BIO

Anthony Psulkowski is an accomplished third-year industrial engineering student at Florida State University. In his current role as a research assistant at the High-Performance Materials Institute, his focus has been on the cutting-edge additive manufacturing of electronic components and the implementation of error-prevention techniques. Anthony's unique intellectual abilities, characterized by his critical thinking and problem-solving skills, make him an invaluable asset to the professional lab setting. He aspires to deepen his knowledge through advanced studies in optimization, robotics, and electronic manufacturing, with the ultimate aim of pursuing a distinguished career in the defense and aerospace industry.

In-situ Electrical Characterization for Local Feature Segmentation and Material-Driven Control

Abstract

Demand for electronics and embedded systems augmented by Additive Manufacturing (AM) continues to increase across a multitude of industries, largely governed by, lower investment costs, a growing material library, and heightened flexibility in application. As the greater research community has gravitated towards knowledge-based design, IIOT driven in-situ monitoring provides real-time informatics to detect and classify errors that arise during fabrication. Within Material Extrusion (MEX), propagative errors including extrusion inconsistencies, bed adhesion failure, and layer shifting have the potential to cascade throughout the entire print, hampering wide-scale adoption in the industry as this lack of reliability of parts used in critical processes or applications. The following investigation showcases the implementation of a material-driven control method that enables real-time monitoring of printed electronics. Building upon volumetric ohmic models, in-situ electrical characterization of MEX structures enables forecastable features throughout the build volume of a 3D printed structure. The regime not only facilitates segmentation of local features throughout the build volume, but demonstrates a 90% accuracy to conventional methods, and repeatability to order of 85% as a statistically viable means to qualify part density. Collected at a rate of 100 kHz, these findings can reduce failure rates and improve the reliability of 3D printed systems to the precision of <1μm of the printed segment, broadening both the utility and application of MEX in intelligent-manufacturing industries.

Keywords: Additive Manufacturing, In-Situ Prognostics, Real-Time Defect Monitoring, Fabrication Anomalies, Material-Driven Control