A Multi-Resolution Convolutional Neural Network Architecture for Ultrasonic Flaw Detection

Virupakshappa, Kushal; Marino, Michael; Oruklu, Erdal

doi:10.1109/ultsym.2018.8579888

Cited by 25 publications

(6 citation statements)

References 10 publications

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“…Automation of the testing can generate a large amount of data to be processed. Hence machine learning and deep learning methods have been developed to conduct defect detection either on composite [1][2][3] or metallic materials [4][5][6][7][8][9]. One challenge is then to label the data before the learning stage of those methods.…”

Section: State Of the Artmentioning

confidence: 99%

“…A common approach for defect detection using learning approaches consists in the transformation of the 1D signal (A-scan) into features vector with lower dimension, using for instance wavelet transform [3,6], chirplet transform [2] or directly several convolutional layers of a neural network [7,8]. This features vector is then used at the input of a classifier.…”

Section: State Of the Artmentioning

confidence: 99%

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Benefit of Neural Network for the Optimization of Defect Detection on Composite Material Using Ultrasonic Non Destructive Testing

Trouvé-Peloux

Abeloos

Fekih

et al. 2021

2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation

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This paper is dedicated to out-of-plane waviness defect detection within composite materials by ultrasonic testing. We present here an in-house experimental database of ultrasonic data built on composite pieces with/without elaborated defects. Using this dataset, we have developed several defect detection methods using the C-scan representation, where the defect is clearly observable. We compare here the defect detection performance of unsupervised, classical machine learning methods and deep learning approaches. In particular, we have investigated the use of semantic segmentation networks that provides a classification of the data at the “pixel level”, hence at each C-scan measure. This technique is used to classify if a defect is detected, and to produce a precise localization of the defect within the material. The results we obtained with the various detection methods are compared, and we discuss the drawbacks and advantages of each method.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Benefit of Neural Network for the Optimization of Defect Detection on Composite Material Using Ultrasonic Non Destructive Testing

Trouvé-Peloux

Abeloos

Fekih

et al. 2021

2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation

View full text Add to dashboard Cite

show abstract

“…In a GPR task, there are main two convolutional filters as shown in Figure 4. Traditional convolutional filters (illustrated in Figure 4a) are mainly used in the image processing, such as GPR B-scan images [34,37], while another type of filters, named one-dimension convolutional filter (illustrated in Figure 4b), are mainly used in the processing of GPR signals [48,49], which can be regarded as a specific form of traditional convolutional filters. As implied by their name, the dimension of one-dimension convolutional filters is 1.…”

Section: Cnnsmentioning

confidence: 99%

Advances of deep learning applications in ground-penetrating radar: A survey

Zheng

Gao

Yuan

2020

Construction and Building Materials

101

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“…The signal features can be extracted from time domain, frequency domain and time-frequency domain analyses. Marino et al [22,23] chose the low-pass, low-pass (LL) component of discrete wavelet transform (DWT) decomposition as the input feature for proposed UML classification models, and improved their operation by taking power signal of the LL component as input [24]. Cardoso et al [25] established an ultrasonic echo signal parameter estimation algorithm based on continuous wavelet transform (CWT) , which can effectively denoise and compress the original signals.…”

Section: Introductionmentioning

confidence: 99%

Sub-Macroscopic Inclusion Classification in Bearing Steels Based on LFCN and Ultrasonic Testing

Zhang

Wang

Chen

2023

Preprint

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With the improvement of science and technology, the demand for advanced steel with excellent performance has gradually increased. Therefore, the evaluation of steel internal cleanness is an important indicator for the evaluation of material quality. Sub-macroscopic inclusions, which size from 50um to 400um and cannot be detected under the domestic and international bearing steel testing standard, are bound to affect the quality, stability and service life of bearing steel seriously. Hence, the researches of inclusion control technology has gradually attracted attention in the academia and industrial manufacture field. In this paper, we propose an end-to-end LFCN classification model based on LSTM unit and 1DFCN, and verify the effectiveness on the large-scale sub-macroscopic inclusion signal data set collected by ultrasonic experiments. To the best of our knowledge, this study is the first one in this field that has acquire such large amount of experimental sub-macroscopic signal data and solve the classification task by FCN. Especially, our framework can accurately detect the features of sub-macroscopic inclusions, which meets the urgent need of the metallurgical industry. The accuracy rate of proposed model is 88.97%, which is state-of-the-art experimental result among other strong time series classifiers.

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A Multi-Resolution Convolutional Neural Network Architecture for Ultrasonic Flaw Detection

Cited by 25 publications

References 10 publications

Benefit of Neural Network for the Optimization of Defect Detection on Composite Material Using Ultrasonic Non Destructive Testing

Benefit of Neural Network for the Optimization of Defect Detection on Composite Material Using Ultrasonic Non Destructive Testing

Advances of deep learning applications in ground-penetrating radar: A survey

Sub-Macroscopic Inclusion Classification in Bearing Steels Based on LFCN and Ultrasonic Testing

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