RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing
This article proposes a Radial Basis Function Artificial Neural Network (RBF-ANN) to classify tempered steel cams as correctly or incorrectly treated pieces by using multi-frequency nondestructive eddy current testing. Impedances at five frequencies between 10 kHz and 300 kHz were employed to perfor...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-09-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/7/10/385 |
| _version_ | 1819036799874367488 |
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| author | Víctor Martínez-Martínez Javier Garcia-Martin Jaime Gomez-Gil |
| author_facet | Víctor Martínez-Martínez Javier Garcia-Martin Jaime Gomez-Gil |
| author_sort | Víctor Martínez-Martínez |
| collection | DOAJ |
| description | This article proposes a Radial Basis Function Artificial Neural Network (RBF-ANN) to classify tempered steel cams as correctly or incorrectly treated pieces by using multi-frequency nondestructive eddy current testing. Impedances at five frequencies between 10 kHz and 300 kHz were employed to perform the binary sorting. The ANalysis Of VAriance (ANOVA) test was employed to check the significance of the differences between the impedance samples for the two classification groups. Afterwards, eleven classifiers were implemented and compared with one RBF-ANN classifier: ten linear discriminant analysis classifiers and one Euclidean distance classifier. When employing the proposed RBF-ANN, the best performance was achieved with a precision of 95% and an area under the Receiver Operating Characteristic (ROC) curve of 0.98. The obtained results suggest RBF-ANN classifiers processing multi-frequency impedance data could be employed to classify tempered steel DIN 100Cr6 cams with a better performance than other classical classifiers. |
| first_indexed | 2024-12-21T08:11:16Z |
| format | Article |
| id | doaj.art-664e7adf07d747c29fea167dd0a2f800 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-12-21T08:11:16Z |
| publishDate | 2017-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-664e7adf07d747c29fea167dd0a2f8002022-12-21T19:10:39ZengMDPI AGMetals2075-47012017-09-0171038510.3390/met7100385met7100385RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current TestingVíctor Martínez-Martínez0Javier Garcia-Martin1Jaime Gomez-Gil2Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, SpainDepartment of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, SpainDepartment of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, SpainThis article proposes a Radial Basis Function Artificial Neural Network (RBF-ANN) to classify tempered steel cams as correctly or incorrectly treated pieces by using multi-frequency nondestructive eddy current testing. Impedances at five frequencies between 10 kHz and 300 kHz were employed to perform the binary sorting. The ANalysis Of VAriance (ANOVA) test was employed to check the significance of the differences between the impedance samples for the two classification groups. Afterwards, eleven classifiers were implemented and compared with one RBF-ANN classifier: ten linear discriminant analysis classifiers and one Euclidean distance classifier. When employing the proposed RBF-ANN, the best performance was achieved with a precision of 95% and an area under the Receiver Operating Characteristic (ROC) curve of 0.98. The obtained results suggest RBF-ANN classifiers processing multi-frequency impedance data could be employed to classify tempered steel DIN 100Cr6 cams with a better performance than other classical classifiers.https://www.mdpi.com/2075-4701/7/10/385nondestructive testingeddy currenttempering processradial basis function neural networkmulti-frequencyanalysis of variance |
| spellingShingle | Víctor Martínez-Martínez Javier Garcia-Martin Jaime Gomez-Gil RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing Metals nondestructive testing eddy current tempering process radial basis function neural network multi-frequency analysis of variance |
| title | RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing |
| title_full | RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing |
| title_fullStr | RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing |
| title_full_unstemmed | RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing |
| title_short | RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing |
| title_sort | rbf neural network applied to the quality classification of tempered 100cr6 steel cams by the multi frequency nondestructive eddy current testing |
| topic | nondestructive testing eddy current tempering process radial basis function neural network multi-frequency analysis of variance |
| url | https://www.mdpi.com/2075-4701/7/10/385 |
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