RT info:eu-repo/semantics/article
T1 RBF-Neural network applied to the quality classification of tempered 100Cr6 steel cams by the multi-frequency nondestructive eddy current testing
A1 Martínez Martínez, Víctor
A1 Garcia Martin, Javier
A1 Gómez Gil, Jaime
K1 Nondestructive testing
K1 Eddy current
K1 Tempering process
K1 Radial basis function neural network
K1 Multi-frequency
K1 Analysis of variance
K1 33 Ciencias Tecnológicas
AB 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.
PB MDPI
YR 2017
FD 2017
LK https://uvadoc.uva.es/handle/10324/57236
UL https://uvadoc.uva.es/handle/10324/57236
LA eng
NO Metals, 2017, vol. 7, n. 10, p. 385
NO Producción Científica
DS UVaDOC
RD 17-jul-2024