Automatic Defect Detection and Depth Visualization in Mild Steel Sample Using Quadratic Frequency Modulated Thermal Wave Imaging

• Published in: Journal of physics. Conference series Vol. 1804; no. 1; p. 12173
• Main Authors: Gopi Tilak, V., Subbarao, G. V., Vijaya Lakshmi, A., Suresh, B.
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.02.2021
• Subjects: Artificial neural networks; Defects; Heat flux; Infrared analysis; Infrared imaging; Infrared inspection; Inspection; Low carbon steels; Mathematical models; Model testing; Neural networks; Nondestructive testing; Physics; Support vector machines; Testing time; Thermal imaging; Thermal wave imaging; Thermography; Visualization
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1804/1/012173

Abstract 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.