Edgard de Macedo Silva

Sistema de segmentação de imagens para quantificação de microestruturas em metais utilizando redes neurais artificiais

Digital Image Processing is an increasing expansion area in some field of application that uses the interpretation of images as tool. Quantitative Metallography area applied to Materials Sciences uses this technique for characterization of phase volumetric fractions, grain size, inclusion distribution determination and other parameters that influence the properties of the materials. The present paper has, as main objective, to present and validate the software Segmentation by Artificial Neural Network (SVRNA), developed by the authors. This software, based on artificial neural network, makes the percentile constituent counting in time reduced in relation to the conventional model. The study is carried out over ABNT 1020 and 1045 steel and nodular cast iron samples. Statistical analysis showed that this software is efficient for admitted degree of significance. It has concluded, therefore, that the program can be used in applications in the field of Material Sciences for determination of microstructures.

Evaluation of the magnetic permeability for the microstructural characterization of a duplex stainless steel

Non-destructive testing has been commonly used to assess the presence of discontinuities that may affect the integrity of materials in service. In this study, a Hall-effect sensor is used in a methodology developed to study, in a non-destructive manner, the microstructural variations of a material that occur because of the single-phase decomposition. The material selected was the UNS S31803 duplex stainless steel, particularly because of its behavior at temperatures below 525°C. Measurements of magnetic permeability based on Hall voltage values were performed, as well as hardness measurements and x-ray diffraction studies. The results confirm that the magnetic permeability can be used to successfully track the formation of α′ phase from α phase in a duplex stainless steel.