Open AccessAutomatic Defect Detection and Depth Visualization in Mild Steel Sample Using Quadratic Frequency Modulated Thermal Wave Imaging
Author(s) -
V Gopi Tilak,
V. S. Ghali,
A. Vijaya Lakshmi,
B. Suresh
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1804/1/012173
Subject(s) - quadratic equation , visualization , artificial neural network , thermography , artificial intelligence , nondestructive testing , support vector machine , pattern recognition (psychology) , quadratic function , computer science , infrared , optics , mathematics , physics , geometry , quantum mechanics
Deeper defect detection and depth resolution capabilities of quadratic frequency-modulated optical stimulus became a viable approach for material inspection in active infrared non-destructive testing modality. But the limitations of complex and non-linear analytical models associated with processing techniques propel towards automated defect assessment techniques in infrared thermography. This paper introduces a deep neural network-based automatic defect detection and depth visualization technique in quadratic frequency modulated thermal wave imaging. The neural network classifier uses the modified loss function of a one-class support vector machine to classify defects. The regression network estimates the depth of classified defects. A mild steel specimen with artificial delaminations is numerically modeled and excited by a quadratic frequency-modulated heat flux. The proposed network classification and regression performances are qualitatively assessed using testing time, accuracy, and mean squared error as a figure of merits.