Abstract
Duplex stainless steels when subjected to temperatures above 600 ° C have its tenacity decreased by the formation of sigma phase. This phase has high hardness and is rich in chromium and reduces the matrix of this element. In this study, field line density measurements, obtained in the reversibility region of magnetic domains, and application of artificial neural networks are used to monitor the formation of this undesirable phase. Samples of a stainless steel SAF 2205 were subjected to aging at temperatures of 800 ° C and 900 ° C, in order to obtain different amounts of sigma phase. The amount of this phase was obtained by image processing and the density of field lines through a Hall Effect sensor. Charpy impact tests were performed. The field lines densities were used for training of an artificial neural network and correlated with the presence of sigma phase and embrittlement of the material. The results showed that the method was able to correlate the parameters studied with the presence of the sigma phase and toughness of the material studied in both temperatures.
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