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Online prognosis of fatigue crack at welded joints using nonlinear ultrasonic modulation
Author(s): Hyung Jin Lim; Hoon Sohn
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Paper Abstract

In this study, an online residual fatigue life estimation (prognosis) technique based on nonlinear ultrasonic modulation is developed for a welded joint. When two ultrasonic input signals are applied to a structure at two distinct frequencies, resulting crack opening-and-closing generates modulation components at the summation and difference of the two input frequencies. For prognosis, a fatigue index is defined and extracted from nonlinear ultrasonic modulation components obtained from a target structure. Based on Nazarov-Sutin and Paris-Erdogan theories, the fatigue index is formulated as a simple power function of the loading cycles applied to the target structure. Next, a residual fatigue life is estimated by fitting a power function to the ultrasonic modulation data obtained up to the current loading cycles and extrapolating the fitted power function to the failure point. Finally, the proposed prognosis technique is validated using test data obtained from aluminum (6061-T6) plate specimens with a welded joint. The uniqueness of this paper lies in (1) definition of a fatigue index based on nonlinear ultrasonic modulation components, (2) theoretical formulation of the fatigue index as a power function of loading cycles, (3) online fatigue crack prognosis based on nonlinear ultrasonic measurements, and (4) experimental validation using welded aluminum plates under cyclic loading.

Paper Details

Date Published: 27 March 2019
PDF: 8 pages
Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 109701H (27 March 2019); doi: 10.1117/12.2513830
Show Author Affiliations
Hyung Jin Lim, KAIST (Korea, Republic of)
Hoon Sohn, KAIST (Korea, Republic of)

Published in SPIE Proceedings Vol. 10970:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019
Jerome P. Lynch; Haiying Huang; Hoon Sohn; Kon-Well Wang, Editor(s)

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