Fatigue Damage Detection

2009 ◽  
pp. 123-123-105 ◽  
Author(s):  
JR Barton ◽  
FN Kusenberger
Keyword(s):  
Damage Detection ◽  
Fatigue Damage

Fatigue damage detection using a speckle-contrast technique

2009 ◽  
Vol 47 (3-4) ◽  
pp. 398-402 ◽  
Author(s):  
A. Dávila ◽  
G. Garnica ◽  
J.A. López ◽  
F.J. Carrión
Keyword(s):  
Damage Detection ◽  
Fatigue Damage ◽  
Speckle Contrast ◽  
Contrast Technique

Fatigue Damage Detection in Composite Rods Using Fiber Optic Intensity Based Sensors

2004 ◽  
Vol 453-454 ◽  
pp. 497-504
Author(s):  
Lj. Brajović ◽  
Z. Mišković ◽  
P.S. Uskoković ◽  
I. Živković ◽  
R. Aleksić
Keyword(s):  
Damage Detection ◽  
Fatigue Damage ◽  
Fiber Optic

Fatigue damage detection using cyclostationarity

2015 ◽  
Vol 58-59 ◽  
pp. 128-142 ◽  
Author(s):  
D. Boungou ◽  
F. Guillet ◽  
M. El Badaoui ◽  
P. Lyonnet ◽  
T. Rosario
Keyword(s):  
Damage Detection ◽  
Fatigue Damage

scholarly journals A Data-Driven Framework for Early-Stage Fatigue Damage Detection in Aluminum Alloys Using Ultrasonic Sensors

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 211
Author(s):  
Susheel Dharmadhikari ◽  
Chandrachur Bhattacharya ◽  
Asok Ray ◽  
Amrita Basak
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Damage Detection ◽  
Fatigue Damage ◽  
Early Stage ◽  
Confocal Microscope ◽  
Recognition Algorithm ◽  
Pattern Recognition Algorithm ◽  
Notched Specimens ◽  
Digital Microscope

The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultrasonic sensors and, thereafter, tested on an MTS apparatus integrated with a confocal microscope and a digital microscope. The confocal microscope is focused on the notch root of the specimens, whereas the digital microscope is focused on the side of the notch. Two features, viz., the crack opening displacement (COD) and the crack length, are extracted during the tests in addition to the ultrasonic signal data. These signal data are analyzed using a machine learning framework that is built upon a symbolic time-series algorithm. This framework is interrogated for crack detection in the crack coalescence (CC) regime defined by COD of ~3 μm and detected through the confocal microscope. Additionally, the framework is probed in the crack propagation (CP) regime characterized by a crack length of ~0.2 mm and detected via the digital microscope. For the CC regime, training accuracies of 79.82% and 81.94% are achieved, whereas testing accuracies of 68.18% and 74.12% are observed for the U- and V-notched specimens, respectively. For the CP regime, overall training accuracies of 88.3% and 91.85% are observed, and accordingly, testing accuracies of 81.94% and 85.62% are obtained for the U- and V-notched specimens, respectively. The results show that a combined machine learning and pattern recognition algorithm enables robust and reliable fatigue damage detection in aerospace structural components.

scholarly journals Design of ultrasonic testing device for fatigue damage of pressure vessels

2018 ◽  
Vol 232 ◽  
pp. 04088
Author(s):  
Wenchao LU ◽  
Huibin YANG ◽  
Juan YAN ◽  
Chengbo KANG
Keyword(s):  
Damage Detection ◽  
Fatigue Damage ◽  
Ultrasonic Testing ◽  
Detection System ◽  
Pressure Vessels ◽  
Ultrasonic Nondestructive Testing ◽  
Testing Device ◽  
Test Device ◽  
Detection Technology ◽  
Device Research

The container fatigue damage detection device is researched and designed for the fatigue damage detection requirements of pressure vessels. The hardware design of the detection device, the corresponding detection technology and the functions realized are introduced. The new test performs ultrasonic nondestructive testing by driving five DC servo motors to drive the mechanical part of the test device. Research on the detection system and technology to automate the detection of fatigue damage in pressure vessels.

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