An automatic system of classification of weld defects in radiographic images

Vilar, Rafael; Zapata, Juan; Ruíz, Ramón Eduardo

doi:10.1016/j.ndteint.2009.02.004

Cited by 108 publications

(47 citation statements)

References 13 publications

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“…The paper contains comparative results of classifications for Bayesian and bootstrap regularization as well as classic and supplemented MSE criteria of ANN performance. Authors of [25] conclude that the proposed technique is capable of achieving good results. The ANN (composed of 11 input neurons and 20 neurons in hidden layer) optimized by means of supplemented MSE criterion and PCA transformation of input data has occurred to be the best classifier.…”

Section: The Brief Overview Of Methods Used For Weld Defects Classifimentioning

confidence: 86%

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The Application of Rough Sets Theory to Design of Weld Defect Classifiers

et al. 2017

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The innovative method for weld defect classification based on rough set theory is presented in this study. The classification rules have been generated by processing of data base composed of 640 radiographic images referring to certain welding process in aircraft industry. The obtained accuracy of defect identification (from 88% up to 100%, depending on class of defect and choice of classifier) can be evaluated as at least competitive or even better one comparing to results referring to other type of frequently "exploited" classifiers, those mentioned in attached overview section. The identification of weld defects is the final operation which is premised by complicated "chain" of consecutive operations transforming primary radiographs to the form enabling calculation of conditional attributes. That is why brief description of process of transformation of primary radiographs to the forms which are suitable for attributes calculation is included in the paper.

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Section: The Brief Overview Of Methods Used For Weld Defects Classifimentioning

confidence: 86%

“…The problems associated with design of NN classifiers of weld flaws are deeply discussed in [25]. The samples for 140 non-defects, 126 slag inclusions, 87 porosities, 8 transversal and 14 longitudinal cracks were examined.…”

Section: The Brief Overview Of Methods Used For Weld Defects Classifimentioning

confidence: 99%

The Application of Rough Sets Theory to Design of Weld Defect Classifiers

et al. 2017

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“…Many researches were done on the image processing, segmentation and classification of weld defects [1][2][3][4][5][6][7]. Heesang Park et al [1] investigated the detection of SCC micro-cracks in the dissimilar metal weld used for nuclear power plant pipes through ultrasound infrared thermography.…”

Section: Introductionmentioning

confidence: 99%

“…And the detection results were compared with penetration testing, they found out that it is possible to detect micro-cracks with ultrasonic vibration, but a further research must be done to determine the kinds, depth and length of cracks and the width of defects. Rafael Vilar et al [2] provided an automatic detection system to recognize welding defects in radiographic images, and the defects detection were divided into three stages, they thought that the proposed technique is capable of achieving good results when the best implementation was adopted, but they need to use a regularization method to enhance the performance of the ANN. Marcelo Kleber Felisberto et al [3] developed a new methodology to perform a weld quality interpretation system, which can achieve the weld bead extraction from a digital radiograph, and genetic algorithm was used to choose suitable parameters values.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Method to Remove Air-ring Artifacts on Welding Tube Slice Images Based on Micro-focus CT Testing

Shi¹,

Tie²,

Yang³

et al. 2017

dtmse

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Abstract. In this paper, a nondestructive testing method for tiny defects in precision welding tubes based on micro-focus CT device is presented, and slice images are obtained. Edge detection and morphology operations were adopted to remove air-ring artifacts in slice images. The removal methods are as follows. First, the canny orthogonal gradient operator was used to obtain the integrated edge of the welded tube. Second, the adaptive morphology method was used to fill the inner area of the welded tube, and mask images were obtained. Finally, the mask images were multiplied with the origin images, and slice images, from which air-ring artifacts were removed, could be obtained. The presented method was feasible. This method is expected to provide a better basis for the segmentation and 3D reconfiguration of tiny defects in slice images of a precision welding tube.

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“…The automatic analysis field approach is in general to segment out any possible defect [6] and then characterize it [7][8][9], for example, by its type (lack of fusion or crack etc). A merge between the segmentation part of the general automatic analysis and the 3-D point reconstruction has been shown to result in a high probability of detecting true defects and a low probability of detecting false defects, especially for low CNR defects [10].…”

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confidence: 99%

Detection, 3-D positioning, and sizing of small pore defects using digital radiography and tracking

Lindgren

2014

EURASIP J. Adv. Signal Process.

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This article presents an algorithm that handles the detection, positioning, and sizing of submillimeter-sized pores in welds using radiographic inspection and tracking. The possibility to detect, position, and size pores which have a low contrast-to-noise ratio increases the value of the nondestructive evaluation of welds by facilitating fatigue life predictions with lower uncertainty. In this article, a multiple hypothesis tracker with an extended Kalman filter is used to track an unknown number of pore indications in a sequence of radiographs as an object is rotated. Each pore is not required to be detected in all radiographs. In addition, in the tracking step, three-dimensional (3-D) positions of pore defects are calculated. To optimize, set up, and pre-evaluate the algorithm, the article explores a design of experimental approach in combination with synthetic radiographs of titanium laser welds containing pore defects. The pre-evaluation on synthetic radiographs at industrially reasonable contrast-to-noise ratios indicate less than 1% false detection rates at high detection rates and less than 0.1 mm of positioning errors for more than 90% of the pores. A comparison between experimental results of the presented algorithm and a computerized tomography reference measurement shows qualitatively good agreement in the 3-D positions of approximately 0.1-mm diameter pores in 5-mm-thick Ti-6242.

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An automatic system of classification of weld defects in radiographic images

Cited by 108 publications

References 13 publications

The Application of Rough Sets Theory to Design of Weld Defect Classifiers

The Application of Rough Sets Theory to Design of Weld Defect Classifiers

An Adaptive Method to Remove Air-ring Artifacts on Welding Tube Slice Images Based on Micro-focus CT Testing

Detection, 3-D positioning, and sizing of small pore defects using digital radiography and tracking

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