Defect shape detection and defect reconstruction in active thermography by means of two-dimensional convolutional neural network as well as spatiotemporal convolutional LSTM network
| Autor: | Bernd Valeske, David Müller, Udo Netzelmann |
|---|---|
| Přispěvatelé: | Publica |
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
defect detection business.industry Computer science Defect reconstruction Pattern recognition 02 engineering and technology neural networks 01 natural sciences Convolutional neural network active thermography Task (project management) 010309 optics Data sequences 0103 physical sciences Thermography defect profile reconstruction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation induction thermography Artificial intelligence Electrical and Electronic Engineering business Instrumentation |
| Zdroj: | Quantitative InfraRed Thermography Journal. 19:126-144 |
| ISSN: | 2116-7176 1768-6733 |
| DOI: | 10.1080/17686733.2020.1810883 |
| Popis: | A neural network (NN) for semantic segmentation (U-Net) was used for the detection of crack-type defects from thermography sequences. For this task, data sequences of forged steel parts were acquired through induction thermography and the corresponding phase images calculated. The results for defect detection were quantitatively evaluated using Intersection over Union (IoU) metric. Further, a combination of 2D convolutional layer as well as LSTM (Long-Short-Term-Memory) is shown, which includes three-dimensional aspects in the form of time dependent and spatial changes and allows a defect shape reconstruction of back wall drillings. Therefore, pulsed thermography sequences were simulated with COMSOL Multiphysics. Finally, the reconstruction results were compared with the ground-truth defect profile using Mean Squared Error (MSE). The approaches provide improvements over conventional methods in non-destructive testing using infrared thermography. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|