Show simple item record

dc.contributor.authorZapata Pérez, Juan Francisco 
dc.contributor.authorRuiz Merino, Ramón Jesús 
dc.contributor.authorVilar Hernández, Rafael Eduardo
dc.description.abstractRadiographic inspection is a well-established testing method to detect weld defects. However, interpretation of radiographic films is a difficult task. The reliability of such interpretation and the expense of training suitable experts have allowed that the efforts being made towards automation in this field. In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under three regularisation process with different architectures. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used in the aim to give better performance for defect classification in both
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.titleAn automatic welding defects classifier systemes
dc.subjectInspección radiográficaes
dc.subjectDetección de defectos de soldaduraes
dc.subjectTécnicas de proceso de datoses
dc.subjectModelo neuronales
dc.subjectArquitectura de redeses
dc.contributor.investgroupGrupo Diseño Electronico y Técnicas de Tratamiento de Señales

Files in this item


This item appears in the following Collection(s)

Show simple item record

Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España