The Experts below are selected from a list of 186 Experts worldwide ranked by ideXlab platform
Jesus Mirapeix - One of the best experts on this subject based on the ideXlab platform.
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Spectroscopic analysis of the plasma continuum radiation for on-line arc-Welding Defect detection
Journal of Physics D: Applied Physics, 2008Co-Authors: Jesus Mirapeix, Angel Cobo, R. Cardoso, S. Fernandez, Jose Miguel Lopez-higueraAbstract:When plasma optical spectroscopy is applied to on-line Welding quality monitoring, the plasma electronic temperature is commonly selected as the spectroscopic parameter to determine. However, several processing stages have to be considered in this case, including plasma emission line identification, which is significantly costly in terms of computational performance. In this paper, the wavelength associated with the maximum intensity of the plasma background emission is proposed as the new monitoring signal, as it will be demonstrated that there is a clear correlation between this parameter and the Welding quality. The resulting processing scheme is clearly simpler, and experimental and field tests will prove the feasibility of the proposed technique.
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Real-time arc-Welding Defect detection and classification with principal component analysis and artificial neural networks
NDT & E International, 2007Co-Authors: Jesus Mirapeix, P. Beatriz Garcia-allende, Angel Cobo, Olga M Conde, Jose Miguel Lopez-higueraAbstract:A novel system which allows arc-Welding Defect detection and classification is presented in this paper. The spectroscopic analysis of the plasma spectra produced during the Welding process is a well-known technique to monitor the quality of the resulting weld seams. The analysis of specific emission lines and the subsequent estimation of the electronic temperature Te profile offers a direct correlation between this parameter and the corresponding weld seams. However, the automatic identification and classification of weld Defects has proven to be difficult, and it is usually performed by means of statistical studies of the electronic temperature profile. In this paper, a new approach that allows automatic weld Defect detection and classification based in the combined use of principal component analysis (PCA) and an artificial neural network (ANN) is proposed. The plasma spectra captured from the Welding process is processed with PCA, which reduces the processing complexity, by performing a data compression in the spectral dimension. The designed ANN, after the selection of a proper data training set, allows automatic detection of weld Defects. The proposed technique has been successfully checked. Arc-weld tests on stainless steel are reported, showing a good correlation between the ANN outputs and the classical interpretation of the electronic temperature profile.
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real time arc Welding Defect detection technique by means of plasma spectrum optical analysis
Ndt & E International, 2006Co-Authors: Jesus Mirapeix, Angel Cobo, Olga M Conde, Cesar Jauregui, J M LopezhigueraAbstract:An optimized technique for real-time spectral analysis of thermal plasmas, with application in the monitoring and Defect detection of industrial Welding processes, particularly arc-Welding, is presented in this paper. The calculation of the plasma electronic temperature by means of a sub-pixel algorithm permits on-line quality assessment of the welds, allowing the detection of common Defects to be found in the Welding seam, such as oxidation due to insufficient shielding gas flux or lack of penetration caused by current fluctuations of the Welding power source. The proposed technique has been successfully checked in a real-time arc-Welding monitoring system, and experimental results of stainless-steel welds are also reported.
Jose Miguel Lopez-higuera - One of the best experts on this subject based on the ideXlab platform.
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Spectroscopic analysis of the plasma continuum radiation for on-line arc-Welding Defect detection
Journal of Physics D: Applied Physics, 2008Co-Authors: Jesus Mirapeix, Angel Cobo, R. Cardoso, S. Fernandez, Jose Miguel Lopez-higueraAbstract:When plasma optical spectroscopy is applied to on-line Welding quality monitoring, the plasma electronic temperature is commonly selected as the spectroscopic parameter to determine. However, several processing stages have to be considered in this case, including plasma emission line identification, which is significantly costly in terms of computational performance. In this paper, the wavelength associated with the maximum intensity of the plasma background emission is proposed as the new monitoring signal, as it will be demonstrated that there is a clear correlation between this parameter and the Welding quality. The resulting processing scheme is clearly simpler, and experimental and field tests will prove the feasibility of the proposed technique.
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Real-time arc-Welding Defect detection and classification with principal component analysis and artificial neural networks
NDT & E International, 2007Co-Authors: Jesus Mirapeix, P. Beatriz Garcia-allende, Angel Cobo, Olga M Conde, Jose Miguel Lopez-higueraAbstract:A novel system which allows arc-Welding Defect detection and classification is presented in this paper. The spectroscopic analysis of the plasma spectra produced during the Welding process is a well-known technique to monitor the quality of the resulting weld seams. The analysis of specific emission lines and the subsequent estimation of the electronic temperature Te profile offers a direct correlation between this parameter and the corresponding weld seams. However, the automatic identification and classification of weld Defects has proven to be difficult, and it is usually performed by means of statistical studies of the electronic temperature profile. In this paper, a new approach that allows automatic weld Defect detection and classification based in the combined use of principal component analysis (PCA) and an artificial neural network (ANN) is proposed. The plasma spectra captured from the Welding process is processed with PCA, which reduces the processing complexity, by performing a data compression in the spectral dimension. The designed ANN, after the selection of a proper data training set, allows automatic detection of weld Defects. The proposed technique has been successfully checked. Arc-weld tests on stainless steel are reported, showing a good correlation between the ANN outputs and the classical interpretation of the electronic temperature profile.
Angel Cobo - One of the best experts on this subject based on the ideXlab platform.
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Spectroscopic analysis of the plasma continuum radiation for on-line arc-Welding Defect detection
Journal of Physics D: Applied Physics, 2008Co-Authors: Jesus Mirapeix, Angel Cobo, R. Cardoso, S. Fernandez, Jose Miguel Lopez-higueraAbstract:When plasma optical spectroscopy is applied to on-line Welding quality monitoring, the plasma electronic temperature is commonly selected as the spectroscopic parameter to determine. However, several processing stages have to be considered in this case, including plasma emission line identification, which is significantly costly in terms of computational performance. In this paper, the wavelength associated with the maximum intensity of the plasma background emission is proposed as the new monitoring signal, as it will be demonstrated that there is a clear correlation between this parameter and the Welding quality. The resulting processing scheme is clearly simpler, and experimental and field tests will prove the feasibility of the proposed technique.
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Real-time arc-Welding Defect detection and classification with principal component analysis and artificial neural networks
NDT & E International, 2007Co-Authors: Jesus Mirapeix, P. Beatriz Garcia-allende, Angel Cobo, Olga M Conde, Jose Miguel Lopez-higueraAbstract:A novel system which allows arc-Welding Defect detection and classification is presented in this paper. The spectroscopic analysis of the plasma spectra produced during the Welding process is a well-known technique to monitor the quality of the resulting weld seams. The analysis of specific emission lines and the subsequent estimation of the electronic temperature Te profile offers a direct correlation between this parameter and the corresponding weld seams. However, the automatic identification and classification of weld Defects has proven to be difficult, and it is usually performed by means of statistical studies of the electronic temperature profile. In this paper, a new approach that allows automatic weld Defect detection and classification based in the combined use of principal component analysis (PCA) and an artificial neural network (ANN) is proposed. The plasma spectra captured from the Welding process is processed with PCA, which reduces the processing complexity, by performing a data compression in the spectral dimension. The designed ANN, after the selection of a proper data training set, allows automatic detection of weld Defects. The proposed technique has been successfully checked. Arc-weld tests on stainless steel are reported, showing a good correlation between the ANN outputs and the classical interpretation of the electronic temperature profile.
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real time arc Welding Defect detection technique by means of plasma spectrum optical analysis
Ndt & E International, 2006Co-Authors: Jesus Mirapeix, Angel Cobo, Olga M Conde, Cesar Jauregui, J M LopezhigueraAbstract:An optimized technique for real-time spectral analysis of thermal plasmas, with application in the monitoring and Defect detection of industrial Welding processes, particularly arc-Welding, is presented in this paper. The calculation of the plasma electronic temperature by means of a sub-pixel algorithm permits on-line quality assessment of the welds, allowing the detection of common Defects to be found in the Welding seam, such as oxidation due to insufficient shielding gas flux or lack of penetration caused by current fluctuations of the Welding power source. The proposed technique has been successfully checked in a real-time arc-Welding monitoring system, and experimental results of stainless-steel welds are also reported.
Olga M Conde - One of the best experts on this subject based on the ideXlab platform.
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Real-time arc-Welding Defect detection and classification with principal component analysis and artificial neural networks
NDT & E International, 2007Co-Authors: Jesus Mirapeix, P. Beatriz Garcia-allende, Angel Cobo, Olga M Conde, Jose Miguel Lopez-higueraAbstract:A novel system which allows arc-Welding Defect detection and classification is presented in this paper. The spectroscopic analysis of the plasma spectra produced during the Welding process is a well-known technique to monitor the quality of the resulting weld seams. The analysis of specific emission lines and the subsequent estimation of the electronic temperature Te profile offers a direct correlation between this parameter and the corresponding weld seams. However, the automatic identification and classification of weld Defects has proven to be difficult, and it is usually performed by means of statistical studies of the electronic temperature profile. In this paper, a new approach that allows automatic weld Defect detection and classification based in the combined use of principal component analysis (PCA) and an artificial neural network (ANN) is proposed. The plasma spectra captured from the Welding process is processed with PCA, which reduces the processing complexity, by performing a data compression in the spectral dimension. The designed ANN, after the selection of a proper data training set, allows automatic detection of weld Defects. The proposed technique has been successfully checked. Arc-weld tests on stainless steel are reported, showing a good correlation between the ANN outputs and the classical interpretation of the electronic temperature profile.
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real time arc Welding Defect detection technique by means of plasma spectrum optical analysis
Ndt & E International, 2006Co-Authors: Jesus Mirapeix, Angel Cobo, Olga M Conde, Cesar Jauregui, J M LopezhigueraAbstract:An optimized technique for real-time spectral analysis of thermal plasmas, with application in the monitoring and Defect detection of industrial Welding processes, particularly arc-Welding, is presented in this paper. The calculation of the plasma electronic temperature by means of a sub-pixel algorithm permits on-line quality assessment of the welds, allowing the detection of common Defects to be found in the Welding seam, such as oxidation due to insufficient shielding gas flux or lack of penetration caused by current fluctuations of the Welding power source. The proposed technique has been successfully checked in a real-time arc-Welding monitoring system, and experimental results of stainless-steel welds are also reported.
J M Lopezhiguera - One of the best experts on this subject based on the ideXlab platform.
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signal to noise ratio snr comparison for pulsed thermographic data processing methods applied to Welding Defect detection
2010Co-Authors: P Albendea, F J Madruga, Ana Cobo, J M LopezhigueraAbstract:In this paper, the use of a signal to noise ratio (SNR) is proposed for the quantification of the goodness of some selected processing techniques of thermographic images, such as skewness and kurtosis based algorithms, the application of the Fourier transform known as Pulsed Phase Transform, or the Principal Component Analysis. They are applied to the quality analysis of different welds of stainless steel plates containing Defects such as top and back shielding gases ausence, lack of penetration or perforation in variable thickness plates. The resulting images are compared using the SNR parameter value, which should be higher than 0dB if automated detection of Defects is required. The performance of each processing technique to detect different Defects is analysed.
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real time arc Welding Defect detection technique by means of plasma spectrum optical analysis
Ndt & E International, 2006Co-Authors: Jesus Mirapeix, Angel Cobo, Olga M Conde, Cesar Jauregui, J M LopezhigueraAbstract:An optimized technique for real-time spectral analysis of thermal plasmas, with application in the monitoring and Defect detection of industrial Welding processes, particularly arc-Welding, is presented in this paper. The calculation of the plasma electronic temperature by means of a sub-pixel algorithm permits on-line quality assessment of the welds, allowing the detection of common Defects to be found in the Welding seam, such as oxidation due to insufficient shielding gas flux or lack of penetration caused by current fluctuations of the Welding power source. The proposed technique has been successfully checked in a real-time arc-Welding monitoring system, and experimental results of stainless-steel welds are also reported.