An Autonomous Technique for Multi Class Weld Imperfections Detection and Classification by Support Vector Machine
Abstract In modern engineering, weld joint excellence achieved through competent and on-line welding imperfections monitoring techniques. Online weld joint examination by non-destructive testing significantly demanded in vehicle, railways, aerospace, petrochemical, oil, gas, land transportation, shi...
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Gespeichert in:
| Autor*in: |
Patil, Rajesh V. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of nondestructive evaluation - Springer US, 1980, 40(2021), 3 vom: 23. Aug. |
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| Übergeordnetes Werk: |
volume:40 ; year:2021 ; number:3 ; day:23 ; month:08 |
| Links: |
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| DOI / URN: |
10.1007/s10921-021-00801-w |
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OLC207705526X |
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| 520 | |a Abstract In modern engineering, weld joint excellence achieved through competent and on-line welding imperfections monitoring techniques. Online weld joint examination by non-destructive testing significantly demanded in vehicle, railways, aerospace, petrochemical, oil, gas, land transportation, shipbuilding, and marine industries. These industries demanded weld imperfection inspection as major part of their testing as physical inspection may confuse for appropriate justifications and lead to incorrect identifications. Hence, for error free examination automatic weld, this work proposed an autonomous technique for multi class weld imperfections namely crack, undercut, gas pores, porosity, slag, warm holes, lack of penetration and non defects are detected and classified in X- ray images by employing support vector machine and artificial neural network and confirm their high-performance accuracy. The proposed methods incorporated mainly four modules. In the first module weld image preprocess by median filtering to achieve protective edge of weld image by suppressing noises and by rectangular shaped histogram for dispersal grey levels to wider range with a brightness gradient achieved. Furthermore, second module propose canny edge operator to achieved complete and more continues edge of weld defect as compared to other edge operators. In the third module, ten texture feature extracted by grey level co-occurrence matrix. Moreover, cluster of signifiers identical to texture features measurements extract separated entity and stated as an input classifier. Finally for speedy advances of weld imperfection detection mechanization, support vector machine and artificial neural network detected and classified weld imperfections and confirmed their accuracy performance of 98.75 and 97.5% by confusion matrix. The autonomous technique for detection and classification of X-ray images reveals ideal calculation time without disturbing complete accuracy of features selection and presented comprehensive innovative technique with improved results. | ||
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10.1007/s10921-021-00801-w doi (DE-627)OLC207705526X (DE-He213)s10921-021-00801-w-p DE-627 ger DE-627 rakwb eng 510 VZ Patil, Rajesh V. verfasserin (orcid)0000-0001-6869-7180 aut An Autonomous Technique for Multi Class Weld Imperfections Detection and Classification by Support Vector Machine 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In modern engineering, weld joint excellence achieved through competent and on-line welding imperfections monitoring techniques. Online weld joint examination by non-destructive testing significantly demanded in vehicle, railways, aerospace, petrochemical, oil, gas, land transportation, shipbuilding, and marine industries. These industries demanded weld imperfection inspection as major part of their testing as physical inspection may confuse for appropriate justifications and lead to incorrect identifications. Hence, for error free examination automatic weld, this work proposed an autonomous technique for multi class weld imperfections namely crack, undercut, gas pores, porosity, slag, warm holes, lack of penetration and non defects are detected and classified in X- ray images by employing support vector machine and artificial neural network and confirm their high-performance accuracy. The proposed methods incorporated mainly four modules. In the first module weld image preprocess by median filtering to achieve protective edge of weld image by suppressing noises and by rectangular shaped histogram for dispersal grey levels to wider range with a brightness gradient achieved. Furthermore, second module propose canny edge operator to achieved complete and more continues edge of weld defect as compared to other edge operators. In the third module, ten texture feature extracted by grey level co-occurrence matrix. Moreover, cluster of signifiers identical to texture features measurements extract separated entity and stated as an input classifier. Finally for speedy advances of weld imperfection detection mechanization, support vector machine and artificial neural network detected and classified weld imperfections and confirmed their accuracy performance of 98.75 and 97.5% by confusion matrix. The autonomous technique for detection and classification of X-ray images reveals ideal calculation time without disturbing complete accuracy of features selection and presented comprehensive innovative technique with improved results. Support vector machine Artificial neural network Grey level cooccurrence matrix Texture features Weld imperfection Reddy, Y. P. aut Enthalten in Journal of nondestructive evaluation Springer US, 1980 40(2021), 3 vom: 23. Aug. (DE-627)130515841 (DE-600)766165-4 (DE-576)016089006 0195-9298 nnns volume:40 year:2021 number:3 day:23 month:08 https://doi.org/10.1007/s10921-021-00801-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 40 2021 3 23 08 |
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10.1007/s10921-021-00801-w doi (DE-627)OLC207705526X (DE-He213)s10921-021-00801-w-p DE-627 ger DE-627 rakwb eng 510 VZ Patil, Rajesh V. verfasserin (orcid)0000-0001-6869-7180 aut An Autonomous Technique for Multi Class Weld Imperfections Detection and Classification by Support Vector Machine 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In modern engineering, weld joint excellence achieved through competent and on-line welding imperfections monitoring techniques. Online weld joint examination by non-destructive testing significantly demanded in vehicle, railways, aerospace, petrochemical, oil, gas, land transportation, shipbuilding, and marine industries. These industries demanded weld imperfection inspection as major part of their testing as physical inspection may confuse for appropriate justifications and lead to incorrect identifications. Hence, for error free examination automatic weld, this work proposed an autonomous technique for multi class weld imperfections namely crack, undercut, gas pores, porosity, slag, warm holes, lack of penetration and non defects are detected and classified in X- ray images by employing support vector machine and artificial neural network and confirm their high-performance accuracy. The proposed methods incorporated mainly four modules. In the first module weld image preprocess by median filtering to achieve protective edge of weld image by suppressing noises and by rectangular shaped histogram for dispersal grey levels to wider range with a brightness gradient achieved. Furthermore, second module propose canny edge operator to achieved complete and more continues edge of weld defect as compared to other edge operators. In the third module, ten texture feature extracted by grey level co-occurrence matrix. Moreover, cluster of signifiers identical to texture features measurements extract separated entity and stated as an input classifier. Finally for speedy advances of weld imperfection detection mechanization, support vector machine and artificial neural network detected and classified weld imperfections and confirmed their accuracy performance of 98.75 and 97.5% by confusion matrix. The autonomous technique for detection and classification of X-ray images reveals ideal calculation time without disturbing complete accuracy of features selection and presented comprehensive innovative technique with improved results. Support vector machine Artificial neural network Grey level cooccurrence matrix Texture features Weld imperfection Reddy, Y. P. aut Enthalten in Journal of nondestructive evaluation Springer US, 1980 40(2021), 3 vom: 23. Aug. (DE-627)130515841 (DE-600)766165-4 (DE-576)016089006 0195-9298 nnns volume:40 year:2021 number:3 day:23 month:08 https://doi.org/10.1007/s10921-021-00801-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 40 2021 3 23 08 |
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Patil, Rajesh V. Reddy, Y. P. |
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Patil, Rajesh V. |
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an autonomous technique for multi class weld imperfections detection and classification by support vector machine |
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An Autonomous Technique for Multi Class Weld Imperfections Detection and Classification by Support Vector Machine |
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Abstract In modern engineering, weld joint excellence achieved through competent and on-line welding imperfections monitoring techniques. Online weld joint examination by non-destructive testing significantly demanded in vehicle, railways, aerospace, petrochemical, oil, gas, land transportation, shipbuilding, and marine industries. These industries demanded weld imperfection inspection as major part of their testing as physical inspection may confuse for appropriate justifications and lead to incorrect identifications. Hence, for error free examination automatic weld, this work proposed an autonomous technique for multi class weld imperfections namely crack, undercut, gas pores, porosity, slag, warm holes, lack of penetration and non defects are detected and classified in X- ray images by employing support vector machine and artificial neural network and confirm their high-performance accuracy. The proposed methods incorporated mainly four modules. In the first module weld image preprocess by median filtering to achieve protective edge of weld image by suppressing noises and by rectangular shaped histogram for dispersal grey levels to wider range with a brightness gradient achieved. Furthermore, second module propose canny edge operator to achieved complete and more continues edge of weld defect as compared to other edge operators. In the third module, ten texture feature extracted by grey level co-occurrence matrix. Moreover, cluster of signifiers identical to texture features measurements extract separated entity and stated as an input classifier. Finally for speedy advances of weld imperfection detection mechanization, support vector machine and artificial neural network detected and classified weld imperfections and confirmed their accuracy performance of 98.75 and 97.5% by confusion matrix. The autonomous technique for detection and classification of X-ray images reveals ideal calculation time without disturbing complete accuracy of features selection and presented comprehensive innovative technique with improved results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract In modern engineering, weld joint excellence achieved through competent and on-line welding imperfections monitoring techniques. Online weld joint examination by non-destructive testing significantly demanded in vehicle, railways, aerospace, petrochemical, oil, gas, land transportation, shipbuilding, and marine industries. These industries demanded weld imperfection inspection as major part of their testing as physical inspection may confuse for appropriate justifications and lead to incorrect identifications. Hence, for error free examination automatic weld, this work proposed an autonomous technique for multi class weld imperfections namely crack, undercut, gas pores, porosity, slag, warm holes, lack of penetration and non defects are detected and classified in X- ray images by employing support vector machine and artificial neural network and confirm their high-performance accuracy. The proposed methods incorporated mainly four modules. In the first module weld image preprocess by median filtering to achieve protective edge of weld image by suppressing noises and by rectangular shaped histogram for dispersal grey levels to wider range with a brightness gradient achieved. Furthermore, second module propose canny edge operator to achieved complete and more continues edge of weld defect as compared to other edge operators. In the third module, ten texture feature extracted by grey level co-occurrence matrix. Moreover, cluster of signifiers identical to texture features measurements extract separated entity and stated as an input classifier. Finally for speedy advances of weld imperfection detection mechanization, support vector machine and artificial neural network detected and classified weld imperfections and confirmed their accuracy performance of 98.75 and 97.5% by confusion matrix. The autonomous technique for detection and classification of X-ray images reveals ideal calculation time without disturbing complete accuracy of features selection and presented comprehensive innovative technique with improved results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract In modern engineering, weld joint excellence achieved through competent and on-line welding imperfections monitoring techniques. Online weld joint examination by non-destructive testing significantly demanded in vehicle, railways, aerospace, petrochemical, oil, gas, land transportation, shipbuilding, and marine industries. These industries demanded weld imperfection inspection as major part of their testing as physical inspection may confuse for appropriate justifications and lead to incorrect identifications. Hence, for error free examination automatic weld, this work proposed an autonomous technique for multi class weld imperfections namely crack, undercut, gas pores, porosity, slag, warm holes, lack of penetration and non defects are detected and classified in X- ray images by employing support vector machine and artificial neural network and confirm their high-performance accuracy. The proposed methods incorporated mainly four modules. In the first module weld image preprocess by median filtering to achieve protective edge of weld image by suppressing noises and by rectangular shaped histogram for dispersal grey levels to wider range with a brightness gradient achieved. Furthermore, second module propose canny edge operator to achieved complete and more continues edge of weld defect as compared to other edge operators. In the third module, ten texture feature extracted by grey level co-occurrence matrix. Moreover, cluster of signifiers identical to texture features measurements extract separated entity and stated as an input classifier. Finally for speedy advances of weld imperfection detection mechanization, support vector machine and artificial neural network detected and classified weld imperfections and confirmed their accuracy performance of 98.75 and 97.5% by confusion matrix. The autonomous technique for detection and classification of X-ray images reveals ideal calculation time without disturbing complete accuracy of features selection and presented comprehensive innovative technique with improved results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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An Autonomous Technique for Multi Class Weld Imperfections Detection and Classification by Support Vector Machine |
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