Automatic Defect Detection and Depth Visualization in Mild Steel Sample Using Quadratic Frequency Modulated Thermal Wave Imaging
| Autor: | V. Gopi Tilak, B. Suresh, A. Vijaya Lakshmi, G. V. Subbarao |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 1804:012173 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1804/1/012173 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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