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NEURO-FUZZY SYSTEM APPLIED TO RECOGNIZE AND ASSESSING THE DAMAGE IN CARBON STEEL SUPPORTED BY DESCRIPTIVE STATISTICS

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MAY 2016   -  Volume: 91 -  Pages: 301-308

DOI:

https://doi.org/10.6036/7777

Authors:

EDGAR AUGUSTO RUELAS SANTOYO - JOSE ANTONIO VAZQUEZ LOPEZ -
JAVIER YÁÑEZ MENDIOLA
- ISMAEL LOPEZ JUAREZ - CARLOS FERNANDO BRAVO BARRERA

Disciplines:

  • INFORMATION TECHNOLOGY AND KNOWLEDGE (INTELIGENCIA ARTIFICIAL Y SIMULACION )

Downloads:   288

Cites in Web of Science:  1

How to cite this paper:  
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Received Date :   4 August 2015

Reviewing Date :   14 December 2015

Accepted Date :   19 December 2015


Key words:
Redes Neuronales, procesamiento de imágenes, lógica difusa y patrones metalográficos, Artificial neural network, image processing, fuzzy logic and metallography.
Article type:
ARTICULO DE INVESTIGACION / RESEARCH ARTICLE
Section:
RESEARCH ARTICLES

This paper describes the development of an intelligent integrated system able to estimate the damage to carbon steel, the system, comprises an fuzzy logic architecture developed from descriptive statistics and an artificial neural network multilayer perceptron applied in metallographic pattern recognition with digital image processing is also carried just characterizing textures metallographic images using statistical first, second and third order. The studied patterns are from the microstructure of carbon steel SA 210 Grade A-1. The purpose is to estimate the damage present in the material from the determination of the physical state of the material. Steel samples were tested in actual conditions, such as the steam and water at high temperature suffering deterioration not easily detectable by standard metallographic means. Studied patterns in the microstructure of the material were: pearlite lamellar, spheronization and graphitization. The microstructure was revealed from images obtained by an inverted metallographic microscope (Olympus - GX71) in the Testing Laboratory Equipment and Materials of the Federal Electricity Commission in Mexico. (LAPEM - CFE). The results showed that the damage estimation and pattern recognition in the material were correctly predicted with the developed system compared to the human expert. Furthermore, the analysis can be performed in less time and cost.

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