For a warm rolled Mg-Zn-MM (Misch Metal) alloy sheet, the static recrystallization process is adapted to develop a weak-textured microstructure with an average grain size of 4 mu m. Due to the relatively low recrystallization temperature of the alloy, the sheet can be fabricated by the warm rolling process and then recrystallized at 220 degrees C. Enhanced ductility is observed from room temperature to 350 degrees C. In addition to the basal slips within the grains, at room temperature nano-twins actively form near the grain boundaries, accommodating the plastic strain incompatibility near the grain boundaries. Moreover, above 150 degrees C, dynamic recrystallized grains are actively developed during deformation. In the temperature range of 100-350 degrees C, the values of elongation to failure are approximately 40% higher than those of the AZ31 alloy sheet without the loss of tensile strength. (C) 2013 Elsevier B.V. All rights reserved.

Image segmentation techniques have been widely used in medical image analysis. However, existing methods can not provide exact physical significance of segmented image regions because they are mainly based on basic image features such as grey level and texture without taking into account specialised medical knowledge. However, medical knowledge plays an indispensable role when doctors analyse medical images in their diagnosis. To deal with this problem, many tissue classification systems were developed by incorporating specific medical knowledge. However, these systems strongly depend on specific applications and then can not provide a general structure for integrating medical knowledge in a larger context. To treat medical image recognition in a systematic way, we propose in this paper a general intelligent tissue classification system which combines both FCM (fuzzy C-means) based clustering algorithm and qualitative medical knowledge on geometric properties of different tissues. In this system, a general model is proposed for formalising non structured and non normalised medical knowledge from various medical images. This model utilises a DOGMA approach (a natural language-based ontology system) for formal representation of these geometric features.