Composite structures are commonly used in complex applications such as automotive and aerospace due to their high strength-to-weight ratio. Although strictly supervised and inspected, they are often subject to dynamic...
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Composite structures are commonly used in complex applications such as automotive and aerospace due to their high strength-to-weight ratio. Although strictly supervised and inspected, they are often subject to dynamic events during their useful life that can cause invisible failures that extend and severely compromise their performance over time. Detecting these defects preventively and repairing them could avoid dramatic accidents. Here, we present a deep learning-based method for the non-destructivedetection of defects in composite samples based on a laser ultrasonic system (LUT). Laser ultrasonic technology is a promising non-destructive testing (NDT) method for detecting inner defects in a non-contact way, as it does not require liquid coupling media. We investigated a composite laminate specimen containing six programmed defects as a test sample. We show that training deep learning-based models as autoencoders makes it possible to extract features that can be used to discern defective areas from non-defective ones in the US C-scan maps. The results demonstrate high detection accuracies (above 90% balanced accuracy and 75% F-1-score), indicating a promising and effective approach to NDT on composite materials.
We propose a reliable and robust defectdetection method from the noisy fringe patterns obtained in optical interferometry. The proposed method relies on a naive Bayes classifier based machine learning model. The mode...
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We propose a reliable and robust defectdetection method from the noisy fringe patterns obtained in optical interferometry. The proposed method relies on a naive Bayes classifier based machine learning model. The model utilizes the phase derivatives computed using fringe signal subspace analysis as feature vectors. This allows for automated defect identification without the requirement of selection of manual threshold parameters. The simulation analysis of various types of defects corroborates the utility of the proposed method. Further, the method is tested on experimental fringes obtained in diffraction phase microscopy to validate its practical applicability.
This paper introduces a technique to identify defects from fringe patterns for optical non-destructive testing and metrology. The technique relies on computation of the windowed Fourier spectrum of the fringe pattern ...
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This paper introduces a technique to identify defects from fringe patterns for optical non-destructive testing and metrology. The technique relies on computation of the windowed Fourier spectrum of the fringe pattern at a given spatial frequency, and subsequent application of automated spectrum thresholding to localize the defect. The technique offers the advantages of high robustness against noise, fast implementation, high throughput and minimal operator intervention. The performance of the proposed technique is demonstrated for identifying defects of different types and sizes under varying levels of noise using numerical simulations, and practical validity is tested using experimental interferograms obtained in diffraction phase microscopy.
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