Classification of welding defects in radiographic images using an adaptive-network-based fuzzy system

Authors:
Rafael Vilar;Juan Zapata;Ramón Ruiz
Affiliations:
Departamento de Estructuras y Construcción, Universidad Politécnica de Cartagena, Cartagena, Spain;Departamento de Electrónica, Tecnología de Computadores y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain;Departamento de Electrónica, Tecnología de Computadores y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain
Venue:
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Year:
2011

Citing 3
Cited 0

Two-dimensional signal and image processing

Two-dimensional signal and image processing
Computer and Robot Vision

Computer and Robot Vision
Classification of Welding Defects in Radiographic Images Using an ANN with Modified Performance Function

IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation

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Abstract

In this paper, we describe an automatic system of radiographic inspection of welding. An important stage in the construction of this system is the classification of defects. In this stage, an adaptive network-based fuzzy inference system (ANFIS) for weld defect classification was used. The results was compared with the aim to know the features that allow the best classification. The correlation coefficients were determined obtaining a minimum value of 0.84. The accuracy or the proportion of the total number of predictions that were correct was determined obtaining a value of 82.6%.