Issue 16

F. R. Renzetti et alii, Frattura ed Integrità Strutturale, 16 (2011) 43-51; DOI : 10.3221/IGF-ESIS.16.05 43 Use of a gray level co-occurrence matrix to characterize duplex stainless steel phases microstructure F. R. Renzetti, L. Zortea Università di Roma “La Sapienza”, D.I.C.M.A. via Eudossiana, 18 – Roma A BSTRACT . Duplex stainless steels are widely used in industry. This is due to their higher strength compared to austenitic steels and to their higher toughness than ferritic steels. They also have good weldability and high resistance to stress corrosion cracking. These steels are characterized by two-phase microstructures composed by almost the same level of ferrite and austenite. Duplex steel 2205 samples evaluated are: as received, cold rolled (33%) and heat-treated at 800°C for 10 hours. A metallographic etching with 10% oxalic acid has been carried out to highlight the phases morphology. Some photos have been taken by SEM microscope and submitted to image analysis. The analysis carried out is based on the determination of co-occurrence matrix and on the following interpretation of appropriate indicators. Through these indicators is possible to estimate the features of images objectively. K EYWORDS . Gray level co-occurrence matrix (GLCM); Image analysis; Duplex stainless steel; Microstructure characterization. I NTRODUCTION uplex stainless steels are resistant to intergranular corrosion [1]. They are characterized by almost the same amount of two phases, ferrite and austenite. In the pseudo binary phase diagram of DSS, we can see after a first phase of fully ferritic solidification, there is a partial conversion of the microstructure in austenitic phase [2]. In this paper a non invasive methodology is used to highlight the DSS microstructure, an algorithm based on a statistical approach, which allowed an objective and repeatable study of some images obtained by electron microscope. The statistical approach for image analysis based on the matrix of co-occurrence (GLCM Gray Level Co-occurrence Matrix) is widespread in many fields, alone or synergistically with other analysis, to evaluate the images morphology. This one, better known as “texture” (an innate property of all the virtual surfaces), gives information on the disposition of the structures and their relations with the environment. Relevant indicators will be derived from the GLCM to describe the “texture” of the image. Through this method it’s possible to see tool marks, the presence of surface defects and also the grain boundaries of each phase. D