Quantitative metallography is a technique to determine and correlate the microstructures of materials with their properties and behavior. Generic commercial image processing and analysis software packages have been used to quantify material phases from metallographic images. However, these all-purpose solutions also have some drawbacks, particularly when applied to segmentation of material phases. To overcome such limitations, this work presents a new solution to automatically segment and quantify material phases from SEM metallographic images. The solution is based on a neuronal network and in this work was used to identify the secondary phase precipitated in the gamma matrix of a Nickel base alloy. The results obtained by the new solution were validated by visual inspection and compared with the ones obtained by a commonly used commercial software. The conclusion is that the new solution is precise, reliable and more accurate and faster than the commercial software.
Keywords: materials sciences; metallography; microstructural analysis; nickel alloy; secondary phase; precipitation; phase quantification; dissimilar metal weld; image segmentation; artificial neuronal network