scholarly journals Coded Thermal Wave Imaging based Defect Detection in Composites using Neural Networks

2022 ◽  
Vol 35 (1) ◽  
Keyword(s):  
Neural Networks ◽  
Defect Detection ◽  
Thermal Wave ◽  
Wave Imaging ◽  
Thermal Wave Imaging

Material characterisation by enhanced resolution in non-stationary thermal wave imaging

2021 ◽  
Vol 63 (12) ◽  
pp. 721-726
Author(s):  
G T Vesala ◽  
V S Ghali ◽  
S Subhani ◽  
Y Naga Prasanthi
Keyword(s):  
Defect Detection ◽  
Thermal Wave ◽  
Signal To Noise Ratio ◽  
Empirical Relationship ◽  
Depth Resolution ◽  
Steel Sample ◽  
Detection Depth ◽  
Wave Imaging ◽  
Domain Phase ◽  
Thermal Wave Imaging

In the recent past, quadratic frequency-modulated thermal wave imaging (QFMTWI) has been advanced with a chirp z-transform (CZT)-based processing approach to facilitate enhanced subsurface anomaly detection, depth quantification and material property estimation with enhanced depth resolution. In the present study, the applicability of CZT-based phase analysis for foreign object defect detection in a structural steel sample using QFMTWI is validated through finite element-based numerical modelling rather than experimental verification due to limited available resources. Furthermore, the enhanced defect detection capability of the CZT phase approach is qualitatively compared with the frequency- and time-domain phase approaches using the defect signal-to-noise ratio (SNR) as a quality metric. Also, an empirical relationship between the observed phases and the thermal reflection coefficient is obtained, which recommends the CZT phase as a prominent approach for foreign material defect detection.

scholarly journals Numerical Simulation of Non-Destructive Characterization of CFRP Composite Through Frequency Modulated Thermal wave Imaging

2019 ◽  
Vol 8 (4) ◽  
pp. 9754-9757
Keyword(s):  
Numerical Simulation ◽  
Defect Detection ◽  
Thermal Wave ◽  
Pulse Compression ◽  
Principal Component ◽  
Random Projection ◽  
Area Of Interest ◽  
Wave Imaging ◽  
Non Destructive ◽  
Thermal Wave Imaging

Non-destructive testing plays a vital role in industrial and biomedical applications. Non-stationary stimulation based active Infrared thermography is an emerging area of interest in subsurface defect detection and visualization. In present article, frequency modulated thermal wave imaging is employed on a numerical simulation to detect defects of CFRP specimen and applied various post processing techniques such as FFT phase, Pulse compression, principal component analysis and random projection transform for better defect detection. Defect Signal to noise ratios considered as merit of analysis.

Deep Learning in Quadratic Frequency Modulated Thermal Wave Imaging for Automatic Defect Detection

2021 ◽  
pp. 433-442
Author(s):  
G. T. Vesala ◽  
V. S. Ghali ◽  
R. B. Naik ◽  
A. Vijaya Lakshmi ◽  
B. Suresh
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Thermal Wave ◽  
Wave Imaging ◽  
Thermal Wave Imaging
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