ISSN 2394-5125
 

Research Article 


ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao.

Abstract
Infrared thermography is a viable approach for non-destructive testing and evaluation in various industrial applications. Quadratic frequency modulated thermal wave imaging is gaining much interest due to its depth resolvable capabilities. On the other hand, machine learning and artificial intelligence has been introduced into conventional thermography approaches for subsurface anomaly analysis. The present study implements an artificial neural network for defect classification in quadratic frequency modulated thermal wave imaging modality. A mild steel specimen is numerically simulated to prepare data and the proposed network classification performance is compared with conventional feature based approaches. From the classification result and corresponding signal to noise ratios, it is concluded that proposed ANN model provide enhanced defect detection.

Key words: Infrared thermography, non-destructive testing, Quadratic frequency modulated thermal wave imaging, defect classification, artificial neural network.


 
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How to Cite this Article
Pubmed Style

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao. ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI. JCR. 2020; 7(9): 718-720. doi:10.31838/jcr.07.09.138


Web Style

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao. ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI. http://www.jcreview.com/?mno=112289 [Access: April 18, 2021]. doi:10.31838/jcr.07.09.138


AMA (American Medical Association) Style

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao. ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI. JCR. 2020; 7(9): 718-720. doi:10.31838/jcr.07.09.138



Vancouver/ICMJE Style

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao. ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI. JCR. (2020), [cited April 18, 2021]; 7(9): 718-720. doi:10.31838/jcr.07.09.138



Harvard Style

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao (2020) ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI. JCR, 7 (9), 718-720. doi:10.31838/jcr.07.09.138



Turabian Style

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao. 2020. ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI. Journal of Critical Reviews, 7 (9), 718-720. doi:10.31838/jcr.07.09.138



Chicago Style

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao. "ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI." Journal of Critical Reviews 7 (2020), 718-720. doi:10.31838/jcr.07.09.138



MLA (The Modern Language Association) Style

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao. "ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI." Journal of Critical Reviews 7.9 (2020), 718-720. Print. doi:10.31838/jcr.07.09.138



APA (American Psychological Association) Style

V. V. N. S. G. Satwik, D. Sai Prahlad, V. Gopi Tilak, B. Suresh, G. V. Subbarao (2020) ARTIFICIAL NEURAL NETWORK FOR SUBSURFACE ANOMALY DETECTION IN QFMTWI. Journal of Critical Reviews, 7 (9), 718-720. doi:10.31838/jcr.07.09.138