Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Production of a low young modulus titanium alloy by powder metallurgy

Titanium alloys have several advantages over ferrous and non-ferrous metallic materials, such as high strengthto-weight ratio and excellent corrosion resistance. A blended elemental titanium powder metallurgy process has been developed to offer low cost commercial products. The process employs hydride-dehydride (HDH) powders as raw material. In this work, results of the Ti-35Nb alloy sintering are presented. This alloy due to its lower modulus of elasticity and high biocompatibility is a promising candidate for aerospace and medical use. Samples were produced by mixing of initial metallic powders followed by uniaxial and cold isostatic pressing with subsequent densification by isochronal sintering between 900 up to 1600 °C, in vacuum. Sintering behavior was studied by means of microscopy and density. Sintered samples were characterized for phase composition, microstructure and microhardness by X-ray diffraction, scanning electron microscopy and Vickers indentation, respectively. Samples sintered at high temperatures display a fine plate-like alpha structure and intergranular beta. A few remaining pores are still found and density above 90% for specimens sintered in temperatures over 1500 °C is reached.