Non-Destructive Testing of Aluminum Alloys by Using Miniature Eddy-Current Flaw Transducers

2014 ◽  
Vol 880 ◽  
pp. 105-108 ◽  
Author(s):  
Viktor V. Polyakov ◽  
Sergey F. Dmitriev ◽  
Alexey V. Ishkov ◽  
Evgeny A. Kolubaev ◽  
Vladimir N. Malikov
Keyword(s):  
Eddy Current ◽  
Flaw Detection ◽  
Material Surface ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Model Composite ◽  
Measuring Device ◽  
Testing Method ◽  
Measurement Results ◽  
Non Destructive

The paper puts forward a modified eddy current testing method based on the use of miniature eddy current transducers within a localized controlled area of the material surface of up to 50 μm2. Measurement results are provided for a model composite material made up of alternating conducting and dielectric layers of system Al-HDPE-Al. Structural flaws are specified as changes in the number and position of layers in the sequence. Prospects of the proposed method and a measuring device operating on its basis for flaw detection in composite aluminum alloy materials are shown.

Reduced False Calls in Eddy Current Images Using Signal Processing

2021 ◽  
Author(s):  
S. B. Mahalakshmi ◽  
Ganesh Seshadri ◽  
Aparna Sheila-Vadde ◽  
Manoj Kumar KM
Keyword(s):  
Eddy Current ◽  
Flaw Detection ◽  
Non Destructive Testing ◽  
Testing Methods ◽  
Destructive Testing ◽  
Noise Measurements ◽  
Component Manufacturing ◽  
Lift Off ◽  
The Time Domain ◽  
Non Destructive

Abstract Non-destructive testing methods are used largely in component manufacturing industries like Aerospace, Renewables and Power to evaluate the properties of a material or the quality of a component by inspecting for cracks and discontinuities without causing damage to the part. Among the many non-destructive testing methods, Eddy current imaging enables efficient flaw detection for surface and sub-surface cracks. However, in typical eddy current inspection, there can be significant number of false calls arising from variation in lift-off and surface anomalies. Discriminating defect signals from false calls can be very challenging. This paper describes a method to reduce false calls by using a wavelet based denoising algorithm and combining it with statistical-based features extracted inside a sliding window in the time domain to efficiently identify the cracks. The results are verified on specimens with cracks of different sizes that are oriented randomly along with locations available for baseline noise measurements.

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