the concept of dynamic coupons to generate large quantities of basic AM samples. An in-situ actuator is printed on the build surface to deploy coupons through impact, which is sensed by a load cell system utilizing machine learning (ML) to correlate AM data. By employing ATCAM through the automated high-volume testing of derivated fabricated objects capable of flight, I use the basis of ATCAM to consider the aeronautical characterization and manufacturability of a design for its environment through an automated network of systems comparing and characterizing the performance of an aircraft they manufactured, deployed, and sensed cooperatively.