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Ultrasonic testing

About: Ultrasonic testing is a(n) research topic. Over the lifetime, 10548 publication(s) have been published within this topic receiving 83990 citation(s). The topic is also known as: UT.

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Patent
01 Aug 1986-
Abstract: An ultrasonic apparatus for testing a material comprises an oscillator (10) which generates a selected frequency in the ultrasonic range. A transducer (1) is connected to the oscillator (10) for applying an ultrasonic signal to the material and for receiving an echo signal back from the material. A phase detector (5) receives the echo signal and an in-phase oscillator signal to generate a first display signal, and a phase detector (6) receives a quadrature signal (90° out of phase from the oscillator signal) and the echo signal to generate a second display signal. The first and second display signals are utilised in a visual display, such as a cathode ray tube (8), to generate an image. The image changes according to the phase shift between the ultrasonic signal transmitted into the material and the echo signal, which, in turn, can be utilised to determine the presence and depth of a flaw or boundary in the material.

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1,017 citations


Patent
10 Feb 2011-
Abstract: An ultrasonic treatment device (21) comprises an ultrasonic vibrator (28) which generates ultrasonic vibration, a vibration transmission section (29) which transmits the ultrasonic vibration generated by the ultrasonic vibrator (28), a treatment section (32) which is formed at the front end of the vibration transmission section (29) and transmits to living organism's tissue the ultrasonic vibration transmitted from the vibration transmission section (29), a grip member (33) which is provided so that the grip member (33) can be opened and closed relative to the treatment section (32), and a pad member (49) which is provided to the grip member (33) at a position facing the treatment section (32) and which makes contact with the treatment section (32) when the grip member (33) is in a closed state. The front end of the pad member (49) has a protrusion section (60) which, on the side thereof which faces the treatment section (32), protrudes toward the treatment section (32).

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510 citations


Journal ArticleDOI
Abstract: A piezoelectric based built-in diagnostic technique has been developed for monitoring fatigue crack growth in metallic structures. The technique uses diagnostic signals, generated from nearby piezoelectric actuators built into the structures, to detect crack growth. It consists of three major components: diagnostic signal generation, signal processing and damage interpretation. In diagnostic signal generation, appropriate ultrasonic guided Lamb waves were selected for actuators to maximize receiving sensor measurements. In signal processing, methods were developed to select an individual mode for damage detection and maximize signal to noise ratio in recorded sensor signals. Finally, in damage interpretation, a physics based damage index was developed relating sensor measurements to crack growth size. Fatigue tests were performed on laboratory coupons with a notch to verify the proposed technique. The damage index measured from built-in piezoceramics on the coupons showed a good correlation with the actual fatigue crack growth obtained from visual inspection. Furthermore, parametric studies were also performed to characterize the sensitivity of sensor/actuator location for the proposed technique.

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455 citations


Book
01 Jan 2017-
Abstract: List of Contributors. Preface. 1. Introduction (G. Bartelds, J.H. Heida, J. McFeat and C. Boller). 1.1 Health and Usage Monitoring in Aircraft Structures -- Why and How? 1.2 Smart Solution in Aircraft Monitoring. 1.3 End--User Requirements. 1.3.1 Damage Detection. 1.3.2 Load History Monitoring. 1.4 Assessment of Monitoring Technologies. 1.5 Background of Technology Qualification Process. 1.6 Technology Qualification. 1.6.1 Philosophy. 1.6.2 Performance and Operating Requirements. 1.6.3 Qualification Evidence -- Requirements and Provision. 1.6.4 Risks. 1.7 Flight Vehicle Certification. 1.8 Summary. References. 2. Aircraft Structural Health and Usage Monitoring (C. Boller and W.J. Staszewski). 2.1 Introduction. 2.2 Aircraft Structural Damage. 2.3 Ageing Aircraft Problem. 2.4 LifeCycle Cost of Aerospace Structures. 2.4.1 Background. 2.4.2 Example. 2.5 Aircraft Structural Design. 2.5.1 Background. 2.5.2 Aircraft Design Process. 2.6 Damage Monitoring Systems in Aircraft. 2.6.1 Loads Monitoring. 2.6.2 Fatigue Monitoring. 2.6.3 Load Models. 2.6.4 Disadvantages of Current Loads Monitoring Systems. 2.6.5 Damage Monitoring and Inspections. 2.7 Non--Destructive Testing. 2.7.1 Visual Inspection. 2.7.2 Ultrasonic Inspection. 2.7.3 Eddy Current. 2.7.4 Acoustic Emission. 2.7.5 Radiography, Thermography and Shearography. 2.7.6 Summary. 2.8 Structural Health Monitoring. 2.8.1 Vibration and Modal Analysis. 2.8.2 Impact Damage Detection. 2.9 Emerging Monitoring Techniques and Sensor Technologies. 2.9.1 Smart Structures and Materials. 2.9.2 Damage Detection Techniques. 2.9.3 Sensor Technologies. 2.9.4 Intelligent Signal Processing. 2.10 Conclusions. References. 3. Operational Load Monitoring Using Optical Fibre Sensors (P. Foote, M. Breidne, K. Levin, P. Papadopolous, I. Read, M. Signorazzi, L.K. Nilsson, R. Stubbe and A. Claesson). 3.1 Introduction. 3.2 Fibre Optics. 3.2.1 Optical Fibres. 3.2.2 Optical Fibre Sensors. 3.2.3 Fibre Bragg Grating Sensors. 3.3 Sensor Target Specifications. 3.4 Reliability of Fibre Bragg Grating Sensors. 3.4.1 Fibre Strength Degradation. 3.4.2 Grating Decay. 3.4.3 Summary. 3.5 Fibre Coating Technology. 3.5.1 Polyimide Chemistry and Processing. 3.5.2 Polyimide Adhesion to Silica. 3.5.3 Silane Adhesion Promoters. 3.5.4 Experimental Example. 3.5.5 Summary. 3.6 Example of Surface Mounted Operational Load Monitoring Sensor System. 3.6.1 Sensors. 3.6.2 Optical Signal Processor. 3.6.3 Optical Interconnections. 3.7 Optical Fibre Strain Rosette. 3.8 Example of Embedded Optical Impact Detection System. 3.9 Summary. References. 4. Damage Detection Using Stress and Ultrasonic Waves (W.J. Staszewski, C. Boller, S. Grondel, C. Biemans, E. O'Brien, C. Delebarre and G.R. Tomlinson). 4.1 Introduction. 4.2 Acoustic Emission. 4.2.1 Background. 4.2.2 Transducers. 4.2.3 Signal Processing. 4.2.4 Testing and Calibration. 4.3 Ultrasonics. 4.3.1 Background. 4.3.2 Inspection Modes. 4.3.3 Transducers. 4.3.4 Display Modes. 4.4 Acousto--Ultrasonics. 4.5 Guided Wave Ultrasonics. 4.5.1 Background. 4.5.2 Guided Waves. 4.5.3 Lamb Waves. 4.5.4 Monitoring Strategy. 4.6 Piezoelectric Transducers. 4.6.1 Piezoelectricity and Piezoelectric Materials. 4.6.2 Constitutive Equations. 4.6.3 Properties. 4.7 Passive Damage Detection Examples. 4.7.1 Crack Monitoring Using Acoustic Emission. 4.7.2 Impact Damage Detection in Composite Materials. 4.8 Active Damage Detection Examples. 4.8.1 Crack Monitoring in Metallic Structures Using Broadband Acousto--Ultrasonics. 4.8.2 Impact Damage Detection in Composite Structures Using Lamb Waves. 4.9 Summary. References. 5. Signal Processing for Damage Detection (W.J. Staszewski and K. Worden). 5.1 Introduction. 5.2 Data Pre--Processing. 5.2.1 Signal Smoothing. 5.2.2 Signal Smoothing Filters. 5.3 Signal Features for Damage Identification. 5.3.1 Feature Extraction. 5.3.2 Feature Selection. 5.4 Time--Domain Analysis. 5.5 Spectral Analysis. 5.6 Instantaneous Phase and Frequency. 5.7 Time--Frequency Analysis. 5.8 Wavelet Analysis. 5.8.1 Continuous Wavelet Transform. 5.8.2 Discrete Wavelet Transform. 5.9 Dimensionality Reduction Using Linear and Nonlinear Transformation. 5.9.1 Principal Component Analysis. 5.9.2 Sammon Mapping. 5.10 Data Compression Using Wavelets. 5.11 Wavelet--Based Denoising. 5.12 Pattern Recognition for Damage Identification. 5.13 Artificial Neural Networks. 5.13.1 Parallel Processing Paradigm. 5.13.2 The Artificial Neuron. 5.13.3 Multi--Layer Networks. 5.13.4 Multi--Layer Perceptron Neural Networks and Others. 5.13.5 Applications. 5.14 Impact Detection in Structures Using Pattern Recognition. 5.14.1 Detection of Impact Positions. 5.14.2 Detection of Impact Energy. 5.15 Data Fusion. 5.16 Optimised Sensor Distributions. 5.16.1 Informativeness of Sensors. 5.16.2 Optimal Sensor Location. 5.17 Sensor Validation. 5.18 Conclusions. References. 6. Structural Health Monitoring Evaluation Tests (P.A. Lloyd, R. Pressland, J. McFeat, I. Read, P. Foote, J.P. Dupuis, E. O'Brien, L. Reithler, S. Grondel, C. Delebarre, K. Levin, C. Boller, C. Biemans and W.J. Staszewski). 6.1 Introduction. 6.2 Large--Scale Metallic Evaluator. 6.2.1 Lamb Wave Results from Riveted Metallic Specimens. 6.2.2 Acoustic Emission Results from a Full--Scale Fatigue Test. 6.3 Large--Scale Composite Evaluator. 6.3.1 Test Article. 6.3.2 Sensor and Specimen Integration. 6.3.3 Impact Tests. 6.3.4 Damage Detection Results -- Distributed Optical Fibre Sensors. 6.3.5 Damage Detection Results -- Bragg Grating Sensors. 6.3.6 Lamb Wave Damage Detection System. 6.4 Flight Tests. 6.4.1 Flying Test--Bed. 6.4.2 Acoustic Emission Optical Damage Detection System. 6.4.3 Bragg Grating Optical Load Measurement System. 6.4.4 Fibre Optic Load Measurement Rosette System. 6.5 Summary. References. Index.

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442 citations


Patent
25 Apr 2005-
Abstract: A three-dimensional ultrasonic inspection apparatus includes: a sensing device for ultrasonic inspection including an ultrasonic sensor as a transducer, in which a plurality of piezoelectric vibrators are disposed in a matrix or an array; a drive element selecting unit for selecting and driving a piezoelectric vibrator for producing an ultrasonic wave among the ultrasonic transducer; a signal detecting circuit for detecting the electric signal of the reflected echo by receiving the reflected echo from the joined area; a signal processing unit for subjecting the electric signal to signal processing and generating three-dimensional imaging data by causing the electric signals to correspond to mesh elements in a three-dimensional imaging region of the object to be inspected; and a display processing device for displaying the detection results and three-dimensional image data from the signal processing unit, while detecting the size and the position of the molten-solidified portion, and the size and the position of the weld defect of the joined area, from the intensity distribution of the three-dimensional imaging data generated by the signal processing unit.

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360 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202214
2021225
2020243
2019339
2018341
2017384

Top Attributes

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Topic's top 5 most impactful authors

Krishnan Balasubramaniam

30 papers, 315 citations

Steve Dixon

23 papers, 423 citations

Joseph L. Rose

23 papers, 254 citations

Frank J. Margetan

19 papers, 145 citations

Lester W. Schmerr

17 papers, 118 citations