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Title: Electromechanical Impedance Based Part Identification via Linear Projection
Abstract The geographical separation between various supply chain participants creates challenges in ensuring the integrity of the parts under circulation. These supply chains have to regularly deal with counterfeiting, a significant problem with an estimated value equivalent to at least the tenth-largest global economy. Industries are constantly upgrading their anti-counterfeiting methods to tackle this ever-increasing issue. Traditionally, a physical or cyber-physical part identifier is used to assert the integrity and identity of parts moving through the supply chain. For this work, we propose the use of electromechanical impedance measurements to generate a robust, unique part identifier linked to physical attributes. Electromechanical impedance measurements have been employed as a basis for non-destructive evaluation techniques in damage detection and health monitoring. We propose using these high-frequency measurements recorded through bonded piezoceramic transducers to help uniquely identify parts. For this study, identical piezoceramic transducers (cut from the same wafer to minimize variations) were mounted on identically manufactured specimens. The only distinction between these specimens was the physical variation introduced during manufacturing and instrumentation. Multiple measurements for each specimen were recorded. A unique part identification methodology based on linear projection was created using these measurements. Lastly, a leave-one-out-study was performed to uniquely identify the left-out specimen. This was used to validate the part identification methodology. This paper introduces the use of electromechanical impedance measurements (widely adopted for damage detection) as a unique part identifier, with a basic experimental implementation of the proposed mechanism on identically manufactured parts. The paper also highlights some challenges and future work needed to make this methodology robust and adaptable.  more » « less
Award ID(s):
1932213
PAR ID:
10486351
Author(s) / Creator(s):
;
Publisher / Repository:
American Society of Mechanical Engineers
Date Published:
Journal Name:
Proceedings of the ASME 2023 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
ISBN:
978-0-7918-8752-3
Format(s):
Medium: X
Location:
Austin, Texas, USA
Sponsoring Org:
National Science Foundation
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