TY - JOUR 
A1 - Zapata Pérez, Juan Francisco
AU - Ruiz Merino, Ramón Jesús
AU - Vilar Hernández, Rafael Eduardo
T1 - An automatic welding defects classifier system
Y1 - 2009
SN - 1698-2924
UR - http://hdl.handle.net/10317/874
AB - Radiographic 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 cases.
KW - Inspección radiográfica
KW - Detección de defectos de soldadura
KW - Técnicas de proceso de datos
KW - Modelo neuronal
KW - Arquitectura de redes
LA - eng
ER -