Condition Estimation Of Carbon Steel Using A Neuro-Fuzzy System And Image Processing

This paper describes the development of an intelligent integrated system comprised of a fuzzy logic architecture developed from descriptive statistics and an artificial neural network multilayer perceptron applied in pattern recognition with digital image processing. 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.