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Title: Internal Porosity Detection in Additively Manufactured Parts via Electromechanical Impedance Measurements
The flexibility offered by additive manufacturing (AM) technologies to fabricate complex geometries poses several challenges to non-destructive evaluation (NDE) and quality control (QC) techniques. Existing NDE and QC techniques are not optimized for AM processes, materials, or parts. Such lack of reliable means to verify and qualify AM parts is a significant barrier to further industrial adoption of AM technologies. Electromechanical impedance measurements have been recently introduced as an alternative solution to detect anomalies in AM parts. With this approach, piezoelectric wafers bonded to the part under test are utilized as collocated sensors and actuators. Due to the coupled electromechanical characteristics of piezoelectric materials, the measured electrical impedance of the piezoelectric wafer depends on the mechanical impedance of the part under test, allowing build defects to be detected. This paper investigates the effectiveness of impedance-based NDE approach to detect internal porosity in AM parts. This type of build defects is uniquely challenging as voids are normally embedded within the structure and filled with unhardened model or supporting material. The impact of internal voids on the electromechanical impedance of AM parts is studied at several frequency ranges.  more » « less
Award ID(s):
1635356
NSF-PAR ID:
10079218
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
Page Range / eLocation ID:
V001T08A009
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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