SYMPOSIA PAPER Published: 05 June 2020
STP162020180091

Performance Signature Qualification for Additively Manufactured Parts under Conditions Emulating In-Service Loading

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The micro- and meso-scale material morphologies generated by additive manufacturing (AM) processes can differ remarkably from those arising from conventional manufacturing (CM) methods. A consequence of this fact is the substandard functional performance of the produced parts that can limit the use of AM in some applications. In the present work, a rapid functional qualification methodology for AM-produced parts is presented. This method is based on a concept defined as absolute and differential performance signature qualification. The concept of performance signature (PerSig) is introduced both as a vector of featured quantities of interest (QoIs) and as a graphic representation in the form of radar or spider graph, representing the QoIs associated with the performance of relevant parts. The PerSigs are defined for both the prequalified CM parts and the AM-produced parts. Comparison measures are defined and enable the construction of differential PerSigs (dPerSig) in a manner that captures the differential performance of the AM part versus the prequalified CM one. The dPerSigs enable AM part qualification based on how their PerSigs are different from those of prequalified CM parts. After defining the steps of the proposed methodology, a description of its application is given for a part of an aircraft landing gear assembly and demonstrate its feasibility for the case of static loads. Furthermore, the extension of this methodology is introduced for a multiaxial loading environment intended to reproduce the proper loading conditions of more complex structures, by using the custom-designed six degrees of freedom testing frames.

Author Information

Michopoulos, John, G.
Computational Multiphysics Systems Lab, U.S. Naval Research Laboratory, Washington, DC, US
Steuben, John, C.
Computational Multiphysics Systems Lab, U.S. Naval Research Laboratory, Washington, DC, US
Iliopoulos, Athanasios, P.
Computational Multiphysics Systems Lab, U.S. Naval Research Laboratory, Washington, DC, US
Nguyen, Trung
Structures Division, Patuxent River, MD, US
Phan, Nam
Structures Division, Patuxent River, MD, US
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Details
Developed by Committee: F42
Pages: 550–572
DOI: 10.1520/STP162020180091
ISBN-EB: 978-0-8031-7687-4
ISBN-13: 978-0-8031-7686-7