MFL signals and artificial neural networks applied to detection and classification of pipe weld defects

NDT & E International - Tập 39 - Trang 661-667 - 2006
A.A. Carvalho1, J.M.A. Rebello1, L.V.S. Sagrilo2, C.S. Camerini3, I.V.J. Miranda4
1Department of Metallurgical and Materials Engineering, COPPE/UFRJ-Federal University of Rio de Janeiro, P.O. Box 68505, CEP 21941-972, Rio de Janeiro RJ, Brazil
2Department of Civil Engineering, COPPE/UFRJ-Federal University of Rio de Janeiro, P.O. Box 68505, CEP 21941-972, Rio de Janeiro RJ, Brazil
3PETROBRAS Research and Development Center (CENPES), Cidade Universitária, Q. 7, Ilha do Fundão, CEP 21949-900, Rio de Janeiro RJ, Brazil
4Pipe Way Engineering, Praça Mario Nazaré, 40 São Cristovão, CEP 20940-080, Rio de Janeiro RJ, Brazil

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