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Master's Dissertation
DOI
Document
Author
Full name
Ana Isabel Castillo Pereda
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2010
Supervisor
Committee
Padovese, Linilson Rodrigues (President)
Campos, Marcos Flávio de
Ting, Daniel Kao Sun
Title in Portuguese
Automação de diagnóstico para ensaios nao destrutivos magnéticos.
Keywords in Portuguese
Deformação plástica
Ensaio não destrutivo
Rede neural probabilística
Ruído Magnético de Barkhausen
Abstract in Portuguese
Este trabalho apresenta um método para o reconhecimento e a detecção automática dos diferentes valores ou graus de deformação plástica em Ensaios Não Destrutivos empregando o Ruído Magnético de Barkhausen. O método é baseado no uso de uma Rede Neural Probabilística que permite o diagnóstico automático dos diferentes valores de deformação plástica, conteúdo de carbono, estas medidas são procedentes das medições das amostras de placas de aço AISI 1006, 1050 e 1070, esta base de dados foi feita pelo grupo de pesquisadores do Laboratório de Dinâmica e Instrumentação LADIN da Escola Politécnica da USP, departamento da Mecânica. Os excelentes resultados da rede neural probabilística de detectar automaticamente os valores de deformação mostram a efetividade do desempenho da rede neural probabilística que tem um desempenho superior aos métodos não destrutivos tradicionais e que realmente esta nova tecnologia é uma excelente solução para o diagnóstico.
Title in English
Automation of diagnostic for non-destrutive magnetic tests.
Keywords in English
Magnetic Barkhausen Noise
Non-destructive testing
Plastic deformation
Probabilistic neural network
Abstract in English
This work presents a method for automatic detection and recognition of different levels or degrees of plastic deformation in Non-Destructive Testing using the Magnetic Barkhausen Noise. The method is based on using a Probabilistic Neural Network that allows the automatic diagnosis of the different values of plastic deformation and carbon content. The measurements corresponds to samples of steel plates AISI 1006, 1050 and 1070, this database was made by the group of researchers from the Laboratory of Dynamics and Instrumentation LADIN the Polytechnic School of USP, Department of Mechanical Engineering. The results show the effectiveness of the probabilistic neural network to automatically detect plastic deformation levels as well as carbon content level. This method has a superior performance in comparison to traditional nondestructive methods.
 
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Publishing Date
2010-12-23
 
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