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In-Situ Monitoring for Local Feature Segmentation and Material-Driven Contraol


Title: In-Situ Monitoring for Local Feature Segmentation and Material-Driven Contraol

Authors: Anthony W. Psulkowski, Sean C. Psulkowski, Bryant Rodriguez, Tarik J. Dickens

DOI: 10.33599/nasampe/c.23.0084

Abstract: Demand for electronics and embedded systems augmented by Additive Manufacturing (AM) continues to increase across many industries, 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 subsequent investigation showcases implementing 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 facilitates local feature segmentation throughout the build volume and demonstrates a contactless, non-destructive, and repeatable 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.

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Conference: CAMX 2023

Publication Date: 2023/10/30

SKU: TP23-0000000084

Pages: 11

Price: $22.00

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