A Spectral Correlation Based Nonlinear Ultrasonic Resonance Technique for Fatigue Crack Detection

2021 ◽  
Author(s):  
Junzhen Wang ◽  
Yanfeng Shen
Author(s):  
Junzhen Wang ◽  
Yanfeng Shen

Abstract This paper presents a spectral correlation based nonlinear ultrasonic resonance technique for fatigue crack detection. A reduced-order nonlinear oscillator model is initially constructed to illuminate the Contact Acoustic Nonlinearity (CAN) and nonlinear resonance phenomenon. The tailored analytical model considers the rough surface condition of the fatigue cracks, with a crack open-close transition range for the effective modeling of the variable-stiffness CAN. Multiple damage indices (DIs) associated with the degree of nonlinearity of the interrogated materials are then proposed by correlating the ultrasonic resonance spectra. The frequency sweeping signals serve as the excitation waveform to obtain the structural dynamic features. The nonlinear resonance procedure is numerically solved using the central difference method. Short time Fourier transform (STFT) is utilized to extract the resonance spectroscopy. In this study, pristine, linear wave damage interaction case (an open notch case), and nonlinear wave damage interaction case (a fatigue crack case) with various damage severities are considered. Subsequently, three case studies taking advantage of different nonlinear oscillation phenomena are conducted based on the spectral correlation algorithm to detect and monitor the fatigue crack growth: time-history dependence, amplitude dependence, and breakage of superposition. Each of these three nonlinear behaviors can either work individually or collaborate synthetically to detect the nucleation and growth of the fatigue cracks. The proposed nonlinear ultrasonic resonance technique possesses great application potential for fatigue crack detection and quantification. This paper finishes with summary, concluding remarks, and suggestions for future work.


Author(s):  
Yanfeng Shen ◽  
Nipon Roy ◽  
Junzhen Wang ◽  
Zixuan Liu ◽  
Danyu Rao ◽  
...  

This paper investigates the amplitude and sweeping direction dependent behavior of nonlinear ultrasonic resonance spectroscopy for fatigue crack detection. The Contact Acoustic Nonlinearity (CAN) and the nonlinear resonance phenomena are illuminated via a reduced-order bilinear oscillator model. Unlike conventional linear ultrasonic spectroscopy, which would not change its pattern under different amplitudes of excitation or the frequency sweeping direction, the nonlinear resonance spectroscopy, on the other hand, may be noticeably influenced by both the wave amplitude and the loading history. Both up-tuning and down-tuning sweeping active sensing tests with various levels of excitation amplitudes are performed on a fatigued specimen. Short time Fourier transform is adopted to obtain the time-frequency features of the sensing signal. Corresponding to each excitation frequency, a nonlinear resonance index can be established based on the amplitude ratio between the superhamronic, the subharmonic, the mixed-frequency response components and the fundament frequency. The measured nonlinear resonance spectroscopy for a certain amplitude and frequency sweeping direction can be readily used to establish an instantaneous baseline. The spectroscopy of a different amplitude or frequency sweeping direction can be compared with such an instantaneous baseline and a Damage Index (DI) is obtained by measuring the deviation between the two spectra. Experimental investigations using an aluminum plate with rivet hole nucleated fatigue cracks are performed. A series of nonlinear spectroscopies are analyzed for both the pristine case and the damaged case. The spectral features for both cases are obtained to demonstrate the proposed fatigue crack detection methodology which may find its application for structural health monitoring (SHM). The paper finishes with summary, concluding remarks, and suggestions for future work.


2014 ◽  
Vol 333 (5) ◽  
pp. 1473-1484 ◽  
Author(s):  
Hoon Sohn ◽  
Hyung Jin Lim ◽  
Martin P. DeSimio ◽  
Kevin Brown ◽  
Mark Derriso

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