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Metals 2017, 7(10), 385; doi:10.3390/met7100385

RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing

1
Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain
2
Ingeniería y Sistemas de Ensayos no Destructivos (ISEND), Boecillo Technological Park, Luis Proust 10, 47151 Valladolid, Spain
*
Author to whom correspondence should be addressed.
Received: 18 August 2017 / Revised: 13 September 2017 / Accepted: 15 September 2017 / Published: 21 September 2017
(This article belongs to the Special Issue Advanced Non-Destructive Testing in Steels)
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Abstract

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. View Full-Text
Keywords: nondestructive testing; eddy current; tempering process; radial basis function neural network; multi-frequency; analysis of variance nondestructive testing; eddy current; tempering process; radial basis function neural network; multi-frequency; analysis of variance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Martínez-Martínez, V.; Garcia-Martin, J.; Gomez-Gil, J. RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing. Metals 2017, 7, 385.

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