The use of artificial neural network in the classification of pulse-echo and TOFD ultra-sonic signals

Veiga, J. L. B. C.; Carvalho, A. A.; Silva, I. C. da; Rebello, J. M. A.

doi:10.1590/s1678-58782005000400007

Cited by 26 publications

(17 citation statements)

References 5 publications

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“…The ANN model was simulated in MATLAB ® software, version 6.3 (Matrix Laboratory) for Windows, which is a computational environment used for visualization and highperformance numerical computation. MATLAB integrates numerical analysis, signal processing, and graphs, and the problems and solutions are expressed as mathematically formulated, with no traditional programming (Veiga et al, 2005).…”

Section: Hatchability Was Determined According To Equationmentioning

confidence: 99%

Egg hatchability prediction by multiple linear regression and artificial neural networks

Bolzan

Machado

Piaia

2008

Rev. Bras. Cienc. Avic.

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An artificial neural network (ANN) was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained from industrial incubations. The ANN model was chosen as it produced data that fit better the experimental data as compared to the multiple linear regression model, which used coefficients determined by minimum square method. The proposed simulation results of these approaches indicate that this ANN can be used for incubation performance prediction.

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Section: Hatchability Was Determined According To Equationmentioning

confidence: 99%

Egg hatchability prediction by multiple linear regression and artificial neural networks

Bolzan

Machado

Piaia

2008

Rev. Bras. Cienc. Avic.

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“…The ultrasonic techniques that have been used are: (a) pulse-echo, (b) transmission, (c) resonance, and (d) a more sophisticated ultrasonic holography method. Among ultrasonic techniques, the pulse-echo method is the most commonly used one in industry, mainly due to its simplicity, small size of the equipment, and efficiency [4]. In this method, a single ultrasonic transducer sends a pulse and then collects the echo.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Neural Network Based Pulse-Echo Weld Defect Classifiers

Seyedtabaii¹

2012

Measurement Science Review

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Pulse-echo ultrasonic signal is used to detect weld defects with high probability. However, utilizing echo signal for defects classification is another issue that has attracted attention of many researchers who have devised algorithms and tested them against their own databases. In this paper, a study is conducted to score the performance of various algorithms against a single echo signal database. Algorithms tested the use of Wavelet Transform (WT), Fast Fourier Transform (FFT) and time domain echo signal features and employed several NN's architectures such as Multi-Layer Perceptron Neural Network (MLP), Self Organizing Map (SOM) and others known to be good classifiers. The average performance of all can be viewed fair (90%) while some algorithms render success rate of about 94%. It seems that acquiring higher success rates out of a single fixed angle probe pulseecho set up needs new arrangements of data collection, which is under investigation.

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“…E P de Moura, et al, [5] used a hierarchical and non hierarchical linear discriminator for classifying the weld defects. J. L. B. C. Veigav et al, [6] has implemented a feed forward back propagation network with one hidden layer. Feed forward Multi layer perceptron ANNs with varying topologies were simulated in software and trained by Shaun W. Lawson et al [7].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network based Defect Detection of Welds in TOFD Technique

Lalithakumari¹,

Sheelarani²,

Venkatraman³

2012

IJCA

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Time of Flight Diffraction Technique is one of the NDE methods, used in weld inspection to identify the weld defects. The classification of defects using the TOFD technique depends on the knowledge and experience of the operator. The classification reliability of defects detected by this technique can be improved by applying the Artificial Neural Network. In this work, four austenitic stainless steel weldments with defects viz, Lack of Fusion, Lack of Penetration, Slag, Porosity and one with out any Defect were fabricated. TOFD experiment is conducted on these weldments. Discrete wavelet transform based denoising methods were applied to denoise the resultant A scan signals. Time scale features are extracted from the denoised signals. A multi layer feed forward network with Resilient Back Propagation algorithm has been applied for classification of the signals. The number of hidden layers in the network are increased from 0 to 6. Various performance functions are also employed to achieve a better classification efficiency. The results are promising to proceed the automatic defect classification by TOFD technique.

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The use of artificial neural network in the classification of pulse-echo and TOFD ultra-sonic signals

Cited by 26 publications

References 5 publications

Egg hatchability prediction by multiple linear regression and artificial neural networks

Egg hatchability prediction by multiple linear regression and artificial neural networks

Performance Evaluation of Neural Network Based Pulse-Echo Weld Defect Classifiers

Artificial Neural Network based Defect Detection of Welds in TOFD Technique

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